PLearn Namespace Reference

< for swap More...

Classes
class	PLearn::Array< T >
class	PLearn::TypeTraits< Array< T > >
class	PLearn::Array2ArrayMap< T >
class	PLearn::Option< T, Enclosing >
	Template class for option definitions. More...
class	PLearn::ArrayAllocatorOptions
class	PLearn::ArrayAllocator< T, SizeBits >
class	PLearn::ArrayAllocatorIndex< IndexBase, SizeBits >
	This type represents an index into the allocated memory, as a bit-field parameterized by the template argument SizeBits. More...
class	PLearn::ArrayAllocatorTrivial< T, SizeBits >
	This allocator solely performs allocation. More...
class	PLearn::PLearnInit
class	PLearn::Object
	The Object class. More...
class	PLearn::OptionBase
	Base class for option definitions. More...
class	PLearn::PDate
class	PLearn::PDateTime
class	PLearn::PLearnError
class	PLearn::PPointable
class	PLearn::PP< T >
class	PLearn::TypeTraits< PP< T > >
class	PLearn::MultiMap< A, B >
class	PLearn::ProgressBarPlugin
	Base class for pb plugins. More...
class	PLearn::TextProgressBarPlugin
	Simple plugin for displaying text progress bar. More...
struct	PLearn::NullProgressBarPlugin
	Simpler plugin that doesn't display a progress bar at all. More...
class	PLearn::ProgressBar
	This class will help you display progress of a calculation. More...
class	PLearn::Range
class	PLearn::RealRange
	represents a real range: i.e. one of ]low,high[ ; [low,high[; [low,high]; ]low,high] More...
class	PLearn::RealMapping
class	PLearn::TypeTraits< RealMapping >
class	PLearn::SetOption
class	PLearn::TypeTraits< SetOption >
class	PLearn::SmallVector< T, SizeBits, Allocator >
class	PLearn::StaticInitializer
	A StaticInitializer is typically declared as a static member of a class, and given a parameter that is a static initialization function for said class. More...
class	PLearn::Storage< T >
class	PLearn::StringTable
class	PLearn::TinyVector< T, N, TTrait >
class	PLearn::TinyVectorTrait< T >
class	PLearn::TinyVectorTrait< unsigned char >
class	PLearn::TinyVectorTrait< char >
class	PLearn::TinyVectorTrait< unsigned int >
class	PLearn::TinyVectorTrait< int >
class	PLearn::TypeMapEntry
class	PLearn::TypeFactory
class	PLearn::TypeTraits< T >
class	PLearn::TypeTraits< T * >
class	PLearn::TypeTraits< string >
class	PLearn::TypeTraits< vector< T > >
class	PLearn::TypeTraits< list< T > >
class	PLearn::TypeTraits< pair< T, U > >
class	PLearn::TypeTraits< map< T, U > >
class	PLearn::StringFieldMapping
class	PLearn::NumToStringMapping
class	PLearn::AutoSDBVMatrix
	A VMatrix view of a SimpleDB: columns whose type is string are removed from the view, all others are converted to real (characters to their ascii code, and dates to the float date format: 990324). More...
class	PLearn::NistDB
class	PLearn::SDBVMOutputCoder
class	PLearn::SDBVMField
class	PLearn::SDBVMSource
	A SDBVMSource represents a source for a value that can be either directly a field from a SDB or an already processed SDBVMField. More...
class	PLearn::SDBVMatrix
class	PLearn::SDBVMFieldSource1
	A field that maps exactly 1 SDB field to a VMatrix segment (abstract). More...
class	PLearn::SDBVMFieldSource2
	A field that maps exactly 2 SDB fields to a VMatrix segment (abstract). More...
class	PLearn::SDBVMFieldAsIs
	Pass through the value within the SDB (after conversion to real of the underlying SDB type). More...
class	PLearn::SDBVMFieldNormalize
	Normalize the field (subtract the mean then divide by standard dev). More...
class	PLearn::SDBVMFieldDivSigma
	Just divide by standard deviation. More...
class	PLearn::SDBVMFieldAffine
	Apply an affine transformation to the field: y = a*x+b. More...
class	PLearn::SDBVMFieldPosAffine
	Take the positive part of the field, followed by affine transformation: y = a*max(x,0)+b. More...
class	PLearn::SDBVMFieldSignedPower
	Do the following : y = x^a. More...
class	PLearn::SDBVMFieldFunc1
class	PLearn::SDBVMFieldFunc2
class	PLearn::SDBVMFieldDate
	Convert a date to fill 3 columns in the VMat: YYYY, MM, DD. More...
class	PLearn::SDBVMFieldDay
class	PLearn::SDBVMFieldMonths
	Computed year*12+(month-1). More...
class	PLearn::SDBVMFieldDateDiff
	difference between two dates ("source1-source2" expressed as an integer number of days, months, or years) More...
class	PLearn::SDBVMFieldDiscrete
	A field that recodes its source field according to an OutputCoder object. More...
class	PLearn::SDBVMFieldDateGreater
	verifies if the date within the row is greater than a threshold date More...
class	PLearn::SDBVMFieldCodeAsIs
class	PLearn::SDBVMFieldRemapReals
class	PLearn::SDBVMFieldRemapStrings
class	PLearn::SDBVMFieldRemapIntervals
class	PLearn::SDBVMFieldMultiDiscrete
class	PLearn::SDBVMFieldICBCTargets
class	PLearn::SDBVMFieldHasClaim
class	PLearn::SDBVMFieldSumClaims
class	PLearn::SDBVMFieldICBCClassification
class	PLearn::FieldStat
class	PLearn::SDBWithStats
struct	PLearn::Field
class	PLearn::FieldPtr
class	PLearn::FieldValue
struct	PLearn::FieldValue::DateVal_t
class	PLearn::Schema
class	PLearn::SimpleDBIndexKey< KeyType >
class	PLearn::SimpleDB< KeyType, QueryResult >
class	PLearn::RowIterator
class	PLearn::FieldRowRef
class	PLearn::Row
class	PLearn::UCISpecification
class	PLearn::GhostScript
class	PLearn::Gnuplot
class	PLearn::MatlabInterface
class	PLearn::RGB
class	PLearn::HSV
class	PLearn::RGBImage
	uses top left coordinate system Pixel (i,j) is at row i, column j More...
class	PLearn::RGBImageDB
class	PLearn::RGBImageVMatrix
class	PLearn::RGBImagesVMatrix
class	PLearn::FilePStreamBuf
class	PLearn::IntStream
class	PLearn::IntStreamVMatrix
class	PLearn::FilesIntStream
class	PLearn::InMemoryIntStream
class	PLearn::IntVecFile
class	PLearn::MRUFileList
class	PLearn::pl_fdstreambuf
	pl_fdstreambuf: stream buffer that acts on a POSIX file descriptor More...
class	PLearn::pl_fdstream
class	PLearn::pl_nullstreambuf
class	PLearn::pl_streambuf
class	PLearn::pl_streammarker
class	PLearn::PStream
class	PLearn::PIFStream
class	PLearn::POFStream
class	PLearn::PIStringStream
class	PLearn::pl_stream_raw
class	PLearn::pl_stream_clear_flags
class	PLearn::pl_stream_initiate
class	PLearn::PStreamBuf
class	PLearn::StdPStreamBuf
class	PLearn::TmpFilenames
struct	PLearn::tRule
class	PLearn::AdditiveNormalizationKernel
class	PLearn::ClassDistanceProportionCostFunction
class	PLearn::ClassErrorCostFunction
class	PLearn::ClassMarginCostFunction
class	PLearn::CompactVMatrixGaussianKernel
class	PLearn::CompactVMatrixPolynomialKernel
class	PLearn::ConvexBasisKernel
	returns prod_i log(1+exp(c*(x1[i]-x2[i]))) NOTE: IT IS NOT SYMMETRIC! More...
class	PLearn::DifferenceKernel
	returns sum_i[x1_i-x2_i] More...
class	PLearn::DirectNegativeCostFunction
class	PLearn::DistanceKernel
	This class implements an Ln distance (defaults to L2 i.e. euclidean distance). More...
class	PLearn::DivisiveNormalizationKernel
class	PLearn::DotProductKernel
	returns <x1,x2> More...
class	PLearn::GaussianDensityKernel
class	PLearn::GaussianKernel
	returns exp(-norm_2(x1-x2)^2/sigma^2) More...
class	PLearn::GeneralizedDistanceRBFKernel
	returns exp(-phi*(sum_i[abs(x1_i^a - x2_i^a)^b])^c) More...
class	PLearn::GeodesicDistanceKernel
class	PLearn::Kernel
class	PLearn::Ker
class	PLearn::KernelVMatrix
class	PLearn::LaplacianKernel
	returns exp(-phi*(sum_i[abs(x1_i - x2_i)])) More...
class	PLearn::LiftBinaryCostFunction
class	PLearn::LLEKernel
class	PLearn::LogOfGaussianDensityKernel
class	PLearn::MulticlassErrorCostFunction
class	PLearn::NegKernel
class	PLearn::NegLogProbCostFunction
class	PLearn::NegOutputCostFunction
	This simply returns -output[0] (target should usually have a length of 0) This is used for density estimators whose use(x) method typically computes log(p(x)). More...
class	PLearn::NormalizedDotProductKernel
class	PLearn::PolynomialKernel
	returns (beta*dot(x1,x2)+1)^n More...
class	PLearn::PowDistanceKernel
class	PLearn::PrecomputedKernel
	A kernel that precomputes the kernel matrix as soon as setDataForKernelMatrix is called. More...
class	PLearn::PricingTransactionPairProfitFunction
class	PLearn::QuadraticUtilityCostFunction
class	PLearn::ReconstructionWeightsKernel
class	PLearn::ScaledGaussianKernel
	returns exp(-sum_i[(phi_i*(x1_i - x2_i))^2]/sigma^2) More...
class	PLearn::ScaledGeneralizedDistanceRBFKernel
	returns exp(-(sum_i phi_i*[abs(x1_i^a - x2_i^a)^b])^c) More...
class	PLearn::ScaledLaplacianKernel
	returns exp(-(sum_i[abs(x1_i - x2_i)*phi_i])) More...
class	PLearn::SelectedOutputCostFunction
	This allows to apply a costfunction on a single output element (and correponding target element) of a larger output vector, rather than on the whole vector. More...
class	PLearn::SigmoidalKernel
	returns sigmoid(c*x1.x2) More...
class	PLearn::SigmoidPrimitiveKernel
	returns log(1+exp(c*x1.x2)) = primitive of sigmoidal kernel More...
class	PLearn::SourceKernel
class	PLearn::SquaredErrorCostFunction
	********************************************************* The following 'kernels' are rather used as cost functions More...
class	PLearn::WeightedCostFunction
	A costfunction that allows to reweight another costfunction (weight being last element of target) Returns target.lastElement() * costfunc(output,target.subVec(0,target.length()-1));. More...
class	PLearn::Binner
class	PLearn::BottomNI< T >
class	PLearn::ConditionalCDFSmoother
class	PLearn::ConditionalStatsCollector
class	PLearn::DoubleAccessSparseMatrix< T >
class	PLearn::SMat< T >
class	PLearn::HashKeyDataPair< KeyType, DataType >
class	PLearn::Hash< KeyType, DataType >
struct	PLearn::Symbol
class	PLearn::IntPair
	Example of class that can be used as key. More...
class	PLearn::LiftStatsCollector
class	PLearn::LimitedGaussianSmoother
class	PLearn::ManualBinner
union	PLearn::_plearn_nan_type
class	PLearn::PLMathInitializer
class	PLearn::ProbSparseMatrix
class	PLearn::PSMat
class	PLearn::RowMapSparseMatrix< T >
class	PLearn::RowMapSparseValueMatrix< T >
class	PLearn::ScaledConditionalCDFSmoother
class	PLearn::Smoother
class	PLearn::SparseMatrix
class	PLearn::StatsCollectorCounts
class	PLearn::StatsCollector
class	PLearn::StatsIterator
class	PLearn::MeanStatsIterator
class	PLearn::ExpMeanStatsIterator
class	PLearn::StddevStatsIterator
class	PLearn::StderrStatsIterator
class	PLearn::SharpeRatioStatsIterator
class	PLearn::MinStatsIterator
class	PLearn::MaxStatsIterator
class	PLearn::LiftStatsIterator
class	PLearn::QuantilesStatsIterator
class	PLearn::StatsItArray
class	PLearn::TTensorElementIterator< T >
class	PLearn::TTensorSubTensorIterator< T >
class	PLearn::TTensor< T >
class	PLearn::TMat< T >
class	PLearn::TypeTraits< TMat< T > >
class	PLearn::SelectedIndicesCmp< T >
class	PLearn::TMatColRowsIterator< T >
	Model of the Random Access Iterator concept for iterating through a single column of a TMat, one row at a time. More...
class	PLearn::TMatElementIterator< T >
class	PLearn::TMatRowsAsArraysIterator< T >
	Model of the Random Access Iterator concept for iterating through the ROWS of a TMat. More...
class	PLearn::TMatRowsIterator< T >
	Model of the Random Access Iterator concept for iterating through the ROWS of a TMat. More...
class	PLearn::TopNI< T >
class	PLearn::TVec< T >
class	PLearn::TypeTraits< TVec< T > >
class	PLearn::VecCompressor
class	PLearn::VecStatsCollector
class	PLearn::Measurer
class	PLearn::CallbackMeasurer
class	PLearn::NearestNeighborPredictionCost
class	PLearn::ObjectGenerator
class	PLearn::RunObject
class	PLearn::ShellScript
class	PLearn::AdaptGradientOptimizer
class	PLearn::ConjGradientOptimizer
class	PLearn::GradientOptimizer
class	PLearn::ScaledGradientOptimizer
class	PLearn::HyperOptimizer
class	PLearn::HSetVal
class	PLearn::HTryAll
class	PLearn::HCoordinateDescent
class	PLearn::HTryCombinations
class	PLearn::Optimizer
class	PLearn::RandomVar
	we follow the same pattern as Var & Variable More...
class	PLearn::RVArray
	An RVArray stores a table of RandomVar's. More...
class	PLearn::RVInstance
	RVInstance represents a RandomVariable V along with a "value" v. More...
class	PLearn::RVInstanceArray
class	PLearn::ConditionalExpression
class	PLearn::RandomVariable
class	PLearn::StochasticRandomVariable
class	PLearn::FunctionalRandomVariable
class	PLearn::NonRandomVariable
class	PLearn::JointRandomVariable
class	PLearn::RandomElementOfRandomVariable
	RandomVariable that is the element of the first parent RandomVariable indexed by the second parent RandomVariable. More...
class	PLearn::RVArrayRandomElementRandomVariable
class	PLearn::NegRandomVariable
class	PLearn::ExpRandomVariable
class	PLearn::LogRandomVariable
class	PLearn::DiagonalNormalRandomVariable
class	PLearn::MixtureRandomVariable
class	PLearn::PlusRandomVariable
class	PLearn::MinusRandomVariable
class	PLearn::ElementWiseDivisionRandomVariable
class	PLearn::ProductRandomVariable
class	PLearn::SubVecRandomVariable
	Y = sub-vector of X starting at position "start", of length "value->length()". More...
class	PLearn::MultinomialRandomVariable
class	PLearn::ExtendedRandomVariable
class	PLearn::ConcatColumnsRandomVariable
	concatenate the columns of the matrix arguments, just like the hconcat function (PLearn.h) on Vars. More...
class	PLearn::RandomVarVMatrix
	This is a convenient wrapping around the required data structures for efficient repeated sampling from a RandomVar. More...
class	PLearn::SourceSampleVariable
class	PLearn::UnarySampleVariable
class	PLearn::BinarySampleVariable
class	PLearn::UniformSampleVariable
class	PLearn::MultinomialSampleVariable
class	PLearn::DiagonalNormalSampleVariable
class	PLearn::IPServer
class	PLearn::IPopen
class	PLearn::PLMPI
	** PLMPI is just a "namespace holder" (because we're not actually using namespaces) for a few MPI related variables. All members are static ** More...
class	PLearn::Popen
class	PLearn::Profiler
class	PLearn::Profiler::Stats
union	PLearn::semun
class	PLearn::SemId
	This class is defined in order to distinguish semaphore and shared memory id's from plain integers when constructing a Semaphore or a SharedMemory object. More...
class	PLearn::ResourceSemaphore
class	PLearn::CountEventsSemaphore
class	PLearn::SharedMemory< T >
class	PLearn::AbsVariable
class	PLearn::AffineTransformVariable
	Affine transformation of a vector variable. More...
class	PLearn::AffineTransformWeightPenalty
	Weight decay terms for affine transforms. More...
class	PLearn::ArgmaxVariable
class	PLearn::ArgminOfVariable
class	PLearn::ArgminVariable
class	PLearn::BinaryClassificationLossVariable
	For one-dimensional output: class is 0 if output < 0.5, and 1 if >= 0.5. More...
class	PLearn::BinaryVariable
class	PLearn::ClassificationLossVariable
	Indicator(classnum==argmax(netout)). More...
class	PLearn::ColumnIndexVariable
class	PLearn::ColumnSumVariable
	result is a single row that contains the sum of each column of the input More...
class	PLearn::ConcatColumnsVariable
	concatenation of the columns of several variables More...
class	PLearn::ConcatOfVariable
class	PLearn::ConcatRowsVariable
	concatenation of the rows of several variables More...
class	PLearn::ConvolveVariable
	A convolve var; equals convolve(input, mask). More...
class	PLearn::CrossEntropyVariable
	cost = - sum_i {target_i * log(output_i) + (1-target_i) * log(1-output_i)} More...
class	PLearn::CutAboveThresholdVariable
class	PLearn::CutBelowThresholdVariable
class	PLearn::DeterminantVariable
	The argument must be a square matrix Var and the result is its determinant. More...
class	PLearn::DiagonalizedFactorsProductVariable
class	PLearn::DilogarithmVariable
	This is the primitive of a sigmoid: log(1+exp(x)). More...
class	PLearn::DivVariable
	divides 2 matrix vars of same size elementwise More...
class	PLearn::DotProductVariable
	Dot product between 2 vectors (or possibly 2 matrices, which are then simply seen as vectors). More...
class	PLearn::DuplicateColumnVariable
class	PLearn::DuplicateRowVariable
class	PLearn::DuplicateScalarVariable
class	PLearn::ElementAtPositionVariable
class	PLearn::EqualConstantVariable
	A scalar var; equal 1 if input1==input2, 0 otherwise. More...
class	PLearn::EqualScalarVariable
	A scalar var; equal 1 if input1==input2, 0 otherwise. More...
class	PLearn::EqualVariable
	A scalar var; equal 1 if input1==input2, 0 otherwise. More...
class	PLearn::ErfVariable
class	PLearn::ExpVariable
class	PLearn::ExtendedVariable
class	PLearn::Func
class	PLearn::Function
class	PLearn::HardSlopeVariable
class	PLearn::IfThenElseVariable
class	PLearn::IndexAtPositionVariable
class	PLearn::InterValuesVariable
	if values = [x1,x2,...,x10], the resulting variable is [(x1+x2)/2,(x2+x3)/2, ... More...
class	PLearn::InvertElementsVariable
class	PLearn::IsAboveThresholdVariable
	Does elementwise newx_i = (x_i>=threshold ?truevalue :falsevalue);. More...
class	PLearn::IsLargerVariable
	++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ More...
class	PLearn::IsMissingVariable
	A scalar var; equal 1 if input1!=c, 0 otherwise. More...
class	PLearn::IsSmallerVariable
class	PLearn::LeftPseudoInverseVariable
class	PLearn::LiftOutputVariable
class	PLearn::LogAddVariable
	output = log(exp(input1)+exp(input2)) but it is computed in such a way as to preserve precision More...
class	PLearn::LogSoftmaxVariable
class	PLearn::LogSumVariable
class	PLearn::LogVariable
class	PLearn::MarginPerceptronCostVariable
class	PLearn::MatrixAffineTransformFeedbackVariable
	Affine transformation of a MATRIX variable. More...
class	PLearn::MatrixAffineTransformVariable
	Affine transformation of a MATRIX variable. More...
class	PLearn::MatrixElementsVariable
class	PLearn::MatrixInverseVariable
class	PLearn::MatrixOneHotSquaredLoss
class	PLearn::MatrixSoftmaxLossVariable
class	PLearn::MatrixSoftmaxVariable
class	PLearn::MatrixSumOfVariable
class	PLearn::MatRowVariable
	Variable that is the row of matrix mat indexed by variable input. More...
class	PLearn::Max2Variable
class	PLearn::MaxVariable
class	PLearn::MiniBatchClassificationLossVariable
class	PLearn::MinusColumnVariable
class	PLearn::MinusRowVariable
class	PLearn::MinusScalarVariable
class	PLearn::MinusTransposedColumnVariable
class	PLearn::MinusVariable
class	PLearn::MinVariable
class	PLearn::MulticlassLossVariable
	cost = sum_i {cost_i}, with cost_i = 1 if (target_i == 1 && output_i < 1/2) cost_i = 1 if (target_i == 0 && output_i > 1/2) cost_i = 0 otherwise More...
class	PLearn::NaryVariable
class	PLearn::NegateElementsVariable
class	PLearn::NegCrossEntropySigmoidVariable
class	PLearn::NllSemisphericalGaussianVariable
	This class implements the negative log-likelihood cost of a Markov chain that uses semispherical gaussian transition probabilities. More...
class	PLearn::OneHotSquaredLoss
	Computes sum(square_i(netout[i]-(i==classnum ?hotval :coldval)) This is used typically in a classification setting where netout is a Var of network outputs, and classnum is the target class number. More...
class	PLearn::OneHotVariable
	Represents a vector of a given lenth, that has value 1 at the index given by another variable and 0 everywhere else. More...
class	PLearn::PDistributionVariable
class	PLearn::PLogPVariable
	returns the elementwise x*log(x) in a (hopefully!) numerically stable way This can be used to compute the Entropy for instance More...
class	PLearn::PlusColumnVariable
	adds a single-column var to each column of a matrix var More...
class	PLearn::PlusConstantVariable
	adds a scalar constant to a matrix var More...
class	PLearn::PlusRowVariable
	adds a single-row var to each row of a matrix var More...
class	PLearn::PlusScalarVariable
	adds a scalar var to a matrix var More...
class	PLearn::PlusVariable
	adds 2 matrix vars of same size More...
class	PLearn::PowVariable
	elementwise pow (returns 0 wherever input is negative) More...
class	PLearn::PowVariableVariable
class	PLearn::ProductTransposeVariable
	Matrix product between matrix1 and transpose of matrix2. More...
class	PLearn::ProductVariable
	Matrix product. More...
class	PLearn::ProjectionErrorVariable
	The first input is a set of n_dim vectors (possibly seen as a single vector of their concatenation) f_i, each in R^n The second input is a set of T vectors (possibly seen as a single vector of their concatenation) t_j, each in R^n The output is the following: sum_j min_{w_j} || t_j - sum_i w_{ji} f_i ||^2 where row w_j of w is optmized analytically and separately for each j. More...
class	PLearn::ReshapeVariable
	Variable that views another variable, but with a different length() and width() (the only restriction being that length()*width() remain the same). More...
class	PLearn::RightPseudoInverseVariable
class	PLearn::RowAtPositionVariable
class	PLearn::RowSumVariable
	result is a single column that contains the sum of each row of the input More...
class	PLearn::SemiSupervisedProbClassCostVariable
class	PLearn::SigmoidVariable
class	PLearn::SignVariable
	sign(x) = 1 if x>0, -1 if x<0, 0 if x=0, all done element by element. More...
class	PLearn::SoftmaxLossVariable
class	PLearn::SoftmaxVariable
class	PLearn::SoftplusVariable
	This is the primitive of a sigmoid: log(1+exp(x)). More...
class	PLearn::SoftSlopeIntegralVariable
class	PLearn::SoftSlopeVariable
class	PLearn::SourceVariable
class	PLearn::SquareRootVariable
class	PLearn::SquareVariable
class	PLearn::SubMatTransposeVariable
class	PLearn::SubMatVariable
class	PLearn::SubsampleVariable
	A subsample var; equals subrample(input, the_subsamplefactor). More...
class	PLearn::SumAbsVariable
class	PLearn::SumOfVariable
class	PLearn::SumOverBagsVariable
class	PLearn::SumSquareVariable
class	PLearn::SumVariable
class	PLearn::TanhVariable
class	PLearn::TimesColumnVariable
	multiplies each column of a matrix var elementwise with a single column variable More...
class	PLearn::TimesConstantVariable
	multiplies a matrix var by a scalar constant More...
class	PLearn::TimesRowVariable
	multiplies each row of a matrix var elementwise with a single row variable More...
class	PLearn::TimesScalarVariable
	multiplies a matrix var by a scalar var More...
class	PLearn::TimesVariable
	multiplies 2 matrix vars of same size elementwise More...
class	PLearn::TransposeProductVariable
	Matrix product between transpose of matrix1 and matrix2. More...
class	PLearn::UnaryHardSlopeVariable
class	PLearn::UnaryVariable
class	PLearn::UnequalConstantVariable
	A scalar var; equal 1 if input1!=c, 0 otherwise. More...
class	PLearn::UnfoldedFuncVariable
class	PLearn::UnfoldedSumOfVariable
class	PLearn::VarArray
class	PLearn::VarArrayElementVariable
	Variable that is the element of the input1 VarArray indexed by the input2 variable. More...
class	PLearn::VarColumnsVariable
class	PLearn::VarElementVariable
class	PLearn::Var
class	PLearn::Variable
class	PLearn::VarMeasurer
class	PLearn::VarRowsVariable
class	PLearn::VarRowVariable
	Variable that is the row of the input1 variable indexed by the input2 variable. More...
class	PLearn::VecElementVariable
	Variable that is the element of vector vec indexed by variable input. More...
class	PLearn::WeightedSumSquareVariable
class	PLearn::AsciiVMatrix
class	PLearn::AutoVMatrix
	This class is a simple wrapper to an underlying VMatrix of another type All it does is forward the method calls. More...
class	PLearn::BatchVMatrix
	VMat class that replicates small parts of a matrix (mini-batches), so that each mini-batch appears twice (consecutively). More...
class	PLearn::BootstrapSplitter
class	PLearn::BootstrapVMatrix
class	PLearn::ByteMemoryVMatrix
class	PLearn::CenteredVMatrix
union	PLearn::short_and_twobytes
class	PLearn::CompactVMatrix
class	PLearn::CompressedVMatrix
class	PLearn::ConcatColumnsVMatrix
class	PLearn::ConcatRowsSubVMatrix
class	PLearn::ConcatRowsVMatrix
class	PLearn::CrossReferenceVMatrix
class	PLearn::CumVMatrix
class	PLearn::DatedJoinVMatrix
class	PLearn::DatedVMatrix
class	PLearn::DBSplitter
class	PLearn::DiskVMatrix
	A VMatrix whose (compressed) data resides in a directory and can span several files. More...
class	PLearn::ExplicitSplitter
class	PLearn::ExtendedVMatrix
class	PLearn::FileVMatrix
	A VMatrix that exists in a .pmat file (native plearn matrix format, same as for Mat). More...
class	PLearn::FilteredVMatrix
class	PLearn::FilterSplitter
class	PLearn::FinancePreprocVMatrix
class	PLearn::ForwardVMatrix
	This class is a simple wrapper to an underlying VMatrix of another type All it does is forward the method calls. More...
class	PLearn::FractionSplitter
class	PLearn::GeneralizedOneHotVMatrix
	This VMat is a generalization of OneHotVMatrix where all columns (given by the Vec index) are mapped, instead of just the last one. More...
class	PLearn::GetInputVMatrix
class	PLearn::GramVMatrix
class	PLearn::IndexedVMatrix
	VMat class that sees a matrix as a collection of triplets (row, column, value) Thus it is a N x 3 matrix, with N = the number of elements in the original matrix. More...
class	PLearn::InterleaveVMatrix
struct	PLearn::JoinFieldStat
class	PLearn::JoinVMatrix
class	PLearn::JulianizeVMatrix
class	PLearn::KFoldSplitter
class	PLearn::KNNVMatrix
class	PLearn::LearnerProcessedVMatrix
class	PLearn::LocalNeighborsDifferencesVMatrix
class	PLearn::MemoryVMatrix
class	PLearn::MovingAverageVMatrix
class	PLearn::MultiInstanceVMatrix
class	PLearn::OneHotVMatrix
class	PLearn::PairsVMatrix
class	PLearn::PLearnerOutputVMatrix
class	PLearn::PrecomputedVMatrix
class	PLearn::ProcessingVMatrix
class	PLearn::RangeVMatrix
	Outputs scalar samples (length 1) starting at start, up to end (inclusive) with step. When end is reached it starts over again. More...
class	PLearn::RegularGridVMatrix
class	PLearn::RemapLastColumnVMatrix
class	PLearn::RemoveDuplicateVMatrix
class	PLearn::RemoveRowsVMatrix
	sees an underlying VMat with the specified rows excluded More...
class	PLearn::RepeatSplitter
class	PLearn::RowBufferedVMatrix
class	PLearn::RowsSubVMatrix
class	PLearn::SelectColumnsVMatrix
	selects variables (columns) from a source matrix according to given vector of indices. More...
class	PLearn::SelectRowsFileIndexVMatrix
class	PLearn::SelectRowsVMatrix
	selects samples from a source matrix according to given vector of indices More...
class	PLearn::SentencesBlocks
class	PLearn::SequentialSplitter
class	PLearn::ShiftAndRescaleVMatrix
class	PLearn::SortRowsVMatrix
	Sort the samples of a VMatrix according to one (or more) given columns. More...
class	PLearn::SourceVMatrix
class	PLearn::SourceVMatrixSplitter
class	PLearn::SparseVMatrixRow
class	PLearn::SparseVMatrix
class	PLearn::Splitter
class	PLearn::StrTableVMatrix
class	PLearn::SubInputVMatrix
class	PLearn::SubVMatrix
class	PLearn::TemporalHorizonVMatrix
	This VMat delay the last targetsize entries of an underlying VMat by a certain horizon. More...
class	PLearn::TestInTrainSplitter
class	PLearn::ThresholdVMatrix
class	PLearn::ToBagSplitter
class	PLearn::TrainTestBagsSplitter
class	PLearn::TrainTestSplitter
class	PLearn::TrainValidTestSplitter
class	PLearn::TransposeVMatrix
class	PLearn::UniformizeVMatrix
class	PLearn::UniformVMatrix
class	PLearn::UpsideDownVMatrix
class	PLearn::VecExtendedVMatrix
class	PLearn::VMat
class	PLearn::VMatLanguage
class	PLearn::PreprocessingVMatrix
class	PLearn::VMatrix
class	PLearn::VMatrixFromDistribution
class	PLearn::VMField
	a VMField contains a fieldname and a fieldtype More...
class	PLearn::VMFieldStat
	this class holds simple statistics about a field More...
class	PLearn::VVec
	A VVec is a reference to a row or part of a row (a subrow) of a VMatrix. More...
class	PLearn::VVMatrix
	this class is a wrapper for a .vmat VMatrix. More...
class	PLearn::YMDDatedVMatrix
class	PLearn::AdaBoost
class	PLearn::ClassifierFromDensity
class	PLearn::MultiInstanceNNet
class	PLearn::ConditionalDensityNet
class	PLearn::ConditionalDistribution
class	PLearn::ConditionalGaussianDistribution
class	PLearn::Distribution
class	PLearn::EmpiricalDistribution
class	PLearn::GaussianDistribution
class	PLearn::GaussianProcessRegressor
class	PLearn::GaussMix
class	PLearn::HistogramDistribution
class	PLearn::LocallyWeightedDistribution
class	PLearn::ManifoldParzen2
class	PLearn::PConditionalDistribution
class	PLearn::PDistribution
class	PLearn::SpiralDistribution
class	PLearn::UnconditionalDistribution
class	PLearn::UniformDistribution
class	PLearn::AddCostToLearner
class	PLearn::EmbeddedLearner
class	PLearn::Learner
class	PLearn::NeighborhoodSmoothnessNNet
class	PLearn::NeuralNet
class	PLearn::NNet
class	PLearn::PLearner
class	PLearn::SelectInputSubsetLearner
class	PLearn::StackedLearner
class	PLearn::StatefulLearner
class	PLearn::TestingLearner
class	PLearn::GraphicalBiText
class	PLearn::ShellProgressBar
class	PLearn::Dictionary
class	PLearn::ProbVector
class	PLearn::SmoothedProbSparseMatrix
class	PLearn::ComplementedProbSparseMatrix
class	PLearn::TextSenseSequenceVMatrix
	This class handles a sequence of words/sense tag/POS triplets to present it as target words and their context. More...
class	PLearn::ProbabilitySparseMatrix
class	PLearn::PPointableSet
class	PLearn::Set
struct	PLearn::Node
class	PLearn::WordNetOntology
class	PLearn::Experiment
class	PLearn::GenerateDecisionPlot
class	PLearn::Grapher
class	PLearn::StatSpec
	The specification of a statistic to compute (as can be specified as a string in PTester). More...
class	PLearn::PTester
	This code is deprecated, use PTester.h and PTester.cc instead. More...
class	PLearn::ConstantRegressor
class	PLearn::LinearRegressor
class	PLearn::PLS
class	PLearn::EmbeddedSequentialLearner
class	PLearn::MovingAverage
	This SequentialLearner only takes the n previous target to predict the next one. More...
class	PLearn::SequentialLearner
class	PLearn::SequentialModelSelector
class	PLearn::SequentialValidation
class	PLearn::TestMethod
class	PLearn::Train
class	PLearn::EntropyContrast
class	PLearn::GaussianContinuum
class	PLearn::Isomap
class	PLearn::IsomapTangentLearner
class	PLearn::KernelPCA
class	PLearn::KernelProjection
class	PLearn::KPCATangentLearner
class	PLearn::LLE
class	PLearn::PCA
class	PLearn::SpectralClustering
class	PLearn::TangentLearner
class	PLearn::AutoRunCommand
class	PLearn::FieldConvertCommand
class	PLearn::HelpCommand
class	PLearn::JulianDateCommand
class	PLearn::KolmogorovSmirnovCommand
class	PLearn::LearnerCommand
class	PLearn::PLearnCommand
	This is the base class for all PLearn commands (those that can be issued in the plearn program). More...
class	PLearn::PLearnCommandRegistry
class	PLearn::ReadAndWriteCommand
class	PLearn::RunCommand
class	PLearn::TestDependenciesCommand
class	PLearn::TestDependencyCommand
class	PLearn::VMatCommand
Typedefs
typedef map< const void *, void * >	CopiesMap
	Global typedef to make the map of copied objects (needed by the deep copy mechanism in Object) more palatable.
typedef map< string, float >	map_string_float
	Some typedefs to use the NODEEPCOPY macro with.
typedef map< string, double >	map_string_double
typedef map< double, string >	map_double_string
typedef map< float, string >	map_float_string
typedef int(*	compare_function )(const void *, const void *)
typedef vector< PP< OptionBase > >	OptionList
typedef void(*	VOIDFUNC )()
typedef Object *(*	NEW_OBJECT )()
	Typedef for the "new instance" function type, which returns a default-initialized Object.
typedef OptionList &(*	GETOPTIONLIST_METHOD )()
typedef bool(*	ISA_METHOD )(Object *o)
typedef map< string, TypeMapEntry >	TypeMap
typedef PP< SDBVMOutputCoder >	PSDBVMOutputCoder
typedef PP< SDBVMField >	PSDBVMField
typedef PP< SDBVMFieldDiscrete >	PSDBVMFieldDiscrete
typedef Array< PSDBVMFieldDiscrete >	FieldArray
	In general, if there are N fields, x_1...x_N, and each can take y_i values, then the discrete value is:.
typedef SimpleDB	SDB
	A utility typedef for the common case.
typedef Ker	CostFunc
	a cost function maps (output,target) to a loss
typedef CostFunc	ProfitFunc
	a profit function maps (output,target) to a profit
typedef real(*	tRealFunc )(real)
typedef real(*	tRealReadFunc )(real, real)
typedef pair< real, StatsCollectorCounts * >	PairRealSCCType
typedef PP< StatsIterator >	StatsIt
typedef TinyVector< int, 7 >	IVec
typedef TTensor< real >	Tensor
typedef TMat< real >	Mat
typedef TVec< real >	Vec
typedef bool(*	MeasurerCallbackFunction )(int t, const Vec &costs)
typedef Array< PP< HyperOptimizer > >	HStrategy
typedef map< string, string >	HAliases
typedef RandomVar	MatRandomVar
typedef int	tFileHandle
typedef ofstream	pofstream
	The stream classes.
typedef PP< Learner >	PPLearner
typedef map< int, real >	SparseVec
typedef const map< int, real >	ConstSparseVec
typedef PPointableSet::iterator	SetIterator
Enumerations
enum	SDBVMOutputCoding { SDBVMUnknownCoding = 0, SDBVMNumeric, SDBVMOneHot, SDBVMOneHotMinus1 }
enum	FieldType {   Unknown = 0, StringType, CharacterType, SignedCharType,   ShortType, IntType, FloatType, DoubleType,   DateType }
enum	eNumericType {   NT_NOT_NUMERIC = 0x0000, NT_ORDINAL = 0x0001, NT_CARDINAL = 0x0002, NT_CURRENCY = 0x0004,   NT_PREFIXED = 0x0008, NT_SUFFIXED = 0x0010, NT_RANGE = 0x0020, NT_TIME = 0x0040,   NT_CODE = 0x0080, NT_PERCENT = 0x0100, NT_UNKNOWN_NUMERIC_TYPE = 0x8000 }
Functions
template<class T> void	swap (Array< T > &a1, Array< T > &a2)
template<class T> PStream &	operator>> (PStream &in, Array< T > &a)
template<class T> PStream &	operator<< (PStream &out, const Array< T > &a)
template<class T> ostream &	operator<< (ostream &out, const Array< T > &a)
template<class T> void	deepCopyField (Array< T > &field, CopiesMap &copies)
template<class T> Array< T >	operator & (const T &elem, const Array< T > &a)
template<class T> Array< T > &	operator &= (Array< T > &a, const T &elem)
template<class T> Array< T > &	operator &= (Array< T > &a, const Array< T > &ar)
template<class T> Array< T > &	operator &= (Array< T > &a, const vector< T > &ar)
template<class T> Array< T >	operator & (const Array< T > &a, const T &elem)
template<class T> Array< T >	operator & (const Array< T > &a, const Array< T > &ar)
template<class T> Array< T >	operator & (const Array< T > &a, const vector< T > &ar)
string	join (const Array< string > &s, const string &separator)
template<class T> Array< TVec< T > >	operator & (const TVec< T > &m1, const TVec< T > &m2)
	This will allow a convenient way of building arrays of Matrices by writing ex: m1&m2&m3.
template<class T> TVec< T >	concat (const Array< TVec< T > > &varray)
template<class T> TMat< T >	vconcat (const Array< TMat< T > > &ar)
template<class T> TMat< T >	hconcat (const Array< TMat< T > > &ar)
template<class T> TMat< T >	vconcat (const TMat< T > &m1, const TMat< T > &m2)
template<class T> TMat< T >	hconcat (const TMat< T > &m1, const TMat< T > &m2)
template<class T> Array< TMat< T > >	operator & (const TMat< T > &m1, const TMat< T > &m2)
	This will allow a convenient way of building arrays of Matrices by writing ex: m1&m2&m3.
void	endianswap2 (void *ptr, int n)
	swaps endians for n 2-byte elements (such as short)
void	endianswap4 (void *ptr, int n)
	swaps endians for n 4-byte elements (such as int or float)
void	endianswap8 (void *ptr, int n)
	swaps endians for n 8-byte elements (such as double)
char	byte_order ()
void	endianswap (char *ptr, int n=1)
void	endianswap (signed char *ptr, int n=1)
void	endianswap (unsigned char *ptr, int n=1)
void	endianswap (short *ptr, int n=1)
void	endianswap (unsigned short *ptr, int n=1)
void	endianswap (int *ptr, int n=1)
void	endianswap (unsigned int *ptr, int n=1)
void	endianswap (long *ptr, int n=1)
void	endianswap (unsigned long *ptr, int n=1)
void	endianswap (float *ptr, int n=1)
void	endianswap (double *ptr, int n=1)
template<class T> void	deepCopyField (T &, CopiesMap &)
	Types that do not require deep copy. Any type not handled below: do nothing.
template<class T> void	deepCopyField (T *&field, CopiesMap &copies)
template<class T> T *	deepCopy (const T *source, CopiesMap &copies)
	A simple template function that calls the method.
template<class T> T *	deepCopy (const T *source)
	This function simply calls the previous one with an initially empty map.
int	file_size (const string &filename)
	Simple file info.
char *	strcopy (char *s)
	make a copy of a C string and return it
void	pretty_print_number (char *buffer, real number)
	print a number without unnecessary trailing zero's, into buffer
bool	file_exists (const string &filename)
bool	isMapKeysAreInt (map< real, int > &m)
	check that all keys of the map are int values
string	hostname ()
string	prgname (const string &setname)
template<class In, class Out> Out	copy_cast (In first, In last, Out res)
	Like std::copy, but with an explicit cast to the destination type.
template<class T> void	clear_1 (T &x)
	clearing an element (that's called by clear_n...) Default implementation for clearing any type
void	clear_1 (char &x)
void	clear_1 (unsigned char &x)
void	clear_1 (signed char &x)
void	clear_1 (short &x)
void	clear_1 (unsigned short &x)
void	clear_1 (int &x)
void	clear_1 (unsigned int &x)
void	clear_1 (long &x)
void	clear_1 (unsigned long &x)
void	clear_1 (float &x)
void	clear_1 (double &x)
void	clear_1 (bool &x)
template<class For> void	clear_n (For begin, int n)
	clears n elements starting at iterator position begin
void	clear_n (float *begin, int n)
	efficient specialisation for built-in types
void	clear_n (double *begin, int n)
void	clear_n (bool *begin, int n)
void	clear_n (char *begin, int n)
void	clear_n (unsigned char *begin, int n)
void	clear_n (short *begin, int n)
void	clear_n (unsigned short *begin, int n)
void	clear_n (int *begin, int n)
void	clear_n (unsigned int *begin, int n)
void	clear_n (long *begin, int n)
void	clear_n (unsigned long *begin, int n)
template<class T> void	swap (T &a, T &b)
Object *	loadObject (const string &filename)
	Loads an object from the given file (no macro-preprocessing is performed).
Object *	macroLoadObject (const string &filename, map< string, string > &vars)
	Same as loadObject but first performs macro-processing on the file vars may be initialised with the values of some variables and upon return it will also contain newly $DEFINED variables.
Object *	macroLoadObject (const string &filename)
	same as previous, but no need to pass a variables map
Object *	readObject (PStream &in, unsigned int id)
PStream &	operator>> (PStream &in, Object *&x)
template<class T> Object *	toObjectPtr (const T *x)
	The toObjectPtr functions attempt to return a pointer to Object (or 0 if the passed argument cannot be considered an Object subclass).
template<class T> Object *	toObjectPtr (const T &x)
template<> Object *	toObjectPtr (const Object &x)
template<> Object *	toObjectPtr (const Object *x)
template<class T> Object *	toObjectPtr (const PP< T > &x)
template<class T> Object *	toIndexedObjectPtr (const Array< T > &x, int i)
template<class T> Object *	toIndexedObjectPtr (const TVec< T > &x, int i)
template<class T> Object *	toIndexedObjectPtr (const T &, int)
Object *	readObject (istream &in_)
Object *	newObject (const string &representation)
	Creates a new object according to the given representation.
ostream &	operator<< (ostream &out, const Object &obj)
PStream &	operator>> (PStream &in, Object &o)
PStream &	operator<< (PStream &out, const Object &o)
template<class ObjectType, class OptionType> void	declareOption (OptionList &ol, const string &optionname, OptionType ObjectType::*member_ptr, OptionBase::flag_t flags, const string &description, const string &defaultval="")
	For flags, you should specify one of OptionBase::buildoption, OptionBase::learntoption or OptionBase::tuningoption If the option is not to be serialized, you can additionally specify OptionBase::nosave.
template<class ObjectType, class OptionType> void	declareOption (OptionList &ol, const string &optionname, OptionType *ObjectType::*member_ptr, OptionBase::flag_t flags, const string &description, const string &defaultval="")
template<class ObjectType, class OptionType> void	redeclareOption (OptionList &ol, const string &optionname, OptionType ObjectType::*member_ptr, OptionBase::flag_t flags, const string &description, const string &defaultval="")
	Allows one to redeclare an option differently (e.g.
float	date_to_float (const PDate &t)
	converts date to float: ex: September 29 1972: 720929; December 25 2002: 1021225 Also converts missing date to missing flat value and vice-versa.
PDate	float_to_date (float f)
int	operator- (const PDate &to_date, const PDate &from_date)
	substract two dates, the result being counted in days.
PDate	operator+ (const PDate &pdate, int ndays)
	add a number of days
PDate	operator- (const PDate &pdate, int ndays)
	subtract a number of days add a number of days
ostream &	operator<< (ostream &os, const PDate &date)
PDate	float_to_date (double d)
double	datetime_to_double (const PDateTime &t)
	converts date/time to double: for example: September 29 1972: 720929; December 25 2002: 1021225.
PDateTime	double_to_datetime (double f)
double	hhmmss_to_double (int hh, int mm, int ss)
	converts an hours/minutes/seconds to a day fraction
void	double_to_hhmmss (double fraction, int &hh, int &mm, int &ss)
	convert a day fraction (< 1) to hours/minutes/seconds
double	operator- (const PDateTime &to_date, const PDateTime &from_date)
	subtract two dates, the result being counted in days (+ fractions)
ostream &	operator<< (ostream &os, const PDateTime &date)
size_t	hashbytes (const char *byte_start, size_t byte_length)
	**************** Hash tables support *************************
size_t	hashval (const char *strng)
template<class T> size_t	hashval (const T &x)
	default which will work in many cases but not all
void	errormsg (const char *msg,...)
void	warningmsg (const char *msg,...)
void	exitmsg (const char *msg,...)
template<class T> void	deepCopyField (PP< T > &field, CopiesMap &copies)
	Any pointer or smart pointer: call deepCopy().
template<class T> T *	deepCopy (PP< T > source, CopiesMap &copies)
	A simple template function.
template<class T> T *	deepCopy (PP< T > source)
	This function simply calls the previous one with an initially empty map.
ostream &	operator<< (ostream &out, Range r)
PStream &	operator<< (PStream &out, const RealRange &x)
PStream &	operator>> (PStream &in, RealRange &x)
bool	operator< (RealMapping::single_mapping_t a, RealMapping::single_mapping_t b)
void	write (ostream &out, const RealRange &range)
ostream &	operator<< (ostream &out, const RealRange &range)
void	read (istream &in, RealRange &range)
Object *	toObjectPtr (const RealMapping &o)
PStream &	operator>> (PStream &in, RealMapping &o)
PStream &	operator>> (PStream &in, RealMapping *&o)
PStream &	operator<< (PStream &out, const RealMapping &o)
PStream &	operator>> (PStream &in, PP< RealMapping > &o)
Object *	toObjectPtr (const SetOption &o)
PStream &	operator>> (PStream &in, SetOption &o)
PStream &	operator>> (PStream &in, SetOption *&o)
PStream &	operator<< (PStream &out, const SetOption &o)
PStream &	operator>> (PStream &in, PP< SetOption > &o)
template<class T, unsigned SizeBits, class Allocator> unsigned int	hashval (const SmallVector< T, SizeBits, Allocator > &v)
	hash function for hash tables
template<class T, unsigned SizeBits, class Allocator> bool	operator== (const SmallVector< T, SizeBits, Allocator > &a, const SmallVector< T, SizeBits, Allocator > &b)
	Equality operator.
template<class T, unsigned SizeBits, class Allocator> bool	operator!= (const SmallVector< T, SizeBits, Allocator > &x, const SmallVector< T, SizeBits, Allocator > &y)
	const SmallVector<T,SizeBits,Allocator>& y) { return !(x==y); }
template<class T, unsigned SizeBits> bool	operator< (const SmallVector< T, SizeBits > &, const SmallVector< T, SizeBits > &)
	const SmallVector<T,SizeBits,Allocator>&);
template<class T, unsigned SizeBits, class Allocator> bool	operator< (const SmallVector< T, SizeBits, Allocator > &x, const SmallVector< T, SizeBits, Allocator > &y)
template<class T> PStream &	operator<< (PStream &out, const Storage< T > &seq)
template<class T> PStream &	operator>> (PStream &in, Storage< T > &seq)
ostream &	operator<< (ostream &out, const StringTable &st)
string	left (const string &s, size_t width, char padding)
	aligns the given string in a cell having the given width
string	right (const string &s, size_t width, char padding)
string	center (const string &s, size_t width, char padding)
bool	pl_isnumber (const string &str, double *dbl)
bool	pl_isnumber (const string &str, float *dbl)
long	tolong (const string &s, int base)
	conversions from string to numerical types
bool	tobool (const string &s)
double	todouble (const string &s)
string	extract_filename (const string &filepath)
	** File path manipulation functions ** Returns everything after the last '/' (if there's no '/' returns filepath)
string	extract_directory (const string &filepath)
	Returns everything before the last '/' including the '/' (if there's no '/' it returns "./").
string	extract_extension (const string &filepath)
	Returns everything after the last '.' of the filename (i.e.
string	extract_filename_without_extension (const string &filepath)
	Returns everything before the last '.' of the filename, excluding the '.' (if there's no '.' in the filename it returns the whole filename).
string	remove_extension (const string &filename)
	Return the filename withoug the extension (i.e. removing the last.
string *	data_filename_2_filenames (const string &filename, int &nb_files)
	take a filename containing the name of a file per line, and return theses names as a string* of length nb_files
string	removeblanks (const string &s)
	removes starting and ending blanks ' ','',' ',''
string	removeallblanks (const string &s)
	removes all blanks ' ','',' ',''
string	removenewline (const string &s)
	removes any trailing ' ' and/or ''
string	remove_trailing_slash (const string &s)
	removes any trailing '/' from the path
string	append_slash (const string &path)
	appends a trailing slash to path if there isn't already one
string	lowerstring (const string &ss)
	convert a string to all lowercase
string	upperstring (const string &ss)
	convert a string to all uppercase
string	pgetline (istream &in)
	returns the next line read from the stream, after removing any trailing '' and/or ' '
bool	isBlank (const string &s)
	returns true if s is a blank line (containing only space, tab, until end of line or a # comment-character is reached
bool	isParagraphBlank (const string &s)
	returns true if s is a blank paragraph (containing only space, tab, until end of **string**)
string	space_to_underscore (string str)
	replaces all characters <= ' ' (i.e. newline, tab, space, etc...) by '_'
string	underscore_to_space (string str)
	replaces all underscores by a single space character
string	backslash_to_slash (string str)
	replaces all backslashes with slash
int	search_replace (string &text, const string &searchstr, const string &replacestr)
	replaces all occurences of searchstr in the text by replacestr returns the number of matches that got replaced
vector< string >	split (const string &s, char delimiter)
	splits a string along occurences of the delimiters.
vector< string >	split (const string &s, const string &delimiters, bool keep_delimiters)
void	split_on_first (const string &s, const string &delimiters, string &left, string &right)
pair< string, string >	split_on_first (const string &s, const string &delimiters)
void	remove_comments (string &text, const string &commentstart)
	In a multiline text, removes everything starting at commentstart pattern until the end of line.
string	join (const vector< string > &s, const string &separator)
	makes a single string from a vector of strings
vector< string >	remove (const vector< string > &v, string element)
	return vector with all instances of element removed
int	findit (const vector< string > &v, string element)
vector< string >	addprepostfix (const string &prefix, const vector< string > &names, const string &postfix)
	returns the list of names, but with a prepended prefix and an appended postfix
string	addprepostfix (const string &prefix, const string &text, const string &postfix)
	Returns a string with the prefix prepended and the postfix appended to each *line* of the text string.
vector< string >	stringvector (int argc, char **argv)
	makes a C++ style vector of strings from a C style vectr of strings Note: this may be useful in conjunction with get_option.
string	get_option (const vector< string > &command_line, const string &option, const string &default_value)
bool	find (const vector< string > &command_line, const string &option)
vector< string >	getNonBlankLines (const string &in)
	Returns a vector of string containing only non-empty lines, as you guessed it.
ostream &	operator<< (ostream &out, const vector< string > &vs)
	formatted printing of vector<string> prints strings separated by a ", "
string	tostring (const double &x)
string	tostring (const float &x)
template<class T> string	tostring (const T &x)
	------------------------------------------------------------------
string	tostring (const char *s)
	specialised version for char*
int	toint (const string &s, int base=10)
float	tofloat (const string &s)
	FLOAT.
vector< string >	addprefix (const string &prefix, const vector< string > &names)
	returns the list of names, but with a prepended prefix
vector< string >	addpostfix (const vector< string > &names, const string &postfix)
	returns the list of names, but with an appended postfix
string	addprefix (const string &prefix, const string &text)
	Returns a string with the prefix prepended to each *line* of the text string.
string	addpostfix (const string &text, const string &postfix)
	Returns a string with the postfix appended to each *line* of the text string.
int	findpos (const vector< string > &v, string element)
	return index of element in v, or -1 if not found
template<class U, class V> ostream &	operator<< (ostream &out, const pair< U, V > &p)
	Formatted printing of a pair<U,V> as U:V.
template<class T, unsigned N, class TTrait> bool	operator== (const TinyVector< T, N, TTrait > &, const TinyVector< T, N, TTrait > &)
	Equality operator.
template<class T, unsigned N, class TTrait> bool	operator< (const TinyVector< T, N, TTrait > &, const TinyVector< T, N, TTrait > &)
	Lexicographical Ordering.
template<class T, unsigned N, class TTrait> bool	operator!= (const TinyVector< T, N, TTrait > &x, const TinyVector< T, N, TTrait > &y)
	Other operators (should be defined in std::rel_ops, but does not work properly with gcc yet).
template<class T, unsigned N, class TTrait> bool	operator> (const TinyVector< T, N, TTrait > &x, const TinyVector< T, N, TTrait > &y)
template<class T, unsigned N, class TTrait> bool	operator<= (const TinyVector< T, N, TTrait > &x, const TinyVector< T, N, TTrait > &y)
template<class T, unsigned N, class TTrait> bool	operator>= (const TinyVector< T, N, TTrait > &x, const TinyVector< T, N, TTrait > &y)
void	displayObjectHelp (ostream &out, const string &classname)
	Will display the help message for an object of the given classname.
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (char, 0x01, 0x01)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (signed char, 0x01, 0x01)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (unsigned char, 0x02, 0x02)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (short, 0x03, 0x04)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (unsigned short, 0x05, 0x06)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (int, 0x07, 0x08)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (unsigned int, 0x0B, 0x0C)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (long, 0x07, 0x08)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (float, 0x0E, 0x0F)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (double, 0x10, 0x11)
	DECLARE_TYPE_TRAITS_FOR_BASETYPE (bool, 0x12, 0x12)
Mat	input2dSet (const string &filename)
	This will input a 2d binary classification problem (launches a java applet).
void	normalizeDataSets (Mat &training_set, Mat &validation_set, Mat &test_set)
void	normalizeDataSets (VMat &training_set, VMat &validation_set, VMat &test_set)
void	normalizeDataSets (Mat &training_set, Mat &test_set)
	normalize both training_set and test_set according to mean and stddev computed on training_set
void	normalizeDataSet (Mat &m)
void	splitTrainValidTest (VMat &data_set, VMat &train_set, VMat &valid_set, real valid_fraction, VMat &test_set, real test_fraction, bool normalize)
VMat	reduceInputSize (real fraction, VMat data)
VMat	reduceDataSetSize (real fraction, VMat data)
void	remapClassnums (VMat &data, real remap_minval_to, real remap_maxval_to)
	remaps classnums to {0,1} or to {-1,+1}
VMat	loadBreastCancerWisconsin (bool normalize, bool uniq)
int	loadBreastCancer (VMat &training_set, VMat &validation_set, VMat &test_set, int ntrain, int nvalid, bool uniq)
	These calls return the number of classes...
VMat	loadPimaIndians (bool normalize)
VMat	loadHousing (bool normalize)
VMat	loadSonar ()
VMat	loadIonosphere ()
VMat	loadDiabetes (bool normalize)
int	loadDiabetes (VMat &training_set, VMat &validation_set, VMat &test_set, int ntrain, int nvalid)
int	loadATT800 (VMat &training_set, VMat &test_set)
VMat	loadLetters (bool normalize)
VMat	loadLetters (const char *class0, const char *class1, bool normalize)
int	loadLetters (VMat &training_set, VMat &validation_set, VMat &test_set, char *which_letters, real validation_fraction, real test_fraction, bool do_shuffle)
VMat	loadLetters (int n_letters, bool do_shuffle)
int	loadLetters (VMat &training_set, VMat &validation_set, VMat &test_set, int n_letters, real validation_fraction, real test_fraction, bool do_shuffle)
void	loadCorelDatamat (int classnum, Mat &train, Mat &valid, Mat &test)
Mat	smoothCorelHisto (Mat &data)
void	loadCorel (Mat &training_set, Mat &validation_set, Mat &test_set, int negative_class, int positive_class)
void	loadCallxx (int year, VMat &d)
void	loadUSPS (VMat &trainset, VMat &testset, bool use_smooth)
VMat	loadUSPS (bool use_smooth)
void	loadLetters (int &inputsize, int &nclasses, VMat &trainset, VMat &testset)
void	loadClassificationDataset (const string &datasetname, int &inputsize, int &nclasses, VMat &trainset, VMat &testset, bool normalizeinputs, VMat &allset)
void	loadUCI (VMat &trainset, VMat &testset, VMat &allset, string db_spec, string id, bool &normalize, const string &type)
	Load the train, test and all datasets for a UCI database.
void	loadUCIAMat (VMat &data, string file, PP< UCISpecification > uci_spec)
	Load a AMAT format UCI dataset in the given VMatrix.
void	loadUCISet (VMat &data, string file, PP< UCISpecification > uci_spec)
	Load a specific UCI dataset in the given VMatrix.
string	loadClassificationDatasetHelp ()
string	getDataSetHelp ()
	returns a help describing the datasetstring parameter of getDataSet
time_t	getDataSetDate (const string &datasetstring, const string &alias)
VMat	getDataSet (const string &datasetstring, const string &alias)
string	locateDatasetAliasesDir (const string &dir_or_file_path)
	Looks for 'dataset.aliases' file in specified directory and its parent directories; Returns the directory containing dataset.aliases (returned string will be terminated by a slash) or an empty string if not found.
map< string, string >	getDatasetAliases (const string &dir_or_file_path)
	Looks for 'dataset.aliases' file in specified directory and its parent directories; loads it and returns the corresponding map.
void	loadMNIST (VMat &training_set, VMat &test_set)
int	ICBCpartition (const Vec &claims, real threshold)
ostream &	operator<< (ostream &os, const FieldValue &ft)
double	todouble (const RowIterator &it)
string	tostring (const RowIterator &it)
void	printFieldName (ostream &o, const Row::iterator &field)
	outputs the given field name in a cell of apropriate size
void	printFieldNames (ostream &o, const Row &rowc)
	outputs all field names, separated by " | "
ostream &	operator<< (ostream &o, const Row::iterator &field)
	outputs a single field flushed right in a cell of apropriate width (as given by field.char_width())
ostream &	operator<< (ostream &o, const Row &rowc)
	outputs all fields in a row, separated by " | "
void	halfShuffleRows (SDB &sdb)
	not quite a random shuffle (see implementation) but more efficient use of disk cache
void	randomShuffleRows (SDB &sdb)
	Performs a random permutation of all the rows of the SDB (same algorithm as Mat::shuffle).
double	todouble (const Row::iterator &it)
	Generic conversions from an iterator.
string	tostring (const Row::iterator &it)
	PLEARN_IMPLEMENT_OBJECT (UCISpecification,"Describes the specifications of a UCI database.","This object specifies characteristics of a database from the UCI machine\n""learning repository, such as the input size, target size, etc...\n""It is intended to be used in a script put in the same directory as the\n""database, in order to be loaded by the getDataSet() method.\n")
	DECLARE_OBJECT_PTR (UCISpecification)
void	displayHistogram (Gnuplot &gp, Mat dataColumn, int n_bins, Vec *pbins, bool regular_bins, bool normalized, string extra_args)
void	displayVarGraph (const VarArray &outputs, bool display_values, real boxwidth, const char *the_filename, bool must_wait, VarArray display_only_these)
	VarGraph *.
void	OldDisplayVarGraph (const VarArray &outputs, bool display_values, real boxwidth, const char *the_filename, bool must_wait, VarArray display_only_these)
void	displayFunction (Func f, bool display_values, bool display_differentiation, real boxwidth, const char *the_filename, bool must_wait)
Mat	compute2dGridOutputs (Learner &learner, real min_x, real max_x, real min_y, real max_y, int length, int width, real singleoutput_threshold)
void	displayPoints (GhostScript &gs, Mat data, real radius, bool color)
	this draws x and + with the given radius for all the points in data (supposed to have width 3: [x, y, classnum]
void	displayDecisionSurface (GhostScript &gs, real destx, real desty, real destwidth, real destheight, Learner &learner, Mat trainset, Vec svindexes, Vec outlierindexes, int nextsvindex, real min_x, real max_x, real min_y, real max_y, real radius, int nx, int ny)
real	rgb2real (real r, real g, real b)
void	real2rgb (real colorval, real &r, real &g, real &b)
void	matlabR11eigs (RowMapSparseMatrix< real > &A, Mat eigen_vectors, Vec eigen_values, string which_eigenvalues)
void	matlabR11eigs (RowMapSparseMatrix< double > &A, Mat eigen_vectors, Vec eigen_values, string which_eigenvalues="LM")
	PLEARN_IMPLEMENT_OBJECT (FilePStreamBuf,"ONE LINE DESCRIPTION","MULTI LINE\nHELP")
	DECLARE_OBJECT_PTR (FilePStreamBuf)
string	getcwd ()
	returns the absolute path to the current working directory as a string
int	chdir (const string &path)
	change current directory
string	abspath (const string &path)
	returns the absolute path of the (possibly relative) specified path.
bool	pathexists (const string &path)
	returns true if the given path points to an existing regular file or directory
bool	isdir (const string &path)
	returns true if the given path is an existing directory (or a symbolic link pointing to a directory)
bool	isfile (const string &path)
	returns true if the given path is an existing regular file (or a symbolic link pointing to a file)
time_t	mtime (const string &path)
	returns the time of last modification of file (or 0 if file does not exist).
vector< string >	lsdir (const string &dirpath)
vector< string >	lsdir_fullpath (const string &dirpath)
	Same as lsdir, except dirpath is prepended to the entries' names.
bool	force_mkdir (const string &dirname)
void	force_mkdir_for_file (const string &filepath)
	Extracts the directory part of the filepath and calls force_mkdir Calls PLERROR in case of failure.
bool	force_rmdir (const string &dirname)
long	filesize (const string &filename)
	Returns the length of a file, measured in bytes.
string	loadFileAsString (const string &filepath)
	Returns the whole content of the file as a string.
void	saveStringInFile (const string &filepath, const string &text)
	Writes the raw string into the given file Intermediate directories in filepath are created if necessary.
void	cp (const string &srcpath, const string &destpath)
	calls system with cp -R to recursively copy source to destination
void	rm (const string &file)
	calls system rm command with string file as parameters
void	mv (const string &file)
	calls system mv command with string file as parameters
void	mvforce (const string &file)
	calls system mv command with string file as parameters will not prompt before overwriting
void	readWhileMatches (istream &in, const string &s)
	Reads while the characters read exactly match those in s Will throw a PLERROR exception as soon as it doesn't match.
void	skipRestOfLine (istream &in)
	skips everything until ' ' (also consumes the ' ')
void	skipBlanksAndComments (istream &in)
	will skip all blanks (white space, newline and #-style comments) Next character read will be first "non-blank"
void	getNextNonBlankLine (istream &in, string &line)
	returns the next non blank line (#-style comments are considered blank)
int	countNonBlankLinesOfFile (const string &filename)
	Will return the number of non-blank lines of file #-style comments are considered blank.
int	smartReadUntilNext (istream &in, string stoppingsymbols, string &characters_read, bool ignore_brackets)
	same as PStream's method smartReadUntilNext, but for istream
string	newFilename (const string directory, const string prefix, bool is_directory)
	Returns a temporary file (or directory) name suitable for a unique (one time) use.
string	makeFileNameValid (const string &path)
void	touch (const string &file)
	trivial unix touch
string	makeExplicitPath (const string &filename)
	returns "./"+filename if filename is relative to current dir
string	readFileAndMacroProcess (const string &filepath, map< string, string > &variables)
	Same as readAndMacroProcess, but takes a filename instead of an istream The following variables are automatically set from the filepath: FILEPATH DIRPATH FILENAME FILEBASE FILEEXT.
string	readAndMacroProcess (istream &in, map< string, string > &variables)
	Will return the text, macro processed, with each instance of ${varname} in the text that corresponds to a key in the given map replaced by its associated value.
char	peekAfterSkipBlanks (istream &in)
	peeks the first char after removal of blanks
char	peekAfterSkipBlanksAndComments (istream &in)
	peeks the first char after removal of blanks and comments
char	getAfterSkipBlanks (istream &in)
	gets the first char after removal of blanks
char	getAfterSkipBlanksAndComments (istream &in)
	gets the first char after removal of blanks and comments
string	readFileAndMacroProcess (const string &filepath)
FilesIntStream *	word_sequences2files_int_stream (const char *word_sequences_file)
void	loadMat (const string &file_name, TMat< float > &mat)
	Tries to guess the format...
void	loadMat (const string &file_name, TMat< double > &mat)
void	loadVec (const string &file_name, TVec< float > &vec)
void	loadVec (const string &file_name, TVec< double > &vec)
void	savePVec (const string &filename, const TVec< float > &vec)
	Old native PLearn binary format (.pmat).
void	savePVec (const string &filename, const TVec< double > &vec)
void	loadPVec (const string &filename, TVec< float > &vec)
void	loadPVec (const string &filename, TVec< double > &vec)
void	savePMat (const string &filename, const TMat< float > &mat)
void	savePMat (const string &filename, const TMat< double > &mat)
void	loadPMat (const string &filename, TMat< float > &mat)
void	loadPMat (const string &filename, TMat< double > &mat)
void	saveGnuplot (const string &filename, const Vec &vec)
void	saveGnuplot (const string &filename, const Mat &mat)
void	loadGnuplot (const string &filename, Mat &mat)
	Format readable by gnuplot.
void	matlabSave (const string &dir, const string &plot_title, const Vec &data, const Vec &add_col, const Vec &bounds, string legend, bool save_plot)
void	matlabSave (const string &dir, const string &plot_title, const Vec &xValues, const Vec &yValues, const Vec &add_col, const Vec &bounds, string legend, bool save_plot)
void	matlabSave (const string &dir, const string &plot_title, const Mat &data, const Vec &add_col, const Vec &bounds, TVec< string > legend, bool save_plot)
	Simply calls the coming matlabSave function with an empty xValues Vec. See below.
void	matlabSave (const string &dir, const string &plot_title, const Vec &xValues, const Mat &yValues, const Vec &add_col, const Vec &bounds, TVec< string > legend, bool save_plot)
void	saveAsciiWithoutSize (const string &filename, const Vec &vec)
void	loadAsciiWithoutSize (const string &filename, const Vec &vec)
	Reads and writes an ascii file without the size header (assuming that the size(length() and width()) is set).
void	saveAsciiWithoutSize (const string &filename, const Mat &mat)
void	loadAsciiWithoutSize (const string &filename, const Mat &mat)
void	saveSNMat (const string &filename, const Mat &mat)
Mat	loadSNMat (const string &filename)
	SN Format.
void	saveSNVec (const string &filename, const Vec &vec)
Vec	loadSNVec (const string &filename)
Mat	loadADMat (const string &filename)
	Native AD format.
Vec	loadADVec (const string &filename)
int	compare_string_pointers (const void *ts1, const void *ts2)
Mat	loadUCIMLDB (const string &filename, char ****to_symbols, int **to_n_symbols, TVec< int > *the_max_in_col, TVec< string > *header_columns)
Mat	loadSTATLOG (const string &filename, char ****to_symbols, int **to_n_symbols)
void	loadJPEGrgb (const string &jpeg_filename, Mat &rgbmat, int &row_size, int scale)
void	parseSizeFromRemainingLines (const string &filename, ifstream &in, bool &could_be_old_amat, int &length, int &width)
Mat	makeMat (int length, int width, const string &values)
	convenience construction from string allows to write things such as Mat m = newMat(2,2, "1 2 3 4")
Vec	makeVec (int length, const string &values)
template<class T> void	loadAscii (const string &filename, TMat< T > &mat, TVec< string > &fieldnames, TVec< map< string, real > > *map_sr=0)
	WARNING: use only for float, double, and int types. Other type are not guaranteed to work intelligent functions that will load a file in almost all ascii formats that ever existed in this lab.
template<class T> void	loadAscii (const string &filename, TMat< T > &mat)
template<class T> void	saveAscii (const string &filename, const TMat< T > &mat, const TVec< string > &fieldnames)
template<class T> void	saveAscii (const string &filename, const TMat< T > &mat)
template<class T> void	saveAscii (const string &filename, const TVec< T > &vec)
	first number in file is length
template<class T> void	loadAscii (const string &filename, TVec< T > &vec)
template<class T> void	loadAsciiSingleBinaryDescriptor (const string &filename, TMat< T > &mat)
	Load an ASCII matrix whose format is: (entry_name, long_binary_dscriptor) with 'long_binary_dscriptor' being of the form '001100101011', each character being an entry of the matrix.
void	write_compr_mode_and_size (ostream &out, unsigned char mode, int size)
void	read_compr_mode_and_size (istream &in, unsigned char &mode, int &size)
void	binread_compressed (istream &in, double *data, int l)
void	binwrite_compressed (ostream &out, const double *data, int l)
	version for compressed array (efficient for sparse data, and small integer values) (format is detailed in .cc, see write_compr_mode_and_size function in general.cc)
void	binread_compressed (istream &in, float *data, int l)
void	binwrite_compressed (ostream &out, const float *data, int l)
void	read_compr_mode_and_size (FILE *in, unsigned char &mode, int &size)
void	binread_compressed (FILE *in, double *data, int l)
void	binwrite_compressed (FILE *out, const double *data, int l)
void	binread_compressed (FILE *in, float *data, int l)
void	binwrite_compressed (FILE *out, const float *data, int l)
void	write_compr_mode_and_size_ptr (char *&out, unsigned char mode, int size)
void	read_compr_mode_and_size_ptr (char *&in, unsigned char &mode, int &size)
	DEPRECATED DO NOT USE! compressed vec to and from memory.
void	uncompress_vec (char *comprbuf, double *data, int l, bool double_stored_as_float)
void	compress_vec (char *comprbuf, const double *data, int l, bool double_stored_as_float)
size_t	new_read_compressed (FILE *in, real *vec, int l, bool swap_endians)
	Reads the l doubles in the new compressed formtat from in Returns the number of bytes read.
unsigned char	new_get_compr_data_type (double x, double tolerance)
unsigned char	new_get_compr_data_type (float x)
size_t	new_write_mode_and_size (FILE *out, bool insert_zeroes, unsigned int N, unsigned char data_type)
	returns number of bytes written
size_t	new_write_raw_data_as (FILE *out, real *vec, int l, unsigned char data_type)
size_t	new_write_compressed (FILE *out, real *vec, int l, double tolerance, bool swap_endians)
	Writes the l doubles in new compressed format to out.
template<class T> void	binwrite (ostream &out, const T *x, int n)
	general purpose (but less efficient) version for pointers to things that have a binwrite/binread function
template<class T> void	binread (istream &in, T *x, int n)
template<class A, class B> void	binwrite (ostream &out, const pair< A, B > x)
template<class A, class B> void	binread (istream &in, pair< A, B > &x)
void	binwrite (ostream &out, char x)
	binwrite and binread for a few basic types
void	binread (istream &in, char &x)
void	binwrite (ostream &out, unsigned char x)
void	binread (istream &in, unsigned char &x)
void	binwrite (ostream &out, int x)
void	binread (istream &in, int &x)
void	binwrite (ostream &out, unsigned int x)
void	binread (istream &in, unsigned int &x)
void	binwrite (ostream &out, short x)
void	binread (istream &in, short &x)
void	binwrite (ostream &out, unsigned short x)
void	binread (istream &in, unsigned short &x)
void	binwrite (ostream &out, bool x)
	note that bool are saved as unsigned short
void	binread (istream &in, bool &x)
void	binwrite (ostream &out, float x)
void	binread (istream &in, float &x)
void	binwrite (ostream &out, double x)
void	binread (istream &in, double &x)
void	binwrite_double (ostream &out, double x)
void	binread_double (istream &in, double &x)
void	binwrite_double (ostream &out, float x)
void	binread_double (istream &in, float &x)
void	binwrite (ostream &out, const int *x, int n)
	multi-element versions, giving address and number of elements
void	binread (istream &in, int *x, int n)
void	binwrite (ostream &out, const unsigned int *x, int n)
void	binread (istream &in, unsigned int *x, int n)
void	binwrite (ostream &out, const short *x, int n)
void	binread (istream &in, short *x, int n)
void	binwrite (ostream &out, const unsigned short *x, int n)
void	binread (istream &in, unsigned short *x, int n)
void	binwrite (ostream &out, const float *x, int n)
void	binread (istream &in, float *x, int n)
void	binwrite (ostream &out, const double *x, int n)
void	binread (istream &in, double *x, int n)
void	binwrite_double (ostream &out, const double *x, int n)
void	binread_double (istream &in, double *x, int n)
void	binwrite_double (ostream &out, const float *x, int n)
void	binread_double (istream &in, float *x, int n)
template<class T> void	binwrite (FILE *out, const T *x, int n)
	general purpose (but less efficient) version for pointers to things that have a binwrite/binread function
template<class T> void	binread (FILE *in, T *x, int n)
template<class A, class B> void	binwrite (FILE *out, const pair< A, B > x)
template<class A, class B> void	binread (FILE *in, pair< A, B > &x)
void	binwrite (FILE *out, char x)
	binwrite and binread for a few basic types
void	binread (FILE *in, char &x)
void	binwrite (FILE *out, unsigned char x)
void	binread (FILE *in, unsigned char &x)
void	binwrite (FILE *out, int x)
void	binread (FILE *in, int &x)
void	binwrite (FILE *out, unsigned int x)
void	binread (FILE *in, unsigned int &x)
void	binwrite (FILE *out, short x)
void	binread (FILE *in, short &x)
void	binwrite (FILE *out, unsigned short x)
void	binread (FILE *in, unsigned short &x)
void	binwrite (FILE *out, bool x)
	note that bool are saved as unsigned short
void	binread (FILE *in, bool &x)
void	binwrite (FILE *out, float x)
void	binread (FILE *in, float &x)
void	binwrite (FILE *out, double x)
void	binread (FILE *in, double &x)
void	binwrite_double (FILE *out, double x)
void	binread_double (FILE *in, double &x)
void	binwrite_double (FILE *out, float x)
void	binread_double (FILE *in, float &x)
void	binwrite (FILE *out, const int *x, int n)
	multi-element versions, giving address and number of elements
void	binread (FILE *in, int *x, int n)
void	binwrite (FILE *out, const unsigned int *x, int n)
void	binread (FILE *in, unsigned int *x, int n)
void	binwrite (FILE *out, const short *x, int n)
void	binread (FILE *in, short *x, int n)
void	binwrite (FILE *out, const unsigned short *x, int n)
void	binread (FILE *in, unsigned short *x, int n)
void	binwrite (FILE *out, const float *x, int n)
void	binread (FILE *in, float *x, int n)
void	binwrite (FILE *out, const double *x, int n)
void	binread (FILE *in, double *x, int n)
void	binwrite_double (FILE *out, const double *x, int n)
void	binread_double (FILE *in, double *x, int n)
void	binwrite_double (FILE *out, const float *x, int n)
void	binread_double (FILE *in, float *x, int n)
void	writeHeader (ostream &out, const string &classname, int version)
	writes "<ClassName:version>\n"
void	writeFooter (ostream &out, const string &classname)
	writes "</ClassName>\n"
int	readHeader (istream &in, const string &classname)
	consumes "<ClassName:version>\n and returns version"
void	readFooter (istream &in, const string &classname)
	consumes "</ClassName>\n"
void	writeFieldName (ostream &out, const string &fieldname)
	writes "fieldname: "
bool	readFieldName (istream &in, const string &fieldname, bool force)
	consumes "fieldname: " if possible, and return true if it does however if force=true and fieldname is not found then call error.
void	fwrite_int (FILE *f, const int *ptr, int n, bool is_file_bigendian)
	Writes binary data to the file in the specified representation (little or big endian) regardeless of the endianness used on the current architecture.
void	fwrite_float (FILE *f, const float *ptr, int n, bool is_file_bigendian)
void	fwrite_float (FILE *f, const double *ptr, int n, bool is_file_bigendian)
	writes double array to float file
void	fwrite_double (FILE *f, const double *ptr, int n, bool is_file_bigendian)
void	fwrite_double (FILE *f, const float *ptr, int n, bool is_file_bigendian)
	writes float array to double file
void	fread_int (FILE *f, int *ptr, int n, bool is_file_bigendian)
	Reads binary data from a file assuming it is in the specified representation (either little or big endian) If necessary the representation is translated to the endianness on the current architecture.
void	fread_float (FILE *f, float *ptr, int n, bool is_file_bigendian)
void	fread_float (FILE *f, double *ptr, int n, bool is_file_bigendian)
	reads disk floats into double array
void	fread_double (FILE *f, double *ptr, int n, bool is_file_bigendian)
void	fread_double (FILE *f, float *ptr, int n, bool is_file_bigendian)
	reads disk doubles into float array
void	fread_short (FILE *f, unsigned short *ptr, int n, bool is_file_bigendian)
void	write_int (ostream &out, const int *ptr, int n, bool is_file_bigendian)
	Writes binary data to the file in the specified representation (little or big endian) regardeless of the endianness used on the current architecture.
void	write_short (ostream &out, const short *ptr, int n, bool is_file_bigendian)
void	write_double (ostream &out, const double *ptr, int n, bool is_file_bigendian)
void	write_float (ostream &out, const float *ptr, int n, bool is_file_bigendian)
void	read_int (istream &in, int *ptr, int n, bool is_file_bigendian)
	Reads binary data from a file assuming it is in the specified representation (either little or big endian) If necessary the representation is translated to the endianness on the current architecture.
void	read_short (istream &in, short *ptr, int n, bool is_file_bigendian)
void	read_float (istream &in, float *ptr, int n, bool is_file_bigendian)
void	read_double (istream &in, double *ptr, int n, bool is_file_bigendian)
void	reverse_uint (const unsigned int *ptr, int n)
	NOTE: these calls are deprecated, use directly endianswap from base/byte_order.h.
void	reverse_int (const int *ptr, int n)
void	reverse_float (const float *ptr, int n)
void	reverse_double (const double *ptr, int n)
void	reverse_ushort (const unsigned short *ptr, int n)
void	reverse_short (const short *ptr, int n)
int	fread_int (FILE *f, bool is_file_bigendian=true)
float	fread_float (FILE *f, bool is_file_bigendian=true)
double	fread_double (FILE *f, bool is_file_bigendian=true)
void	fwrite_int (FILE *f, int value, bool is_file_bigendian=true)
	The following calls write a single value to the file in the specified representation, regardeless of the endianness on the current architecture.
void	fwrite_float (FILE *f, float value, bool is_file_bigendian=true)
void	fwrite_double (FILE *f, double value, bool is_file_bigendian=true)
void	write_uint (ostream &out, const unsigned int *ptr, int n, bool is_file_bigendian)
void	write_ushort (ostream &out, const unsigned short *ptr, int n, bool is_file_bigendian)
void	write_bool (ostream &out, const bool *ptr, int n, bool is_file_bigendian)
void	write_int (ostream &out, int value, bool is_file_bigendian=true)
	The following calls write a single value to the file in the specified representation, regardeless of the endianness on the current architecture.
void	write_short (ostream &out, short value, bool is_file_bigendian=true)
void	write_float (ostream &out, float value, bool is_file_bigendian=true)
void	write_double (ostream &out, double value, bool is_file_bigendian=true)
void	write_uint (ostream &out, unsigned int value, bool is_file_bigendian=true)
void	write_ushort (ostream &out, unsigned short value, bool is_file_bigendian=true)
void	write_sbyte (ostream &out, signed char x)
void	write_ubyte (ostream &out, unsigned char x)
void	read_uint (istream &in, unsigned int *ptr, int n, bool is_file_bigendian)
void	read_ushort (istream &in, unsigned short *ptr, int n, bool is_file_bigendian)
void	read_bool (istream &in, bool *ptr, int n, bool is_file_bigendian)
int	read_int (istream &in, bool is_file_bigendian=true)
short	read_short (istream &in, bool is_file_bigendian=true)
float	read_float (istream &in, bool is_file_bigendian=true)
double	read_double (istream &in, bool is_file_bigendian=true)
unsigned int	read_uint (istream &in, bool is_file_bigendian=true)
unsigned short	read_ushort (istream &in, bool is_file_bigendian=true)
signed char	read_sbyte (istream &in)
unsigned char	read_ubyte (istream &in)
void	writeNewline (ostream &out)
	Writes a single newline character.
void	readNewline (istream &in)
	Reads next character and issues an error if it's not a newline.
template<class T> void	writeField (ostream &out, const string &fieldname, const T &x)
	generic field writing and reading
template<class T> void	readField (istream &in, const string &fieldname, T &x)
template<class T> void	binwriteField (ostream &out, const string &fieldname, const T &x)
	generic field BINARY writing and reading
template<class T> void	binreadField (istream &in, const string &fieldname, T &x)
template<class T> void	binwriteField_double (ostream &out, const string &fieldname, const T &x)
template<class T> void	binreadField_double (istream &in, const string &fieldname, T &x)
template<class T> void	readField (istream &in, const string &fieldname, T &x, T default_value)
	readField with a default value when the field is not found
PStream &	flush (PStream &out)
PStream &	endl (PStream &out)
PStream &	ws (PStream &in)
void	binread_ (PStream &in, bool *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, double *x, unsigned int n, unsigned char typecode)
	The binread_ for float and double are special.
void	binread_ (PStream &in, float *x, unsigned int n, unsigned char typecode)
template<class T> PStream &	operator>> (PStream &in, T *&x)
template<class T> PStream &	operator<< (PStream &out, const T *&x)
template<class T> PStream &	operator>> (PStream &in, PP< T > &o)
template<class T> PStream &	operator<< (PStream &out, const PP< T > &o)
template<class T> PStream &	operator<< (PStream &out, T *&ptr)
PStream &	operator<< (PStream &out, bool x)
template<class A, class B> PStream &	operator<< (PStream &out, const pair< A, B > &x)
template<typename S, typename T> PStream &	operator>> (PStream &in, pair< S, T > &x)
template<class MapT> void	writeMap (PStream &out, const MapT &m)
template<class MapT> void	readMap (PStream &in, MapT &m)
template<class Key, class Value> PStream &	operator<< (PStream &out, const map< Key, Value > &m)
template<class Key, class Value> PStream &	operator>> (PStream &in, map< Key, Value > &m)
template<class Key, class Value> PStream &	operator<< (PStream &out, const multimap< Key, Value > &m)
template<class Key, class Value> PStream &	operator>> (PStream &in, multimap< Key, Value > &m)
template<class Key, class Value> PStream &	operator<< (PStream &out, const hash_map< Key, Value > &m)
template<class Key, class Value> PStream &	operator>> (PStream &in, hash_map< Key, Value > &m)
template<class Key, class Value> PStream &	operator<< (PStream &out, const hash_multimap< Key, Value > &m)
template<class Key, class Value> PStream &	operator>> (PStream &in, hash_multimap< Key, Value > &m)
template<class Iterator> void	binwrite_ (PStream &out, Iterator &it, unsigned int n)
	Serialization of sequences *.
void	binwrite_ (PStream &out, const bool *x, unsigned int n)
void	binwrite_ (PStream &out, const char *x, unsigned int n)
void	binwrite_ (PStream &out, char *x, unsigned int n)
void	binwrite_ (PStream &out, const signed char *x, unsigned int n)
void	binwrite_ (PStream &out, signed char *x, unsigned int n)
void	binwrite_ (PStream &out, const unsigned char *x, unsigned int n)
void	binwrite_ (PStream &out, unsigned char *x, unsigned int n)
void	binwrite_ (PStream &out, const short *x, unsigned int n)
void	binwrite_ (PStream &out, short *x, unsigned int n)
void	binwrite_ (PStream &out, const unsigned short *x, unsigned int n)
void	binwrite_ (PStream &out, unsigned short *x, unsigned int n)
void	binwrite_ (PStream &out, const int *x, unsigned int n)
void	binwrite_ (PStream &out, int *x, unsigned int n)
void	binwrite_ (PStream &out, const unsigned int *x, unsigned int n)
void	binwrite_ (PStream &out, unsigned int *x, unsigned int n)
void	binwrite_ (PStream &out, const long *x, unsigned int n)
void	binwrite_ (PStream &out, long *x, unsigned int n)
void	binwrite_ (PStream &out, const unsigned long *x, unsigned int n)
void	binwrite_ (PStream &out, unsigned long *x, unsigned int n)
void	binwrite_ (PStream &out, const float *x, unsigned int n)
void	binwrite_ (PStream &out, float *x, unsigned int n)
void	binwrite_ (PStream &out, const double *x, unsigned int n)
void	binwrite_ (PStream &out, double *x, unsigned int n)
template<class Iterator> void	binread_ (PStream &in, Iterator it, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, char *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, signed char *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, unsigned char *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, short *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, unsigned short *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, int *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, unsigned int *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, long *x, unsigned int n, unsigned char typecode)
void	binread_ (PStream &in, unsigned long *x, unsigned int n, unsigned char typecode)
template<class SequenceType> void	writeSequence (PStream &out, const SequenceType &seq)
template<class SequenceType> void	readSequence (PStream &in, SequenceType &seq)
	This reads into a sequence.
template<class T> void	write (ostream &out_, const T &o)
template<class T> void	read (istream &in_, T &o)
template<class T> void	read (const string &stringval, T &x)
template<class T> PStream &	operator>> (PStream &in, vector< T > &v)
template<class T> PStream &	operator<< (PStream &out, const vector< T > &v)
template<class SetT> void	writeSet (PStream &out, const SetT &s)
template<class SetT> void	readSet (PStream &in, SetT &s)
template<class T> PStream &	operator>> (PStream &in, set< T > &v)
template<class T> PStream &	operator<< (PStream &out, const set< T > &v)
template<class T> void	load (const string &filepath, T &x)
template<class T> void	save (const string &filepath, const T &x)
	If necessary, missing directories along the filepath will be created.
istream	nullin (&null_streambuf)
ostream	nullout (&null_streambuf)
iostream	nullinout (&null_streambuf)
const string	wordseparators (" \t\n\r)]};,:|#")
	List of characters considered to mark a separation between "words"; This is a fairly restricted list, meaning that many things can be part of a "word" in this sense (for ex: "this-is_a+single@wor'd"), this is to insure a smooth transition for the new setOption, which calls readOptionVal ...
const char *	eNumericTypeNames (int a)
	converts a code in corresponding string
bool	containsChar (const char *s, const char *symbols)
	true if string s contains any one of the characters in symbols.
char *	stringPos (const char *s, const char *strings[])
bool	looksNumeric (const char *s)
	tells wether this string looks like a numeric entity
bool	elementOf (const char *s, const char t)
void	compactRepresentationTranslate (char *t)
void	compactRepresentationShrinkNum (char *t)
void	compactRepresentationRangesAndOrdinals (char *t)
void	compactRepresentation (char *t)
	gives a (intermediate) code for a numeric string (starting with #)
int	numericType (const char *mot)
	assigns a code to a "word"
	PLEARN_IMPLEMENT_OBJECT (AdditiveNormalizationKernel,"Normalizes additively an underlying kernel with respect to a training set.","From a kernel K, defines a new kernel K' such that:\n"" K'(x,y) = K(x,y) - E[K(x,x_i)] - E[K(x_i,y)] + E[K(x_i,x_j)]\n""where the expectation is performed on the data set.\n""If the 'remove_bias' option is set, then the expectation will not\n""take into account terms of the form K(x_i,x_i).\n""If the 'double_centering' option is set, this kernel K' will be\n""multiplied by -1/2 (this turns a squared distance kernel into a\n""centered dot product kernel).\n")
	DECLARE_OBJECT_PTR (AdditiveNormalizationKernel)
	PLEARN_IMPLEMENT_OBJECT (ClassDistanceProportionCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ClassDistanceProportionCostFunction)
CostFunc	class_distance_proportion ()
	if outputs are neg distances to each class: dist_to_correct_class/(dist_to_correct_class+dist_to_closest_other_class)
	PLEARN_IMPLEMENT_OBJECT (ClassErrorCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ClassErrorCostFunction)
CostFunc	class_error (bool output_is_classnum=false)
	PLEARN_IMPLEMENT_OBJECT (ClassMarginCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ClassMarginCostFunction)
CostFunc	class_margin (bool binary_target_is_01=false, bool output_is_positive=false)
	difference between correct class score and max of other class' scores
	PLEARN_IMPLEMENT_OBJECT (CompactVMatrixGaussianKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (CompactVMatrixGaussianKernel)
	PLEARN_IMPLEMENT_OBJECT (CompactVMatrixPolynomialKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (CompactVMatrixPolynomialKernel)
	PLEARN_IMPLEMENT_OBJECT (ConvexBasisKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ConvexBasisKernel)
	PLEARN_IMPLEMENT_OBJECT (DifferenceKernel,"ONE LINE DESCR","NO HELP")
CostFunc	output_minus_target (int singleoutputindex)
	DECLARE_OBJECT_PTR (DifferenceKernel)
	PLEARN_IMPLEMENT_OBJECT (DirectNegativeCostFunction,"ONE LINE DESCR","NO HELP")
CostFunc	directnegative_costfunc ()
	PLEARN_IMPLEMENT_OBJECT (DistanceKernel,"ONE LINE DESCR","NO HELP")
CostFunc	absolute_deviation (int singleoutputindex)
	DECLARE_OBJECT_PTR (DistanceKernel)
	PLEARN_IMPLEMENT_OBJECT (DivisiveNormalizationKernel,"Divisive normalization of an underlying kernel.","From a positive kernel K, defines a new kernel K' such that:\n"" K'(x,y) = K(x,y) / sqrt(E[K(x,x_i)] . E[K(x_i,y)])\n""where the expectation is performed on the data set.\n""If the 'remove_bias' option is set, then the expectation will not\n""take into account terms of the form K(x_i,x_i).\n")
	DECLARE_OBJECT_PTR (DivisiveNormalizationKernel)
	PLEARN_IMPLEMENT_OBJECT (DotProductKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (DotProductKernel)
	PLEARN_IMPLEMENT_OBJECT (GaussianDensityKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (GaussianDensityKernel)
	PLEARN_IMPLEMENT_OBJECT (GaussianKernel,"The good old Gaussian kernel.","")
	DECLARE_OBJECT_PTR (GaussianKernel)
	PLEARN_IMPLEMENT_OBJECT (GeneralizedDistanceRBFKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (GeneralizedDistanceRBFKernel)
	PLEARN_IMPLEMENT_OBJECT (GeodesicDistanceKernel,"Computes the geodesic distance based on k nearest neighbors.","")
	DECLARE_OBJECT_PTR (GeodesicDistanceKernel)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (Kernel,"ONE LINE DESCR","NO HELP")
Mat	findClosestPairsOfDifferentClass (int k, VMat data, Ker dist)
	DECLARE_OBJECT_PTR (Kernel)
	DECLARE_OBJECT_PP (Ker, Kernel)
template<> void	deepCopyField (Ker &field, CopiesMap &copies)
Array< Ker >	operator & (const Ker &k1, const Ker &k2)
	******************** inline Ker operators
	PLEARN_IMPLEMENT_OBJECT (LaplacianKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (LaplacianKernel)
	PLEARN_IMPLEMENT_OBJECT (LiftBinaryCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (LiftBinaryCostFunction)
CostFunc	class_lift (bool make_positive=false)
	PLEARN_IMPLEMENT_OBJECT (LLEKernel,"The kernel used in Locally Linear Embedding.","This kernel is the (weighted) sum of two kernels K' and K'', such that:\n"" - K'(x_i, x_j) = \\delta_{ij}\n"" - K'(x_i, x) = K'(x, x_i) = w(x, x_i), where w(x, x_i) is the weight of\n"" x_i in the reconstruction of x by its knn nearest neighbors in the\n"" training set\n"" - K'(x, y) = 0\n"" - K''(x_i, x_j) = W_{ij} + W_{ji} - \\sum_k W_{ki} W{kj}, where W is the\n"" matrix of weights w(x_i, x_j)\n"" - K''(x, x_i) = K''(x_i, x) = 0\n""The weight of K' is given by the 'reconstruct_coeff' option: when this\n""weight tends to infinity, the mapping obtained is the same as the\n""out-of-sample extension proposed in (Saul and Roweis, 2002). To obtain\n""such a behavior, one should set 'reconstruct_coeff' to -1. This is the\n""default behavior, and it is suggested to keep it.\n")
	DECLARE_OBJECT_PTR (LLEKernel)
	PLEARN_IMPLEMENT_OBJECT (LogOfGaussianDensityKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (LogOfGaussianDensityKernel)
	PLEARN_IMPLEMENT_OBJECT (MulticlassErrorCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (MulticlassErrorCostFunction)
	PLEARN_IMPLEMENT_OBJECT (NegKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (NegKernel)
Ker	operator- (const Ker &k)
	PLEARN_IMPLEMENT_OBJECT (NegLogProbCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (NegLogProbCostFunction)
CostFunc	condprob_cost (bool normalize=false, bool smooth_map_outputs=false)
	negative log conditional probability
	PLEARN_IMPLEMENT_OBJECT (NegOutputCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (NegOutputCostFunction)
CostFunc	neg_output_costfunc ()
	returns -output[0]. This is for density estimators whose use(x) method typically computes log(p(x))
	PLEARN_IMPLEMENT_OBJECT (NormalizedDotProductKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (NormalizedDotProductKernel)
	PLEARN_IMPLEMENT_OBJECT (PolynomialKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (PolynomialKernel)
	PLEARN_IMPLEMENT_OBJECT (PowDistanceKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (PowDistanceKernel)
	PLEARN_IMPLEMENT_OBJECT (PrecomputedKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (PrecomputedKernel)
	PLEARN_IMPLEMENT_OBJECT (PricingTransactionPairProfitFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (PricingTransactionPairProfitFunction)
	PLEARN_IMPLEMENT_OBJECT (QuadraticUtilityCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (QuadraticUtilityCostFunction)
CostFunc	quadratic_risk (real risk_aversion, CostFunc profit_function)
	PLEARN_IMPLEMENT_OBJECT (ReconstructionWeightsKernel,"Computes the reconstruction weights of a point given its neighbors.","K(x, x_i) = the weight of x_i in the reconstruction of x by its knn\n""nearest neighbors. More precisely, we compute weights W_i such that\n""|| x - \\sum_j W_i x_i ||^2 is minimized, and K(x,x_i) = W_i.\n""If the second argument is not in the training set, K(x,y) will be 0.\n""In order not to compute K(x_i, x_j) = delta_{ij} when applied on\n""training points, one can set the 'ignore_nearest' option to 1 (or more),\n""which will ensure we do not use x_i itself in its reconstruction by its\n""nearest neighbors (however, the total number of neighbors computed,\n""including x_i itself, will always stay equal to knn).\n""Note that this is NOT a symmetric kernel!\n")
	DECLARE_OBJECT_PTR (ReconstructionWeightsKernel)
	PLEARN_IMPLEMENT_OBJECT (ScaledGaussianKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ScaledGaussianKernel)
	PLEARN_IMPLEMENT_OBJECT (ScaledGeneralizedDistanceRBFKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ScaledGeneralizedDistanceRBFKernel)
	PLEARN_IMPLEMENT_OBJECT (ScaledLaplacianKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ScaledLaplacianKernel)
	PLEARN_IMPLEMENT_OBJECT (SelectedOutputCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (SelectedOutputCostFunction)
	PLEARN_IMPLEMENT_OBJECT (SigmoidalKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (SigmoidalKernel)
	PLEARN_IMPLEMENT_OBJECT (SigmoidPrimitiveKernel,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (SigmoidPrimitiveKernel)
	PLEARN_IMPLEMENT_OBJECT (SourceKernel,"A kernel built upon an underlying source kernel","The default behavior of a SourceKernel is to forward all calls to the underlying\n""kernel. However, subclasses will probably want to override the methods to perform\n""more complex operations.")
	DECLARE_OBJECT_PTR (SourceKernel)
	PLEARN_IMPLEMENT_OBJECT (SquaredErrorCostFunction,"ONE LINE DESCR","NO HELP")
CostFunc	squared_error (int singleoutputindex)
	DECLARE_OBJECT_PTR (SquaredErrorCostFunction)
CostFunc	squared_classification_error (real hot_value=0.8, real cold_value=0.2)
	PLEARN_IMPLEMENT_OBJECT (WeightedCostFunction,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (WeightedCostFunction)
CostFunc	weighted_costfunc (CostFunc costfunc)
	reweighting
int	dnaupd_ (long int *, const char *, long int *, const char *, long int *, double *, double *, long int *, double *, long int *, long int *, long int *, double *, double *, long int *, long int *, short, short)
int	dneupd_ (long int *, const char *, long int *, double *, double *, double *, long int *, double *, double *, double *, const char *, long int *, const char *, long int *, double *, double *, long int *, double *, long int *, long int *, long int *, double *, double *, long int *, long int *, short, short, short)
int	snaupd_ (long int *, const char *, long int *, const char *, long int *, float *, float *, long int *, float *, long int *, long int *, long int *, float *, float *, long int *, long int *, short, short)
int	sneupd_ (long int *, const char *, long int *, float *, float *, float *, long int *, float *, float *, float *, const char *, long int *, const char *, long int *, float *, float *, long int *, float *, long int *, long int *, long int *, float *, float *, long int *, long int *, short, short, short)
int	dsaupd_ (long int *, const char *, long int *, const char *, long int *, double *, double *, long int *, double *, long int *, long int *, long int *, double *, double *, long int *, long int *, short, short)
int	dseupd_ (long int *, const char *, long int *, double *, double *, long int *, double *, const char *, long int *, const char *, long int *, double *, double *, long int *, double *, long int *, long int *, long int *, double *, double *, long int *, long int *, short, short, short)
int	ssaupd_ (long int *, const char *, long int *, const char *, long int *, float *, float *, long int *, float *, long int *, long int *, long int *, float *, float *, long int *, long int *, short, short)
int	sseupd_ (long int *, const char *, long int *, float *, float *, long int *, float *, const char *, long int *, const char *, long int *, float *, float *, long int *, float *, long int *, long int *, long int *, float *, float *, long int *, long int *, short, short, short)
	PLEARN_IMPLEMENT_OBJECT (Binner,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (Binner)
	PLEARN_IMPLEMENT_OBJECT (ConditionalCDFSmoother,"Smoother that combines a detailed prior curve with a rough input curve.\n","This smoother is meant to smooth conditional distribution functions, using\n""a high-resolution prior cdf provided as a HistogramDistribution. Its 'smooth'\n""function takes a lower-resolution curve and smooths it using the prior\n""to fill the gaps.")
	DECLARE_OBJECT_PTR (ConditionalCDFSmoother)
	PLEARN_IMPLEMENT_OBJECT (ConditionalStatsCollector,"ONE LINE DESCRIPTION","MULTI LINE\nHELP")
	DECLARE_OBJECT_PTR (ConditionalStatsCollector)
real	logOfCompactGaussian (const Vec &x, const Vec &mu, const Vec &eigenvalues, const Mat &eigenvectors, real gamma, bool add_gamma_to_eigenval)
	returns log P(x|gaussian) with a gaussian represented compactly by the first few eigenvalues and eigenvectors of its covariance matrix.
real	logOfNormal (const Vec &x, const Vec &mu, const Mat &C)
real	logPFittedGaussian (const Vec &x, const Mat &X, real lambda)
	Fits a gaussian to the points in X (computing its mean and covariance matrix, and adding lambda to the diagonal of that covariance matrix) Then calls logOfNormal to return log(p(x | the_gaussian)).
template<class T> PStream &	operator<< (PStream &out, const DoubleAccessSparseMatrix< T > &m)
template<class T> PStream &	operator>> (PStream &in, DoubleAccessSparseMatrix< T > &m)
void	ssyevx_ (char *JOBZ, char *RANGE, char *UPLO, int *N, float *A, int *LDA, float *VL, float *VU, int *IL, int *IU, float *ABSTOL, int *M, float *W, float *Z, int *LDZ, float *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
void	dsyevx_ (char *JOBZ, char *RANGE, char *UPLO, int *N, double *A, int *LDA, double *VL, double *VU, int *IL, int *IU, double *ABSTOL, int *M, double *W, double *Z, int *LDZ, double *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
void	ssyev_ (char *JOBZ, char *UPLO, int *N, float *A, int *LDA, float *W, float *WORK, int *LWORK, int *INFO)
void	dsyev_ (char *JOBZ, char *UPLO, int *N, double *A, int *LDA, double *W, double *WORK, int *LWORK, int *INFO)
void	sgetri_ (int *N, float *A, int *LDA, int *IPIV, float *WORK, int *LWORK, int *INFO)
void	dgetri_ (int *N, double *A, int *LDA, int *IPIV, double *WORK, int *LWORK, int *INFO)
void	sgetrf_ (int *M, int *N, float *A, int *LDA, int *IPIV, int *INFO)
void	dgetrf_ (int *M, int *N, double *A, int *LDA, int *IPIV, int *INFO)
void	sgesv_ (int *N, int *NRHS, float *A, int *LDA, int *IPIV, float *B, int *LDB, int *INFO)
void	dgesv_ (int *N, int *NRHS, double *A, int *LDA, int *IPIV, double *B, int *LDB, int *INFO)
void	sgesdd_ (char *JOBZ, int *M, int *N, float *A, int *LDA, float *S, float *U, int *LDU, float *VT, int *LDVT, float *WORK, int *LWORK, int *IWORK, int *INFO)
void	dgesdd_ (char *JOBZ, int *M, int *N, double *A, int *LDA, double *S, double *U, int *LDU, double *VT, int *LDVT, double *WORK, int *LWORK, int *IWORK, int *INFO)
void	ssyevr_ (char *JOBZ, char *RANGE, char *UPLO, int *N, float *A, int *LDA, float *VL, float *VU, int *IL, int *IU, float *ABSTOL, int *M, float *W, float *Z, int *LDZ, int *ISUPPZ, float *WORK, int *LWORK, int *IWORK, int *LIWORK, int *INFO)
void	dsyevr_ (char *JOBZ, char *RANGE, char *UPLO, int *N, double *A, int *LDA, double *VL, double *VU, int *IL, int *IU, double *ABSTOL, int *M, double *W, double *Z, int *LDZ, int *ISUPPZ, double *WORK, int *LWORK, int *IWORK, int *LIWORK, int *INFO)
void	ssygvx_ (int *ITYPE, char *JOBZ, char *RANGE, char *UPLO, int *N, float *A, int *LDA, float *B, int *LDB, float *VL, float *VU, int *IL, int *IU, float *ABSTOL, int *M, float *W, float *Z, int *LDZ, float *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
void	dsygvx_ (int *ITYPE, char *JOBZ, char *RANGE, char *UPLO, int *N, double *A, int *LDA, double *B, int *LDB, double *VL, double *VU, int *IL, int *IU, double *ABSTOL, int *M, double *W, double *Z, int *LDZ, double *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
	PLEARN_IMPLEMENT_OBJECT (LiftStatsCollector,"Computes the performance of a binary classifier","The following statistics can be requested out of getStat():\n""- LIFT = % of positive examples in the first n samples, divided by the % of positive examples in the whole database\n""- LIFT_MAX = best performance that could be achieved, if all positive examples were selected in the first n samples\n""(where n = lift_fraction * nsamples).\n""IMPORTANT: if you add more samples after you call finalize() (or get any of the statistics above), some samples may\n""be wrongly discarded and further statistics may be wrong\n\n""Here are the typical steps to follow to optimize the lift with a neural network:\n""- add a lift_output cost to cost_funcs (e.g. cost_funcs = [ \"stable_cross_entropy\" \"lift_output\"];)\n""- change the template_stats_collector of your PTester:\n"" template_stats_collector =\n"" LiftStatsCollector (\n"" output_column = \"lift_output\" ;\n"" opposite_lift = 1 ; # if you want to optimize the lift\n"" sign_trick = 1 ;\n"" )\n""- add the lift to its statnames:\n"" statnames = [ \"E[train.E[stable_cross_entropy]]\",\"E[test.E[stable_cross_entropy]]\",\n"" \"E[train.LIFT]\", \"E[test.LIFT]\" ]\n""- maybe also change which_cost in your HyperOptimize strategy.\n")
	DECLARE_OBJECT_PTR (LiftStatsCollector)
	PLEARN_IMPLEMENT_OBJECT (LimitedGaussianSmoother,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (LimitedGaussianSmoother)
	PLEARN_IMPLEMENT_OBJECT (ManualBinner,"Binner with predefined cut-points.","ManualBinner implements a Binner for which cutpoints are predefined. ""It's getBinning function doesn't have to look at the data; it simply ""builds a RealMapping from the supplied bin_positions.")
	DECLARE_OBJECT_PTR (ManualBinner)
Vec *	newVecArray (int n)
Vec *	newVecArray (int n, int the_length)
Mat *	newMatArray (int n)
Mat *	newMatArray (int n, int the_length, int the_width)
Mat *	newIndexedMatArray (int n, Mat &m, int indexcolumn)
Mat	operator^ (const Mat &m1, const Mat &m2)
Mat	unitmatrix (int n)
ostream &	operator<< (ostream &out, const Vec &v)
template<> void	deepCopyField (Vec &field, CopiesMap &copies)
template<> void	deepCopyField (Mat &field, CopiesMap &copies)
template<class MatT> int	eigenSparseSymmMat (MatT &A, Vec e_values, Mat e_vectors, long int &n_evalues, int max_n_iter=300, bool compute_vectors=true, bool largest_evalues=true, bool according_to_magnitude=true, bool both_ends=false)
template<class MatT> int	eigenSparseNonSymmMat (MatT &A, Vec e_values, Mat e_vectors, long int &n_evalues, int max_n_iter=300, bool compute_vectors=true, bool largest_evalues=true, bool according_to_magnitude=true, bool both_ends=false)
real	pl_gammln (real z)
	function gamma returns log(Gamma(z)), where Gamma(z) = ^infty t^{z-1}*e^{-t} dt
real	pl_dgammlndz (real z)
	d(pl_gammln(z))/dz derivate of pl_gammln(z)
real	pl_gser (real a, real x)
	returns the series value of the incomplete gamma function
real	pl_gcf (real a, real x)
	returns the continued fraction representation of the incomplete gamma function
real	pl_gammq (real a, real x)
	returns the incomplete gamma function Q(a,x) = 1 - P(a,x) it either uses the series or the continued fraction formula
real	pl_erf (real x)
	The error function.
real	gauss_01_cum (real x)
	For X ~ Normal(0,1), cumulative probability function P(X<x).
real	gauss_01_quantile (real q)
real	gauss_01_density (real x)
	for X ~ Normal(0,1), return density of X at x
real	gauss_01_log_density (real x)
real	gauss_log_density_var (real x, real mu, real var)
real	gauss_density_var (real x, real mu, real var)
real	gauss_log_density_stddev (real x, real mu, real sigma)
real	p_value (real mu, real vn)
real	normal_cdf (real x)
real	gauss_cum (real x, real mu, real sigma)
real	gauss_density_stddev (real x, real mu, real sigma)
real	safeflog (real a)
real	safeexp (real a)
real	log (real base, real a)
real	logtwo (real a)
real	safeflog (real base, real a)
real	safeflog2 (real a)
real	logadd (real log_a, real log_b)
	compute log(exp(log_a)+exp(log_b)) without losing too much precision
real	square_f (real x)
real	logsub (real log_a, real log_b)
	compute log(exp(log_a)-exp(log_b)) without losing too much precision
real	small_dilogarithm (real x)
real	positive_dilogarithm (real x)
real	dilogarithm (real x)
	return the dilogarithm function dilogarithm(x) = sum_{i=1}^{} x^i/i^2 = int_{z=x}^0 log(1-z)/z dz It is also useful because -dilogarithm(-exp(x)) is the primitive of the softplus function log(1+exp(x)).
real	hard_slope_integral (real l, real r, real a, real b)
real	soft_slope_integral (real smoothness, real left, real right, real a, real b)
real	tabulated_soft_slope_integral (real smoothness, real left, real right, real a, real b)
real	sign (real a)
real	positive (real a)
real	negative (real a)
template<class T> T	square (const T &x)
template<class T> T	two (const T &x)
real	fasttanh (const real &x)
real	fastsigmoid (const real &x)
real	ultrafasttanh (const real &x)
real	ultrafastsigmoid (const real &x)
template<class T> bool	is_missing (const T &x)
	Tells if the passed value means "missing" for its data-type.
bool	is_missing (double x)
	Missing value for double and float are represented by NaN.
bool	is_missing (float x)
	Missing value for double and float are represented by NaN.
bool	is_integer (real x)
real	FABS (real x)
real	mypow (real x, real p)
real	ipow (real x, int p)
real	sigmoid (real x)
	numerically stable version of sigmoid(x) = 1.0/(1.0+exp(-x))
real	is_positive (real x)
	"hard" version of the sigmoid, i.e.
real	inverse_sigmoid (real x)
	numerically stable version of inverse_sigmoid(x) = log(x/(1-x))
real	softplus (real x)
	numerically stable computation of log(1+exp(x))
real	tabulated_softplus (real x)
real	inverse_softplus (real y)
	inverse of softplus function
real	hard_slope (real x, real left=0, real right=1)
real	soft_slope (real x, real smoothness=1, real left=0, real right=1)
real	tabulated_soft_slope (real x, real smoothness=1, real left=0, real right=1)
real	d_soft_slope (real x, real smoothness=1, real left=0, real right=1)
int	n_choose (int M, int N)
	Return M choose N, i.e., M! / ( N! (M-N)! ).
real	softplus_primitive (real x)
real	tabulated_softplus_primitive (real x)
int	eigen_SymmMat (Mat &in, Vec &e_value, Mat &e_vector, int &n_evalues_found, bool compute_all, int nb_eigen, bool compute_vectors, bool largest_evalues)
int	eigen_SymmMat_decreasing (Mat &in, Vec &e_value, Mat &e_vector, int &n_evalues_found, bool compute_all, int nb_eigen, bool compute_vectors, bool largest_evalues)
	same as the previous call, but eigenvalues/vectors are sorted by largest firat (in decreasing order)
int	matInvert (Mat &in, Mat &inverse)
	This function compute the inverse of a matrix.
int	lapackSolveLinearSystem (Mat &At, Mat &Bt, TVec< int > &pivots)
void	solveLinearSystem (const Mat &A, const Mat &Y, Mat &X)
	for matrices A such that A.length() <= A.width(), find X s.t.
void	solveTransposeLinearSystem (const Mat &A, const Mat &Y, Mat &X)
	for matrices A such that A.length() >= A.width(), find X s.t.
Mat	solveLinearSystem (const Mat &A, const Mat &B)
Vec	solveLinearSystem (const Mat &A, const Vec &b)
	Returns solution x of Ax = b (same as above, except b and x are vectors).
Vec	constrainedLinearRegression (const Mat &Xt, const Vec &Y, real lambda)
Mat	multivariate_normal (const Vec &mu, const Mat &A, int N)
	generate N vectors sampled from the normal with mean vector mu and covariance matrix A
Vec	multivariate_normal (const Vec &mu, const Mat &A)
	generate a vector sampled from the normal with mean vector mu and covariance matrix A
Vec	multivariate_normal (const Vec &mu, const Vec &e_values, const Mat &e_vectors)
	generate 1 vector sampled from the normal with mean mu and covariance matrix A = evectors * diagonal(e_values) * evectors'
void	multivariate_normal (Vec &x, const Vec &mu, const Vec &e_values, const Mat &e_vectors, Vec &z)
	generate a vector x sampled from the normal with mean mu and covariance matrix A = evectors * diagonal(e_values) * evectors' (the normal(0,I) originally sampled to obtain x is stored in z).
void	affineNormalization (Mat data, Mat W, Vec bias, real regularizer)
void	lapack_Xsyevx_ (char *JOBZ, char *RANGE, char *UPLO, int *N, double *A, int *LDA, double *VL, double *VU, int *IL, int *IU, double *ABSTOL, int *M, double *W, double *Z, int *LDZ, double *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
void	lapack_Xsyevx_ (char *JOBZ, char *RANGE, char *UPLO, int *N, float *A, int *LDA, float *VL, float *VU, int *IL, int *IU, float *ABSTOL, int *M, float *W, float *Z, int *LDZ, float *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
void	lapack_Xgesdd_ (char *JOBZ, int *M, int *N, double *A, int *LDA, double *S, double *U, int *LDU, double *VT, int *LDVT, double *WORK, int *LWORK, int *IWORK, int *INFO)
void	lapack_Xgesdd_ (char *JOBZ, int *M, int *N, float *A, int *LDA, float *S, float *U, int *LDU, float *VT, int *LDVT, float *WORK, int *LWORK, int *IWORK, int *INFO)
void	lapack_Xsyevr_ (char *JOBZ, char *RANGE, char *UPLO, int *N, float *A, int *LDA, float *VL, float *VU, int *IL, int *IU, float *ABSTOL, int *M, float *W, float *Z, int *LDZ, int *ISUPPZ, float *WORK, int *LWORK, int *IWORK, int *LIWORK, int *INFO)
void	lapack_Xsyevr_ (char *JOBZ, char *RANGE, char *UPLO, int *N, double *A, int *LDA, double *VL, double *VU, int *IL, int *IU, double *ABSTOL, int *M, double *W, double *Z, int *LDZ, int *ISUPPZ, double *WORK, int *LWORK, int *IWORK, int *LIWORK, int *INFO)
void	lapack_Xsygvx_ (int *ITYPE, char *JOBZ, char *RANGE, char *UPLO, int *N, double *A, int *LDA, double *B, int *LDB, double *VL, double *VU, int *IL, int *IU, double *ABSTOL, int *M, double *W, double *Z, int *LDZ, double *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
void	lapack_Xsygvx_ (int *ITYPE, char *JOBZ, char *RANGE, char *UPLO, int *N, float *A, int *LDA, float *B, int *LDB, float *VL, float *VU, int *IL, int *IU, float *ABSTOL, int *M, float *W, float *Z, int *LDZ, float *WORK, int *LWORK, int *IWORK, int *IFAIL, int *INFO)
template<class num_t> void	lapackEIGEN (const TMat< num_t > &A, TVec< num_t > &eigenvals, TMat< num_t > &eigenvecs, char RANGE='A', num_t low=0, num_t high=0, num_t ABSTOL=0)
	Computes the eigenvalues and eigenvectors of a symmetric (NxN) matrix A.
template<class num_t> void	lapackGeneralizedEIGEN (const TMat< num_t > &A, const TMat< num_t > &B, int ITYPE, TVec< num_t > &eigenvals, TMat< num_t > &eigenvecs, char RANGE='A', num_t low=0, num_t high=0, num_t ABSTOL=0)
	Computes the eigenvalues and eigenvectors of a real generalized symmetric-definite eigenproblem, of the form A*x=(lambda)*B*x, A*Bx=(lambda)*x, or B*A*x=(lambda)*x A and B are assumed to be symmetric and B is also positive definite.
template<class num_t> void	eigenVecOfSymmMat (TMat< num_t > &m, int k, TVec< num_t > &eigen_values, TMat< num_t > &eigen_vectors)
	Computes up to k largest eigen_values and corresponding eigen_vectors of symmetric matrix m.
template<class num_t> void	generalizedEigenVecOfSymmMat (TMat< num_t > &m1, TMat< num_t > &m2, int itype, int k, TVec< num_t > &eigen_values, TMat< num_t > &eigen_vectors)
	Computes up to k largest eigen_values and corresponding eigen_vectors of a real generalized symmetric-definite eigenproblem, of the form m1*x=(lambda)*m2*x (itype = 1), m1*m2*x=(lambda)*x (itype = 2) or m2*m1*x=(lambda)*x (itype = 3) m1 and m2 are assumed to be symmetric and m2 is also positive definite.
template<class num_t> void	lapackSVD (const TMat< num_t > &At, TMat< num_t > &Ut, TVec< num_t > &S, TMat< num_t > &V, char JOBZ='A', real safeguard=1)
template<class num_t> void	SVD (const TMat< num_t > &A, TMat< num_t > &U, TVec< num_t > &S, TMat< num_t > &Vt, char JOBZ='A', real safeguard=1)
	Performs the SVD decomposition A = U.S.Vt Where U and Vt are orthonormal matrices.
Vec	closestPointOnHyperplane (const Vec &x, const Mat &points, real weight_decay=0.)
	closest point to x on hyperplane that passes through all points (with weight decay)
real	hyperplaneDistance (const Vec &x, const Mat &points, real weight_decay=0.)
	Distance between point x and closest point on hyperplane that passes through all points.
template<class MatT> void	diagonalizeSubspace (MatT &A, Mat &X, Vec &Ax, Mat &solutions, Vec &evalues, Mat &evectors)
template<class T> PStream &	operator<< (PStream &out, const ProbSparseMatrix &p)
template<class T> PStream &	operator>> (PStream &in, ProbSparseMatrix &p)
real	log_gamma (real xx)
real	log_beta (real x, real y)
real	incomplete_beta_continued_fraction (real z, real x, real y)
real	incomplete_beta (real z, real x, real y)
real	student_t_cdf (real t, int nb_degrees_of_freedom)
void	manual_seed (long x)
void	seed ()
long	get_seed ()
real	uniform_sample ()
real	bounded_uniform (real a, real b)
real	expdev ()
real	gaussian_01 ()
real	gaussian_mu_sigma (real mu, real sigma)
real	gaussian_mixture_mu_sigma (Vec &w, const Vec &mu, const Vec &sigma)
real	gamdev (int ia)
real	poidev (real xm)
real	bnldev (real pp, int n)
int	multinomial_sample (const Vec &distribution)
int	uniform_multinomial_sample (int N)
	return an integer between 0 and N-1 with equal probabilities
void	fill_random_uniform (const Vec &dest, real minval, real maxval)
	sample each element from uniform distribution U[minval,maxval]
void	fill_random_discrete (const Vec &dest, const Vec &set)
	sample each element from the given set
void	fill_random_normal (const Vec &dest, real mean, real stdev)
	sample each element from Normal(mean,sdev^2) distribution
void	fill_random_normal (const Vec &dest, const Vec &mean, const Vec &stdev)
	sample each element from multivariate Normal(mean,diag(sdev^2)) distribution
void	fill_random_uniform (const Mat &dest, real minval, real maxval)
void	fill_random_normal (const Mat &dest, real mean, real sdev)
double	incbcf (double a, double b, double x)
double	incbd (double a, double b, double x)
real	normal_sample ()
real	binomial_sample (real prob1)
	alias
template<class T> void	bootstrap_rows (const TMat< T > &source, TMat< T > destination)
	sample with replacement the rows of source and put them in destination.
template<class T> void	shuffleElements (const TVec< T > &vec)
	randomly shuffle the entries of the TVector
template<class T> void	shuffleRows (const TMat< T > &mat)
template<class T> void	computeRanks (const TMat< T > &mat, TMat< T > &ranks)
	For each column of mat, sort the elements and put in the 'ranks' matrix (of the same dimensions) the rank of original elements.
template<class T> void	product (RowMapSparseMatrix< T > &M, const Vec &x, Vec &y)
	PLEARN_IMPLEMENT_OBJECT (ScaledConditionalCDFSmoother,"This smoothes a low-resolution histogram using as prior a high-resolution one.","This class takes as 'prior_cdf' a detailed histogram (usually derived from\n""an unconditional distribution) and uses it to smooth a given survival\n""function and provide extra detail (high resolution).\n""Two smoothing formula are provided, both of which guarantee that the smoothed\n""survival function takes the same value as the raw one at or near original bin\n""positions. In between the original bin positions, the smoothed survival\n""is obtained by applying one of two possible formula, according to the\n""preserve_relative_density option.\n")
	DECLARE_OBJECT_PTR (ScaledConditionalCDFSmoother)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (Smoother,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (Smoother)
SparseMatrix	operator+ (const SparseMatrix &A, const SparseMatrix &B)
	add two sparse matrices (of same dimensions but with values in possibly different places)
SparseMatrix	add (Array< SparseMatrix > &matrices)
	add a bunch of sparse matrices and return result
void	SpearmanRankCorrelation (const VMat &x, const VMat &y, Mat &r)
	Compute the Spearman Rank correlation statistic.
real	testNoCorrelationAsymptotically (real r, int n)
	Return P(|R|>|r|) two-sided p-value for the null-hypothesis that there is no monotonic dependency, with r the observed correlation between two paired samples of length n.
void	testSpearmanRankCorrelationPValues (const VMat &x, const VMat &y, Mat &pvalues)
	Compute P(|R|>|r|) two-sided p-value for the null-hypothesis that there is no monotonic dependency, with r the observed Spearman Rank correlation between two paired samples x and y of length n (column matrices).
void	testSpearmanRankCorrelation (const VMat &x, const VMat &y, Mat &r, Mat &pvalues)
	same as above but return also in r the rank correlations
real	max_cdf_diff (Vec &v1, Vec &v2)
	Returns the max of the difference between the empirical cdf of 2 series of values Side-effect: the call sorts v1 and v2.
real	KS_test (real D, real N, int conv)
void	KS_test (Vec &v1, Vec &v2, int conv, real &D, real &p_value)
	Kolmogorov-Smirnov test.
real	KS_test (Vec &v1, Vec &v2, int conv)
	Returns result of Kolmogorov-Smirnov test between 2 samples The call sorts v1 and v2.
real	paired_t_test (Vec u, Vec v)
	Given two paired sets u and v of n measured values, the paired t-test determines whether they differ from each other in a significant way under the assumptions that the paired differences are independent and identically normally distributed.
	PLEARN_IMPLEMENT_OBJECT (StatsCollector,"Collects basic statistics","A StatsCollector allows to compute basic global statistics for a series of numbers,\n""as well as statistics within automatically determined ranges.\n""The first maxnvalues encountered values will be used as reference points to define\n""the ranges, so to get reasonable results, your sequence should be iid, and NOT sorted!\n""\n""The following statistics are available:"" - E Sample mean\n"" - V Sample variance\n"" - STDDEV Sample standard deviation\n"" - STDERROR Standard error of the mean\n"" - MIN Minimum value\n"" - MAX Maximum value\n"" - SUM Sum of observations \n"" - SUMSQ Sum of squares\n"" - FIRST First observation\n"" - LAST Last observation\n"" - N Total number of observations\n"" - NMISSING Number of missing observations\n"" - NNONMISSING Number of non-missing observations\n"" - SHARPERATIO Mean divided by standard deviation\n")
int	sortIdComparator (const void *i1, const void *i2)
TVec< RealMapping >	computeRanges (TVec< StatsCollector > stats, int discrete_mincount, int continuous_mincount)
PStream &	operator>> (PStream &in, StatsCollectorCounts &c)
	this class holds simple statistics about a field
PStream &	operator<< (PStream &out, const StatsCollectorCounts &c)
	DECLARE_OBJECT_PTR (StatsCollector)
template<> void	deepCopyField (StatsCollector &field, CopiesMap &copies)
	Apparently needed to specialize this method, otherwise it was the generic deepCopyField from CopiesMap.h that was called when deep copying a TVec<StatsCollector>.
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (StatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (MeanStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (ExpMeanStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (StddevStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (StderrStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (SharpeRatioStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (MinStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (MaxStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (LiftStatsIterator,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (QuantilesStatsIterator,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (StatsIterator)
	DECLARE_OBJECT_PTR (MeanStatsIterator)
	DECLARE_OBJECT_PTR (ExpMeanStatsIterator)
	DECLARE_OBJECT_PTR (StddevStatsIterator)
	DECLARE_OBJECT_PTR (StderrStatsIterator)
	DECLARE_OBJECT_PTR (SharpeRatioStatsIterator)
	DECLARE_OBJECT_PTR (MinStatsIterator)
	DECLARE_OBJECT_PTR (MaxStatsIterator)
	DECLARE_OBJECT_PTR (LiftStatsIterator)
	DECLARE_OBJECT_PTR (QuantilesStatsIterator)
	DECLARE_TYPE_TRAITS (StatsItArray)
PStream &	operator>> (PStream &in, StatsItArray &o)
PStream &	operator<< (PStream &out, const StatsItArray &o)
template<> void	deepCopyField (StatsItArray &field, CopiesMap &copies)
StatsItArray	operator & (const StatsIt &statsit1, const StatsIt &statsit2)
StatsIt	mean_stats ()
StatsIt	stddev_stats ()
StatsIt	stderr_stats ()
StatsIt	min_stats ()
StatsIt	max_stats ()
StatsIt	quantiles_stats (Vec quantiles, int n_data=1000)
StatsIt	lift_stats (int the_index=0, real the_fraction=0.1)
StatsIt	sharpe_ratio_stats ()
	exponential of the mean
StatsIt	exp_mean_stats ()
template<class T, class I> void	selectElements (const TVec< T > &source, const TVec< I > &indices, TVec< T > &destination)
template<class T> void	elementsEqualTo (const TVec< T > &source, const T &value, const TVec< T > &destination)
	put in destination 1's when (*this)[i]==value, 0 otherwise
template<class T> TVec< T >	concat (const TVec< T > &v1, const TVec< T > &v2)
template<class T> TVec< T >	removeElement (const TVec< T > &v, int elemnum)
	if the element to remove is the first or the last one, then a submatrix (a view) of m will be returned (for efficiency) otherwise, it is a fresh copy with the element removed.
template<class T, class I> void	selectRows (const TMat< T > &source, const TVec< I > &row_indices, TMat< T > &destination)
template<class T, class I> void	selectColumns (const TMat< T > &source, const TVec< I > &column_indices, TMat< T > &destination)
template<class T, class I> void	select (const TMat< T > &source, const TVec< I > &row_indices, const TVec< I > &column_indices, TMat< T > &destination)
template<class T> TMat< T >	removeRow (const TMat< T > &m, int rownum)
	returns a new mat which is m with the given row removed if the row to remove is the first or the last one, then a submatrix (a view) of m will be returned (for efficiency) otherwise, it is a fresh copy with the row removed.
template<class T> TMat< T >	removeColumn (const TMat< T > &m, int colnum)
	returns a new mat which is m with the given column removed if the column to remove is the first or the last one, then a submatrix (a view) of m will be returned (for efficiency) otherwise, it is a fresh copy with the column removed.
template<class T> TMat< T >	diagonalmatrix (const TVec< T > &v)
template<class T> TMat< T >	deepCopy (const TMat< T > source)
template<class T> TMat< T >	deepCopy (const TMat< T > source, CopiesMap copies)
template<class T> void	deepCopyField (TMat< T > &field, CopiesMap &copies)
template<class T> void	clear (const TMat< T > &x)
template<class T> void	swap (TMat< T > &a, TMat< T > &b)
template<class T> void	operator<< (const TMat< T > &m1, const TMat< T > &m2)
	copy TMat << TMat
template<class T, class U> void	operator<< (const TMat< T > &m1, const TMat< U > &m2)
	copy TMat << TMat (different types)
template<class T> void	operator<< (const TMat< T > &m1, const TVec< T > &m2)
	copy TMat << Tvec
template<class T, class U> void	operator<< (const TMat< T > &m1, const TVec< U > &m2)
	copy TMat << Tvec (different types)
template<class T> void	operator<< (const TVec< T > &m1, const TMat< T > &m2)
	copy TVec << TMat
template<class T, class U> void	operator<< (const TVec< T > &m1, const TMat< U > &m2)
	copy TVec << TMat (different types)
template<class T, class U> void	operator>> (const TMat< T > &m1, const TMat< U > &m2)
	copy TMat >> TMat
template<class T, class U> void	operator>> (const TVec< T > &m1, const TMat< U > &m2)
	copy TVec >> TMat
template<class T, class U> void	operator>> (const TMat< T > &m1, const TVec< U > &m2)
	copy TMat >> Tvec
template<class T> ostream &	operator<< (ostream &out, const TMat< T > &m)
	printing a TMat
template<class T> istream &	operator>> (istream &in, const TMat< T > &m)
	inputing a TMat
template<class T> TMat< T >	rowmatrix (const TVec< T > &v)
	returns a view of this vector as a single row matrix
template<class T> TMat< T >	columnmatrix (const TVec< T > &v)
	returns a view of this vector as a single column matrix
template<class T> void	select (const TMat< T > &source, const TVec< T > &row_indices, const TVec< T > &column_indices, TMat< T > &destination)
template<class T> void	savePMat (const string &filename, const TMat< T > &mat)
template<class T> PStream &	operator<< (PStream &out, const TMat< T > &m)
	Read and Write from C++ stream: write saves length and read resizes accordingly (the raw modes don't write any size information).
template<class T> PStream &	operator>> (PStream &in, TMat< T > &m)
string	join (const TVec< string > &s, const string &separator)
template<class T> T	max (const TVec< T > &vec)
template<class T> void	softmax (const TVec< T > &x, const TVec< T > &y)
	y = softmax(x)
template<class T> void	log_softmax (const TVec< T > &x, TVec< T > &y)
template<class T> void	exp (const TVec< T > &x, TVec< T > &y)
	computes y <- exp(x)
template<class T> T	sumsquare (const TVec< T > &x)
	returns the sum of squared elements
template<class T> T	sumabs (const TVec< T > &x)
	returns the sum of absolute values of elements
template<class T> void	squareElements (const TVec< T > &x)
	squares the elements of x in place
template<class T> void	squareElements (const TMat< T > &m)
	squares the elements of m in place
template<class T> T	sumsquare (const TMat< T > &m)
	returns the sum of squared elements
template<class T> T	sumabs (const TMat< T > &m)
	returns the sum of absolute value of the elements
template<class T> void	multiply (const TVec< T > &source1, T source2, TVec< T > &destination)
	destination = source1*source2
template<class T> T	sum (const TVec< T > &vec, bool ignore_missing=false)
template<class T> T	sum_of_log (const TVec< T > &vec)
	returns the sum of the log of the elements (this is also the log of the product of the elements but is more stable if you have very small elements).
template<class T> T	product (const TVec< T > &vec)
template<class T> T	mean (const TVec< T > &vec, bool ignore_missing=false)
	if ignore_missing==true, then the mean is computed by ignoring the possible MISSING_VALUE in the Vec.
template<class T> T	harmonic_mean (const TVec< T > &vec, bool ignore_missing=false)
template<class T> T	avgdev (const TVec< T > &vec, T meanval)
template<class T> T	geometric_mean (const TVec< T > &vec)
template<class T> T	weighted_mean (const TVec< T > &vec, const TVec< T > &weights, bool ignore_missing=false)
template<class T> T	variance (const TVec< T > &vec, T meanval)
template<class T> T	covariance (const TVec< T > &vec1, const TVec< T > &vec2, T mean1, T mean2)
template<class T> T	weighted_variance (const TVec< T > &vec, const TVec< T > &weights, T no_weighted_mean, T weighted_mean)
template<class T> TVec< T >	histogram (const TVec< T > &vec, T minval, T maxval, int nbins)
template<class T> T	min (const TVec< T > &vec)
template<class T> T	maxabs (const TVec< T > &vec)
template<class T> T	minabs (const TVec< T > &vec, int index=int())
template<class T> int	argmax (const TVec< T > &vec)
template<class T> int	argmax (const TVec< T > &vec, bool ignore_missing)
template<class T> int	argmin (const TVec< T > &vec)
template<class T> int	argmin (const TVec< T > &vec, bool ignore_missing)
template<class T> T	pownorm (const TVec< T > &vec, double n)
template<class T> T	pownorm (const TVec< T > &vec)
template<class T> T	norm (const TVec< T > &vec, double n)
template<class T> T	norm (const TVec< T > &vec)
template<class T> void	normalize (const TVec< T > &vec, double n)
template<class T> T	powdistance (const TVec< T > &vec1, const TVec< T > &vec2, double n)
template<class T> T	powdistance (const TVec< T > &vec1, const TVec< T > &vec2)
template<class T> T	dist (const TVec< T > &vec1, const TVec< T > &vec2, double n)
template<class T> T	L2distance (const TVec< T > &vec1, const TVec< T > &vec2)
template<class T> T	L1distance (const TVec< T > &vec1, const TVec< T > &vec2)
template<class T> T	weighted_powdistance (const TVec< T > &vec1, const TVec< T > &vec2, double n, const TVec< T > &weights)
template<class T> T	weighted_distance (const TVec< T > &vec1, const TVec< T > &vec2, double n, const TVec< T > &weights)
template<class T> void	operator-= (const TVec< T > &vec1, const TVec< T > &vec2)
template<class T> void	operator *= (const TVec< T > &vec1, const TVec< T > &vec2)
template<class T> void	operator/= (const TVec< T > &vec, T scalar)
template<class T> void	operator/= (const TVec< T > &vec, int scalar)
template<class T> void	compute_log (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	log (const TVec< T > &src)
template<class T> void	compute_sqrt (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	sqrt (const TVec< T > &src)
template<class T> void	compute_safelog (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	safelog (const TVec< T > &src)
template<class T> void	operator/= (const TVec< T > &vec1, const TVec< T > &vec2)
template<class T> void	compute_tanh (const TVec< T > &src, const TVec< T > &dest)
template<class T> void	bprop_tanh (const TVec< T > &tanh_x, const TVec< T > &d_tanh_x, TVec< T > &d_x)
template<class T> TVec< T >	tanh (const TVec< T > &src)
template<class T> void	compute_fasttanh (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	fasttanh (const TVec< T > &src)
template<class T> void	compute_sigmoid (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	sigmoid (const TVec< T > &src)
template<class T> void	compute_fastsigmoid (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	fastsigmoid (const TVec< T > &src)
template<class T> void	compute_inverse_sigmoid (const TVec< T > &src, const TVec< T > &dest)
template<class T> TVec< T >	inverse_sigmoid (const TVec< T > &src)
template<class T> void	negateElements (const TVec< T > &vec)
template<class T> void	invertElements (const TVec< T > &vec)
template<class T> TVec< T >	inverted (const TVec< T > &vec)
template<class T> void	operator+= (const TVec< T > &vec, T scalar)
template<class T> void	operator-= (const TVec< T > &vec, T scalar)
template<class T> TVec< T >	operator- (TVec< T > vec)
template<class T> T	dot (const TVec< T > &vec1, const TVec< T > &vec2)
template<class V, class T, class U> V	dot (const TVec< T > &vec1, const TVec< U > &vec2)
	Special dot product that allows TVec's of different types, as long as operator*(T,U) is defined.
template<class T> T	dot (const TMat< T > &m1, const TMat< T > &m2)
template<class T> TVec< T >	operator- (const TVec< T > &v1, const TVec< T > &v2)
template<class T> TVec< T >	operator- (T v1, const TVec< T > &v2)
template<class T> TVec< T >	operator- (const TVec< T > &v1, T v2)
template<class T> TVec< T >	operator+ (const TVec< T > &v1, const TVec< T > &v2)
template<class T> TVec< T >	operator+ (T v1, const TVec< T > &v2)
template<class T> TVec< T >	operator+ (const TVec< T > &v1, T v2)
template<class T> TVec< T >	operator% (const TVec< T > &v1, const TVec< T > &v2)
template<class T> TVec< T >	operator * (T scalar, const TVec< T > &v)
template<class T> TVec< T >	operator * (const TVec< T > &v1, T v2)
template<class T> TVec< T >	operator/ (const TVec< T > &v1, const TVec< T > &v2)
template<class T> TVec< T >	operator/ (T v1, const TVec< T > &v2)
template<class T1, class T2> TVec< T1 >	operator/ (const TVec< T1 > &v1, T2 scalar)
template<class T> T	logadd (const TVec< T > &vec)
template<class T> T	output_margin (const TVec< T > &class_scores, int correct_class)
template<class T> void	fill_one_hot (const TVec< T > &vec, int hotpos, T coldvalue, T hotvalue)
template<class T> TVec< T >	one_hot (int length, int hotpos, T coldvalue, T hotvalue)
template<class T> TVec< T >	square (const TVec< T > &vec)
template<class T> TVec< T >	squareroot (const TVec< T > &vec)
template<class T> TVec< T >	remove_missing (const TVec< T > &vec)
template<class T, class U, class V> TVec< U >	apply (const TVec< T > &vec, U(*func)(V))
	Transform a vector of T into a vector of U through a unary function.
template<class T, class U> void	apply (const TVec< T > &source, TVec< U > &destination, U(*func)(T))
	Transform a vector of T into a vector of U through a unary function.
template<class T, class U, class V> void	apply (const TVec< T > &src1, const TVec< U > &src2, TVec< V > &dest, V(*func)(T, U))
	Transform a vector of T and a vector of U into a vector of V, through a binary function.
template<class T> void	multiply (const TVec< T > &source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	multiplyAdd (const TVec< T > &source1, const TVec< T > &source2, T source3, TVec< T > &destination)
template<class T> void	multiplyScaledAdd (const TVec< T > &source, T a, T b, TVec< T > &destination)
template<class T> void	add (const TVec< T > &source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	add (const TVec< T > &source1, T source2, TVec< T > &destination)
template<class T> void	substract (const TVec< T > &source1, T source2, TVec< T > &destination)
template<class T> void	substract (const TVec< T > &source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	divide (const TVec< T > &source1, T source2, TVec< T > &destination)
template<class T> void	divide (const TVec< T > &source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	divide (T source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	max (const TVec< T > &source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	max (const TVec< T > &source1, T source2, TVec< T > &destination)
template<class T> void	min (const TVec< T > &source1, const TVec< T > &source2, TVec< T > &destination)
template<class T> void	min (const TVec< T > &source1, T source2, TVec< T > &destination)
template<class T> TVec< T >	softmax (const TVec< T > &x)
template<class T> void	tanh (const TVec< T > &x, TVec< T > &y)
template<class T> TVec< T >	exp (TVec< T > vec)
template<class T> TVec< T >	nonZeroIndices (TVec< T > v)
template<class T> TVec< T >	nonZeroIndices (TVec< bool > v)
template<class T> void	complement_indices (TVec< T > &indices, int n, TVec< T > &complement_indices, TVec< T > &buffer)
template<class T> void	equals (const TVec< T > &src, T v, TVec< T > &dest)
template<class T> void	isLargerThan (const TVec< T > &first, const TVec< T > &second, TVec< T > &dest)
template<class T> void	isLargerThanOrEqualTo (const TVec< T > &first, const TVec< T > &second, TVec< T > &dest)
template<class T> void	isSmallerThan (const TVec< T > &first, const TVec< T > &second, TVec< T > &dest)
template<class T> void	isSmallerThanOrEqualTo (const TVec< T > &first, const TVec< T > &second, TVec< T > &dest)
template<class T> void	ifThenElse (const TVec< T > &if_vec, const TVec< T > &then_vec, const TVec< T > &else_vec, TVec< T > &dest)
template<class T> int	vec_counts (const TVec< T > &src, T value)
template<class T> int	vec_find (const TVec< T > &src, T f)
template<class T> T	estimatedCumProb (T x, TVec< T > bins)
template<class T> int	positionOfkthOrderedElement (const TVec< T > &vec, int k)
template<class T> T	kthOrderedElement (const TVec< T > &vec, int k)
	returns the value of the kth ordered element of v k can take values 0 to vec.length()-1
template<class T> T	median (const TVec< T > &vec)
	returns the median value of vec
template<class T> T	selectAndOrder (const TVec< T > &vec, int pos)
	find the element at position pos that would result from a sort and put all elements (not in order!) lower than v[pos] in v[i<pos].
template<class T> TVec< T >	getQuantiles (const TVec< T > &vec, int q)
template<class T> TVec< T >	nonZero (const TVec< T > &vec)
	returns a vector composed of the values of v that are different from 0;
template<class T> TVec< T >	positiveValues (const TVec< T > &vec)
	returns a vector composed of the values of v that are greater than 0;
template<class T> int	positionOfClosestElement (const TVec< T > &vec, const T &value, bool is_sorted_vec=false)
template<class T> void	projectOnOrthogonalSubspace (const TVec< T > &vec, const TMat< T > &orthonormal_subspace)
template<class T> void	operator *= (const TVec< T > &vec, T factor)
template<class T> void	operator+= (const TVec< T > &vec1, const TVec< T > &vec2)
	element-wise +
template<class T> void	multiplyAcc (const TVec< T > &vec, const TVec< T > &x, T scale)
	vec[i] += x[i]*scale;
template<class T> void	exponentialMovingAverageUpdate (const TVec< T > &vec, const TVec< T > &x, T alpha)
	TVec[i] = (1-alpha)*TVec[i]+x[i]*alpha;.
template<class T> void	exponentialMovingVarianceUpdate (const TVec< T > &vec, const TVec< T > &x, const TVec< T > &mu, T alpha)
	TVec[i] = (1-alpha)*TVec[i]+(x[i]-mu[i])^2*alpha;.
template<class T> void	exponentialMovingSquareUpdate (const TVec< T > &vec, const TVec< T > &x, T alpha)
	TVec[i] = (1-alpha)*TVec[i]+x[i]^2*alpha;.
template<class T> void	multiplyAcc (const TVec< T > &vec, const TVec< T > &x, const TVec< T > &y)
	vec[i] += x[i]*y[i];
template<class T> void	squareMultiplyAcc (const TVec< T > &vec, const TVec< T > &x, T scale)
	TVec[i] += x[i]*x[i]*scale;.
template<class T> void	squareAcc (const TVec< T > &vec, const TVec< T > &x)
	TVec[i] += x[i]*x[i];.
template<class T> void	squareSubtract (const TVec< T > &vec, const TVec< T > &x)
	Tvec[i] -= x[i]*x[i];.
template<class T> void	diffSquareMultiplyAcc (const TVec< T > &vec, const TVec< T > &x, const TVec< T > &y, T scale)
	TVec[i] += (x[i]-y[i])^2*scale;.
template<class T> void	diffSquareMultiplyScaledAcc (const TVec< T > &vec, const TVec< T > &x, const TVec< T > &y, T fact1, T fact2)
	TVec[i] = TVec[i]*fact1 + (x[i]-y[i])^2*fact2;.
template<class T> void	product (const TVec< T > &result, const TMat< T > &m, const TVec< T > &v)
	result[i] = sum_j m[i,j] * v[j]
template<class T> void	productAcc (const TVec< T > &vec, const TMat< T > &m, const TVec< T > &v)
	result[i] += sum_j m[i,j] * v[j]
template<class T> void	transposeProduct (const TVec< T > &result, const TMat< T > &m, const TVec< T > &v)
	result[i] = sum_j m[j,i] * v[j] Equivalently: rowvec(result) = rowvec(v) .
template<class T> void	transposeProductAcc (const TVec< T > &result, const TMat< T > &m, const TVec< T > &v)
	result[i] += sum_j m[j,i] * v[j]
template<class T> void	transposeProductAcc (const TVec< T > &result, const TMat< T > &m, const TVec< T > &v, T alpha)
	result[i] += alpha * sum_j m[j,i] * v[j]
template<class T> void	compressedTransposeProductAcc (const TVec< T > &result, const TMat< T > &m, char *comprbufvec)
template<class T> void	diagonalizedFactorsProduct (TMat< T > &result, const TMat< T > &U, const TVec< T > d, const TMat< T > V, bool accumulate=false)
	return the matrix with elements (i,j) = sum_k U_{ik} d_k V_{kj}
template<class T> void	diagonalizedFactorsProductBprop (const TMat< T > &dCdresult, const TMat< T > &U, const TVec< T > d, const TMat< T > V, TMat< T > &dCdU, TVec< T > &dCdd, TMat< T > &dCdV)
	GIVEN that res(i,j) = sum_k U_{ik} d_k V_{kj}, and given dC/dres, U,d and V, accumulate gradients on dC/dU, dC/dd and dC/dV: dC/dU[i,k] += sum_j dC/dres[i,j] d_k V[k,j] dC/dd[k] += sum_{ij} dC/dres[i,j] U[i,k] V[k,j] dC/dV[k,j] += d_k * sum_i U[i,k] dC/dres[i,j].
template<class T> void	diagonalizedFactorsProductTranspose (TMat< T > &result, const TMat< T > &U, const TVec< T > d, const TMat< T > V, bool accumulate=false)
	return the matrix with elements (i,j) = sum_k U_{ik} d_k V_{jk}
template<class T> void	diagonalizedFactorsProductTransposeBprop (const TMat< T > &dCdresult, const TMat< T > &U, const TVec< T > d, const TMat< T > V, TMat< T > &dCdU, TVec< T > &dCdd, TMat< T > &dCdV)
template<class T> void	diagonalizedFactorsTransposeProduct (TMat< T > &result, const TMat< T > &U, const TVec< T > d, const TMat< T > V, bool accumulate=false)
	return the matrix with elements (i,j) = sum_k U_{ki} d_k V_{kj}
template<class T> void	diagonalizedFactorsTransposeProductBprop (const TMat< T > &dCdresult, const TMat< T > &U, const TVec< T > d, const TMat< T > V, TMat< T > &dCdU, TVec< T > &dCdd, TMat< T > &dCdV)
template<class T> void	diagonalizedFactorsTransposeProductTranspose (TMat< T > &result, const TMat< T > &U, const TVec< T > d, const TMat< T > V, bool accumulate=false)
	return the matrix with elements (i,j) = sum_k U_{ki} d_k V_{jk}
template<class T> void	diagonalizedFactorsTransposeProductTransposeBprop (const TMat< T > &dCdresult, const TMat< T > &U, const TVec< T > d, const TMat< T > V, TMat< T > &dCdU, TVec< T > &dCdd, TMat< T > &dCdV)
template<class T> T	matRowDotVec (const TMat< T > &mat, int i, const TVec< T > v)
	return dot product of i-th row with vector v
template<class T> T	matColumnDotVec (const TMat< T > &mat, int j, const TVec< T > v)
	return dot product of j-th column with vector v
template<class T> void	matRowsDots (TVec< T > v, const TMat< T > &A, const TMat< T > &B)
	return dot products of i-th row of A with i-th row of B in vector v
template<class T> void	matRowsDotsAcc (TVec< T > v, const TMat< T > &A, const TMat< T > &B)
	return dot products of i-th row of A with i-th row of B in vector v
template<class T> void	fillItSymmetric (const TMat< T > &mat)
	Fill the bottom left part of a matrix with its top right part, so that it becomes symmetric.
template<class T> void	makeItSymmetric (const TMat< T > &mat, T max_dif)
template<class T> void	product (const TMat< T > &mat, const TVec< T > &x, TVec< T > &y)
template<class T> void	product (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	productAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	product2Acc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	squareProductAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	externalProduct (const TMat< T > &mat, const TVec< T > &v1, const TVec< T > &v2)
template<class T> void	externalProductAcc (const TMat< T > &mat, const TVec< T > &v1, const TVec< T > &v2)
template<class T> void	externalProductScaleAcc (const TMat< T > &mat, const TVec< T > &v1, const TVec< T > &v2, T gamma)
template<class T> void	externalProductScaleAcc (const TMat< T > &mat, const TVec< T > &v1, const TVec< T > &v2, T gamma, T alpha)
template<class T> void	productTranspose (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	squareProductTranspose (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	product2Transpose (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	productTransposeAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	product2TransposeAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	squareProductTransposeAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	transposeProduct (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	transposeProduct2 (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	transposeProductAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	transposeProduct2Acc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	transposeTransposeProduct (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	transposeTransposeProductAcc (const TMat< T > &mat, const TMat< T > &m1, const TMat< T > &m2)
template<class T> T	trace (const TMat< T > &mat)
template<class T> void	regularizeMatrix (const TMat< T > &mat, T tolerance)
	Applies a regularizer : diag(A) += (tolerance * trace(A)).
template<class T> void	makeRowsSumTo1 (const TMat< T > &mat)
template<class T> void	multiply (const TMat< T > &result, const TMat< T > &x, T scale)
template<class T> TMat< T >	operator * (const TMat< T > &m, const T &scalar)
template<class T> TMat< T >	operator * (const T &scalar, const TMat< T > &m)
template<class T> TMat< T >	operator/ (const TMat< T > &m, const T &scalar)
template<class T> void	multiplyAcc (const TMat< T > &mat, const TMat< T > &x, T scale)
template<class T> void	multiplyAcc (const TMat< T > &mat, const TMat< T > &x, const TMat< T > &y)
template<class T> void	squareMultiplyAcc (const TMat< T > &mat, const TMat< T > &x, T scale)
template<class T> void	diffSquareMultiplyAcc (const TMat< T > &mat, const TMat< T > &x, const TMat< T > &y, T scale)
template<class T> TVec< T >	selectAndOrder (const TMat< T > &mat, int pos, int colnum=0)
template<class T> void	addToDiagonal (const TMat< T > &mat, T lambda)
template<class T> void	addToDiagonal (const TMat< T > &mat, const TVec< T > &lambda)
template<class T> void	diag (const TMat< T > &mat, const TVec< T > &d)
template<class T> TVec< T >	diag (const TMat< T > &mat)
template<class T> void	diagonalOfSquare (const TMat< T > &mat, const TVec< T > &d)
template<class T> void	projectOnOrthogonalSubspace (const TMat< T > &mat, TMat< T > orthonormal_subspace)
template<class T> void	averageAcrossRowsAndColumns (const TMat< T > &mat, TVec< T > &avg_across_rows, TVec< T > &avg_across_columns, bool ignored)
template<class T> void	addToRows (const TMat< T > &mat, const TVec< T > row, bool ignored)
template<class T> void	addToColumns (const TMat< T > &mat, const TVec< T > col, bool ignored)
template<class T> void	substractFromRows (const TMat< T > &mat, const TVec< T > row, bool ignored)
template<class T> void	substractFromColumns (const TMat< T > &mat, const TVec< T > col, bool ignored)
template<class T> void	addToMat (const TMat< T > &mat, T scalar, bool ignored)
template<class T> T	sum (const TMat< T > &mat, bool ignore_missing=false)
template<class T> T	product (const TMat< T > &mat)
template<class T> T	sum_of_squares (const TMat< T > &mat)
template<class T> T	mean (const TMat< T > &mat)
template<class T> T	geometric_mean (const TMat< T > &mat)
template<class T> T	variance (const TMat< T > &mat, T meanval)
template<class T> T	correlation (const TMat< T > &mat)
template<class T> T	correlation (const TVec< T > &x, const TVec< T > &y)
template<class T> T	min (const TMat< T > &mat)
template<class T> T	max (const TMat< T > &mat)
template<class T> void	argmin (const TMat< T > &mat, int &mini, int &minj)
	Stores the position of the min in the 'mini' & 'minj' arg.
template<class T> void	argmax (const TMat< T > &mat, int &maxi, int &maxj)
template<class T> int	argmin (const TMat< T > &m)
	return mini*width+minj
template<class T> int	argmax (const TMat< T > &m)
	return maxi*width+maxj
template<class T> void	rowSum (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	rowSum (const TMat< T > &mat, const TVec< T > &colvec)
template<class T> void	rowMean (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	rowVariance (const TMat< T > &mat, const TMat< T > &singlecolumn, const TMat< T > &rowmean)
template<class T> void	rowSumOfSquares (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	rowMax (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	rowMax (const TMat< T > &mat, const TVec< T > &colvec)
template<class T> void	rowMin (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	rowMin (const TMat< T > &mat, const TVec< T > &colvec)
template<class T> void	rowArgmax (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	rowArgmin (const TMat< T > &mat, const TMat< T > &singlecolumn)
template<class T> void	columnSum (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnSumOfSquares (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnMean (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnWeightedMean (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnVariance (const TMat< T > &mat, TVec< T > &result, const TVec< T > &columnmean)
template<class T> void	columnWeightedVariance (const TMat< T > &mat, TVec< T > &result, const TVec< T > &column_weighted_mean)
template<class T> void	columnMax (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnMin (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnArgmax (const TMat< T > &mat, TVec< T > &result)
template<class T> void	columnArgmin (const TMat< T > &mat, TVec< T > &result)
template<class T> T	mahalanobis_distance (const TVec< T > &input, const TVec< T > &meanvec, const TMat< T > &inversecovmat)
template<class T> void	computeMean (const TMat< T > &m, TVec< T > &meanvec)
	compute the mean of the rows of m (looping over columns)
template<class T> void	computeMeanAndVariance (const TMat< T > &m, TVec< T > &meanvec, TVec< T > &variancevec)
	compute the mean and variance of the rows of m (looping over columns)
template<class T> void	computeCovar (const TMat< T > &m, const TVec< T > &meanvec, TMat< T > &covarmat)
template<class T> void	computeMeanAndCovar (const TMat< T > &m, TVec< T > &meanvec, TMat< T > &covarmat)
template<class T> void	computeMeanAndStddev (const TMat< T > &m, TVec< T > &meanvec, TVec< T > &stddevvec)
	compute the mean and standard deviations of the rows of m (looping over columns)
template<class T> void	computeColumnsMeanAndStddev (const TMat< T > &m, TMat< T > &meanvec, TMat< T > &stddevvec)
	compute the mean and standard deviations of the colums of m (looping over s) (the result is stored in column vectors meanvec and stddevvec)
template<class T> void	normalize (TMat< T > &m)
	substract mean, and divide by stddev (these are estimated globally)
template<class T> void	normalizeRows (const TMat< T > &m)
	Divides each row by the sum of its elements.
template<class T> void	normalizeColumns (const TMat< T > &m)
	Divides each column by the sum of its elements.
template<class T> void	normalize (TMat< T > &m, double n)
	divide each row by its n norm
template<class T> void	operator+= (const TMat< T > &m, T scalar)
template<class T> void	operator *= (const TMat< T > &m, T scalar)
template<class T> void	operator-= (const TMat< T > &m, T scalar)
template<class T> void	operator/= (const TMat< T > &m, T scalar)
template<class T> void	operator/= (const TMat< T > &m, int scalar)
template<class T> void	operator+= (const TMat< T > &m, const TVec< T > &v)
	adds v to every row
template<class T> void	operator-= (const TMat< T > &m, const TVec< T > &v)
	subtracts v from every row
template<class T> void	operator *= (const TMat< T > &m, const TVec< T > &v)
	does an elementwise multiplication of every row by v
template<class T> void	operator *= (const TMat< T > &m1, const TMat< T > &m2)
	does an elementwise division of every row by v
template<class T> void	operator/= (const TMat< T > &m, const TVec< T > &v)
template<class T> void	operator/= (const TMat< T > &m1, const TMat< T > &m2)
	does an elementwise division
template<class T> void	operator+= (const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	operator-= (const TMat< T > &m1, const TMat< T > &m2)
template<class T> TMat< T >	operator- (const TMat< T > &m1, const TMat< T > &m2)
template<class T> TMat< T >	operator+ (const TMat< T > &m1, const TMat< T > &m2)
template<class T> void	substract (const TMat< T > &m1, const TMat< T > &m2, TMat< T > &destination)
template<class T> void	add (const TMat< T > &m1, const TMat< T > &m2, TMat< T > &destination)
template<class T> TMat< T >	operator- (const TMat< T > &m)
	return a negated copy of m
template<class T> void	negateElements (const TMat< T > &m)
	x'_ij = -x_ij;
template<class T> void	invertElements (const TMat< T > &m)
	x'_ij = 1.0/x_ij;
template<class T> TMat< T >	leftPseudoInverse (TMat< T > &m)
template<class T> void	leftPseudoInverse (const TMat< T > &m, TMat< T > &inv)
template<class T> TMat< T >	rightPseudoInverse (TMat< T > &m)
template<class T> void	rightPseudoInverse (const TMat< T > &m, TMat< T > &inv)
template<class T> TMat< T >	inverse (TMat< T > &m)
template<class T> void	inverse (const TMat< T > &m, TMat< T > &inv)
template<class T> void	solveLinearSystemByCholesky (const TMat< T > &A, const TMat< T > &B, TMat< T > &X, TMat< T > *pL=0, TVec< T > *py=0)
template<class T> void	solveTransposeLinearSystemByCholesky (const TMat< T > &A, const TMat< T > &B, TMat< T > &X, TMat< T > *pL=0, TVec< T > *py=0)
template<class T> void	choleskyDecomposition (const TMat< T > &A, TMat< T > &L)
template<class T> void	bpropCholeskyDecomposition (const TMat< T > &A, const TMat< T > &L, TMat< T > &dC_dA, TMat< T > &dC_dL)
template<class T> void	choleskySolve (const TMat< T > &L, TVec< T > b, TVec< T > x, TVec< T > &y)
template<class T> void	choleskySolve (const TMat< T > &L, const TMat< T > &B, TMat< T > &X, TVec< T > &y)
template<class T> void	bpropCholeskySolve (const TMat< T > &L, const TVec< T > &x, const TVec< T > &y, TMat< T > &dC_dL, TVec< T > &dC_db, TVec< T > &dC_dx)
template<class T> real	choleskyInvert (const TMat< T > &A, TMat< T > &Ainv)
template<class T> TVec< T >	choleskySolve (const TMat< T > &A, const TVec< T > &b)
template<class T> TMat< T >	choleskyInvert (const TMat< T > &A)
template<class T> void	LU_decomposition (TMat< T > &A, TVec< T > &Trow, int &detsign, TVec< T > *p=0)
template<class T> T	det (const TMat< T > &A)
template<class T> T	det (const TMat< T > &LU, int detsign)
template<class T> void	equals (const TMat< T > &src, T v, TMat< T > &dest)
template<class T> void	transpose (const TMat< T > src, TMat< T > dest)
template<class T> TMat< T >	transpose (const TMat< T > &src)
template<class T> void	apply (T(*func)(const TVec< T > &), const TMat< T > &m, TMat< T > &dest)
template<class T> void	apply (T(*func)(const TVec< T > &, const TVec< T > &), const TMat< T > &m1, const TMat< T > &m2, TMat< T > &dest)
template<class T> void	linearRegressionNoBias (TMat< T > inputs, TMat< T > outputs, T weight_decay, TMat< T > weights)
template<class T> void	linearRegression (TMat< T > inputs, TMat< T > outputs, T weight_decay, TMat< T > theta_t)
template<class T> void	linearRegression (TVec< T > inputs, TVec< T > outputs, T weight_decay, TVec< T > theta_t)
template<class T> TMat< T >	smooth (TMat< T > data, int windowsize)
template<class T> TMat< T >	square (const TMat< T > &m)
template<class T> TMat< T >	sqrt (const TMat< T > &m)
template<class T> void	affineMatrixInitialize (TMat< T > W, bool output_on_columns=true, real scale=1.0)
template<class T> TMat< T >	grep (TMat< T > data, int col, TVec< T > values, bool exclude=false)
template<class T> void	convolve (TMat< T > m, TMat< T > mask, TMat< T > result)
template<class T> void	subsample (TMat< T > m, int thesubsamplefactor, TMat< T > result)
template<class T> void	classification_confusion_matrix (TMat< T > outputs, TMat< T > target_classes, TMat< T > confusion_matrix)
template<class T> int	GramSchmidtOrthogonalization (TMat< T > A, T tolerance=1e-6)
	Orthonormalize in-place the rows of the given matrix, using successive projections on the orthogonal subspace of the previously found basis.
template<class T> TVec< T >	product (const TMat< T > &m, const TVec< T > &v)
	products return m x v
template<class T> TVec< T >	transposeProduct (const TMat< T > &m, const TVec< T > &v)
	return m' x v
template<class T> TMat< T >	product (const TMat< T > &m1, const TMat< T > &m2)
	return m1 x m2
template<class T> TMat< T >	transposeProduct (const TMat< T > &m1, const TMat< T > &m2)
	return m1' x m2
template<class T> TMat< T >	productTranspose (const TMat< T > &m1, const TMat< T > &m2)
	return m1 x m2'
template<class T> TMat< T >	operator+ (const TMat< T > &m, const TVec< T > &v)
	return m + v (added to every ROW of m)
template<class T> TMat< T >	operator- (const TMat< T > &m, const TVec< T > &v)
	return m - v (subtracted from every ROW of m)
template<class T> TMat< T >	operator * (const TMat< T > &m, const TVec< T > &v)
	does an elementwise multiplication of every row by v
template<class T> TMat< T >	operator/ (const TMat< T > &m, const TVec< T > &v)
	elementwise division of every row by v
template<class T> TMat< T >	operator/ (const TMat< T > &m1, const TMat< T > &m2)
	elementwise division of every row by v
template<class T> void	choleskySolve (const TMat< T > &L, TVec< T > b, TVec< T > x)
template<class T> TMat< T >	grep (TMat< T > data, int col, T value, bool exclude=false)
	Same as above, but with a single value argument.
template<class T> void	addIfNonMissing (const TVec< T > &source, const TVec< int > &nnonmissing, TVec< T > destination)
template<class T> void	addXandX2IfNonMissing (const TVec< T > &source, const TVec< int > &nnonmissing, TVec< T > somme, TVec< T > somme2)
real	dot_product (real s, real *x, real *y, int n)
void	bprop_update_layer (real *dy, real *x, real *dx, real *w, int n_y, int n_x, real learning_rate, real weight_decay)
template<class T> void	sortRows (TMat< T > &mat, const TVec< int > &key_columns, bool increasing_order=true)
template<class T> void	sortElements (const TVec< T > &vec)
	Sorts the elements of vec in place.
template<class T> void	partialSortRows (const TMat< T > &mat, int k, int sortk=1, int col=0)
	Uses partial_sort.
template<class T> void	sortRows (const TMat< T > &mat, int col=0, bool increasing_order=true)
	This implementation should be very efficient, but it does two memory allocation: a first one of mat.length()*(sizeof(real)+sizeof(int)) and a second one of mat.length()*sizeof(int).
template<class T> void	sortColumns (const TMat< T > &mat, int rownum)
template<class T> int	binary_search (const TVec< T > &src, T x)
template<class T> int	binary_search (const TMat< T > &src, int c, T x)
template<class T> TMatColRowsIterator< T >	operator+ (typename TMatColRowsIterator< T >::difference_type n, const TMatColRowsIterator< T > &y)
template<class T> TMatRowsAsArraysIterator< T >	operator+ (typename TMatRowsAsArraysIterator< T >::difference_type n, const TMatRowsAsArraysIterator< T > &y)
template<class T> TMatRowsIterator< T >	operator+ (typename TMatRowsIterator< T >::difference_type n, const TMatRowsIterator< T > &y)
void	operator<< (const Vec &v, real f)
	Same as fill(f) (will only work with Vec, because of a potential conflict with T == string if we wanted to make it generic).
template<class T> TVec< T >	deepCopy (const TVec< T > &source)
template<class T> TVec< T >	deepCopy (const TVec< T > &source, CopiesMap &copies)
template<class T> void	deepCopyField (TVec< T > &field, CopiesMap &copies)
template<class T> void	swap (TVec< T > &a, TVec< T > &b)
template<class T> void	operator<< (const TVec< T > &m1, const TVec< T > &m2)
	copy TVec << TVec
template<class T, class U> void	operator<< (const TVec< T > &m1, const TVec< U > &m2)
	copy TVec << TVec (different types)
template<class T, class U> void	operator>> (const TVec< T > &m1, const TVec< U > &m2)
	copy TVec >> TVec
template<class T> void	savePVec (const string &filename, const TVec< T > &vec)
template<class T> PStream &	operator<< (PStream &out, const TVec< T > &v)
	Read and Write from C++ stream: write saves length and read resizes accordingly (the raw modes don't write any size information).
template<class T> PStream &	operator>> (PStream &in, TVec< T > &v)
template<class T> void	binwrite (ostream &out, const TVec< T > &v)
template<class T> void	binread (istream &in, TVec< T > &v)
template<class T> void	binwrite_double (ostream &out, const TVec< T > &v)
template<class T> void	binread_double (istream &in, TVec< T > &v)
template<class T> ostream &	operator<< (ostream &out, const TVec< T > &v)
template<class T> istream &	operator>> (istream &in, const TVec< T > &v)
template<class T> bool	operator<= (const TVec< T > &left, const TVec< T > &right)
	A simple family of relational operators for TVec.
template<class T> bool	operator>= (const TVec< T > &left, const TVec< T > &right)
template<class T> bool	operator< (const TVec< T > &left, const TVec< T > &right)
template<class T> bool	operator> (const TVec< T > &left, const TVec< T > &right)
	PLEARN_IMPLEMENT_OBJECT (VecStatsCollector,"Collects basic statistics on a vector","VecStatsCollector allows to collect statistics on a series of vectors.\n""Individual vectors x are presented by calling update(x), and this class will\n""collect both individual statistics for each element (as a Vec<StatsCollector>)\n""as well as (optionally) compute the covariance matrix.")
	DECLARE_OBJECT_PTR (VecStatsCollector)
	PLEARN_IMPLEMENT_OBJECT (NearestNeighborPredictionCost,"ONE LINE DESCRIPTION","MULTI LINE\nHELP")
	DECLARE_OBJECT_PTR (NearestNeighborPredictionCost)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (ObjectGenerator,"ObjectGenerator is the base class for implementing object-generation techniques.","The OptionGenerator takes a template Object, and from a list of options,\n""it will generate another Object (or a complete list).\n")
	DECLARE_OBJECT_PTR (ObjectGenerator)
	PLEARN_IMPLEMENT_OBJECT (RunObject,"Allows to build a non-runnable object in a PLearn script.","This Object implements a run() method so that it can be used in\n""a PLearn script, in order to build another Object given by the\n""'underlying_object' option without PLearn returning an error.\n")
	DECLARE_OBJECT_PTR (RunObject)
	PLEARN_IMPLEMENT_OBJECT (ShellScript,"Allows one to run shell commands (especially within a script).","This runnable object will execute the given shell commands when run.")
	DECLARE_OBJECT_PTR (ShellScript)
int	print_diff (ostream &out, VMat m1, VMat m2, double tolerance)
	Prints where m1 and m2 differ by more than tolerance returns the number of such differences, or -1 if the sizes differ.
void	interactiveDisplayCDF (const Array< VMat > &vmats)
void	displayBasicStats (VMat vm)
void	printDistanceStatistics (VMat vm, int inputsize)
bool	getList (char *str, int curj, const VMat &vm, Vec &outList, char *strReason)
void	viewVMat (const VMat &vm)
void	plotVMats (char *defs[], int ndefs)
int	vmatmain (int argc, char **argv)
	PLEARN_IMPLEMENT_OBJECT (AdaptGradientOptimizer,"An optimizer that performs gradient descent with learning rate adaptation.","")
	PLEARN_IMPLEMENT_OBJECT (ConjGradientOptimizer,"Optimizer based on the conjugate gradient method.","The conjugate gradient algorithm is basically the following :\n""- 0: initialize the search direction d = -gradient\n""- 1: perform a line search along direction d for the minimum of the gradient\n""- 2: move to this minimum, update the search direction d and go to step 1\n""There are various methods available through the options for both steps 1 and 2.")
	PLEARN_IMPLEMENT_OBJECT (GradientOptimizer,"Optimization by gradient descent.","GradientOptimizer is the simple usual gradient descent algorithm \n"" (the number of samples on which to estimate gradients before an \n"" update, which determines whether we are performing 'batch' \n"" 'stochastic' or even 'minibatch', is currently specified outside \n"" this class, typically in the numer of s/amples of the meanOf function \n"" to be optimized, as its 'nsamples' parameter). \n""Options for GradientOptimizer are [ option_name: <type> (default) ]: \n"" - start_learning_rate: <real> (0.01) \n"" the initial learning rate \n"" - decrease_constant: <real> (0) \n"" the learning rate decrease constant \n""\n""GradientOptimizer derives form Optimizer. \n")
	DECLARE_OBJECT_PTR (GradientOptimizer)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (HyperOptimizer,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (HSetVal,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (HTryAll,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (HCoordinateDescent,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (HTryCombinations,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (HyperOptimizer)
	DECLARE_OBJECT_PTR (HSetVal)
	DECLARE_OBJECT_PTR (HTryAll)
	DECLARE_OBJECT_PTR (HCoordinateDescent)
	DECLARE_OBJECT_PTR (HTryCombinations)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (Optimizer,"ONE LINE DESCR","NO HELP")
void	varDeepCopyField (Var &field, CopiesMap &copies)
	To use varDeepCopyField.
	DECLARE_OBJECT_PTR (Optimizer)
RandomVar	operator * (RandomVar a, RandomVar b)
	********************** GLOBAL FUNCTIONS ********************** //!<
RandomVar	operator+ (RandomVar a, RandomVar b)
RandomVar	operator- (RandomVar a, RandomVar b)
	Return a MatRandomVar that is the element-by-element difference of two RandomVar's.
RandomVar	operator/ (RandomVar a, RandomVar b)
	Return a MatRandomVar that is the element-by-element ratio of two RandomVar's.
RandomVar	exp (RandomVar x)
	exponential function applied element-by-element
RandomVar	log (RandomVar x)
	natural logarithm function applied element-by-element
RandomVar	extend (RandomVar v, real extension_value, int n_extend)
RandomVar	hconcat (const RVArray &a)
real	EM (ConditionalExpression conditional_expression, RVArray parameters_to_learn, VMat distr, int n_samples, int max_n_iterations, real relative_improvement_threshold, bool accept_worsening_likelihood, bool compute_final_train_NLL)
real	EM (ConditionalExpression conditional_expression, RVArray parameters_to_learn, VMat distr, int n_samples, int max_n_iterations, real relative_improvement_threshold, bool compute_final_train_NLL)
Var	P (ConditionalExpression conditional_expression, bool clearMarksUponReturn)
Var	logP (ConditionalExpression conditional_expression, bool clearMarksUponReturn, RVInstanceArray *parameters_to_learn)
Var	ElogP (ConditionalExpression conditional_expression, RVInstanceArray &parameters_to_learn, bool clearMarksUponReturn)
RandomVar	marginalize (const RandomVar &RV, const RandomVar &hiddenRV)
Vec	sample (ConditionalExpression conditional_expression)
void	sample (ConditionalExpression conditional_expression, Mat &samples)
Var	Sample (ConditionalExpression conditional_expression)
RandomVar	normal (real mean, real standard_dev, int d, real minimum_standard_deviation)
	Functions to build a normal distribution.
RandomVar	normal (RandomVar mean, RandomVar log_variance, real minimum_standard_deviation)
RandomVar	mixture (RVArray components, RandomVar log_weights)
RandomVar	multinomial (RandomVar log_probabilities)
real	oEM (ConditionalExpression conditional_expression, RVArray parameters_to_learn, VMat distr, int n_samples, int max_n_iterations, real relative_improvement_threshold=0.001, bool compute_final_train_NLL=true)
real	oEM (ConditionalExpression conditional_expression, RVArray parameters_to_learn, VMat distr, int n_samples, Optimizer &MStepOptimizer, int max_n_iterations, real relative_improvement_threshold=0.001, bool compute_final_train_NLL=true)
int	establish_connection (int n_hosts, const char *hostnames[], int port_no)
int	establish_connection (const char *hostname, int port_no)
int	establish_connection (const int argc, const char *argv[])
void *	MemoryMap (const char *filename, tFileHandle &handle, bool read_only, off_t &filesize)
	returns a pointer to the memory-mapped file or 0 if it fails for some reason.
void	memoryUnmap (void *data, tFileHandle handle, int length)
vector< string >	execute (const string &command)
int	getSystemTotalMemory ()
	PLEARN_IMPLEMENT_OBJECT (AbsVariable,"ONE LINE DESCR","NO HELP")
	AbsVariable *.
	DECLARE_OBJECT_PTR (AbsVariable)
Var	abs (Var v)
	PLEARN_IMPLEMENT_OBJECT (AffineTransformVariable,"Affine transformation of a vector variable.","NO HELP")
	DECLARE_OBJECT_PTR (AffineTransformVariable)
Var	affine_transform (Var vec, Var transformation)
	first row of transformation is the bias.
	PLEARN_IMPLEMENT_OBJECT (AffineTransformWeightPenalty,"Affine transformation with Weight decay terms","NO HELP")
	DECLARE_OBJECT_PTR (AffineTransformWeightPenalty)
Var	affine_transform_weight_penalty (Var transformation, real weight_decay, real bias_decay=0, bool L1_penalty=false)
	weight decay and bias decay terms This has not been tested yet [Pascal: a tester].
	PLEARN_IMPLEMENT_OBJECT (ArgmaxVariable,"Compute the index of the maximum value in the input","NO HELP")
	ArgmaxVariable *.
	DECLARE_OBJECT_PTR (ArgmaxVariable)
Var	argmax (Var v)
	PLEARN_IMPLEMENT_OBJECT (ArgminOfVariable,"ONE LINE DESCR","NO HELP")
Var	argminOf (Var v, Var expression, Var values_of_v, VarArray inputs)
	PLEARN_IMPLEMENT_OBJECT (ArgminVariable,"Compute the index of the minimum value in the input","NO HELP")
	ArgminVariable *.
	DECLARE_OBJECT_PTR (ArgminVariable)
Var	argmin (Var v)
	PLEARN_IMPLEMENT_OBJECT (BinaryClassificationLossVariable,"For one-dimensional output: class is 0 if output < 0.5, and 1 if >= 0.5.","NO HELP")
	BinaryClassificationLossVariable *.
	DECLARE_OBJECT_PTR (BinaryClassificationLossVariable)
Var	binary_classification_loss (Var network_output, Var classnum)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (BinaryVariable,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (ClassificationLossVariable,"Indicator(classnum==argmax(netout))","NO HELP")
	ClassificationLossVariable *.
	DECLARE_OBJECT_PTR (ClassificationLossVariable)
Var	classification_loss (Var network_output, Var classnum)
	PLEARN_IMPLEMENT_OBJECT (ColumnIndexVariable,"Return a row vector with the elements indexed in each column","NO HELP")
	ColumnIndexVariable *.
	DECLARE_OBJECT_PTR (ColumnIndexVariable)
Var	matrixIndex (Var mat, Var index)
	PLEARN_IMPLEMENT_OBJECT (ColumnSumVariable,"ONE LINE DESCR","NO HELP")
Var	columnSum (Var v)
	PLEARN_IMPLEMENT_OBJECT (ConcatColumnsVariable,"Concatenation of the columns of several variables","NO HELP")
	ConcatColumnsVariable *.
	DECLARE_OBJECT_PTR (ConcatColumnsVariable)
Var	hconcat (const VarArray &varray)
	PLEARN_IMPLEMENT_OBJECT (ConcatOfVariable,"Concatenates the results of each operation in the loop into the resulting variable","NO HELP")
	ConcatOfVariable *.
	DECLARE_OBJECT_PTR (ConcatOfVariable)
Var	concatOf (VMat distr, Func f)
	concatOf
Var	concatOf (Var output, const VarArray &inputs, VMat distr, int nsamples, VarArray parameters=VarArray())
	deprecated old version, do not use!
	PLEARN_IMPLEMENT_OBJECT (ConcatRowsVariable,"Concatenation of the rows of several variables","NO HELP")
	ConcatRowsVariable *.
	DECLARE_OBJECT_PTR (ConcatRowsVariable)
Var	vconcat (const VarArray &varray)
	PLEARN_IMPLEMENT_OBJECT (ConvolveVariable,"A convolve var; equals convolve(input, mask)","NO HELP")
	ConvolveVariable *.
	DECLARE_OBJECT_PTR (ConvolveVariable)
Var	convolve (Var input, Var mask)
	PLEARN_IMPLEMENT_OBJECT (CrossEntropyVariable,"cost = - sum_i {target_i * log(output_i) + (1-target_i) * log(1-output_i)}","NO HELP")
	CrossEntropyVariable *.
	DECLARE_OBJECT_PTR (CrossEntropyVariable)
Var	cross_entropy (Var network_output, Var targets)
	PLEARN_IMPLEMENT_OBJECT (CutAboveThresholdVariable,"ONE LINE DESCR","NO HELP")
	CutAboveThresholdVariable *.
	DECLARE_OBJECT_PTR (CutAboveThresholdVariable)
Var	cutAboveThreshold (Var v, real threshold)
Var	negative (Var v)
	PLEARN_IMPLEMENT_OBJECT (CutBelowThresholdVariable,"ONE LINE DESCR","NO HELP")
	CutBelowThresholdVariable *.
	DECLARE_OBJECT_PTR (CutBelowThresholdVariable)
Var	cutBelowThreshold (Var v, real threshold)
Var	positive (Var v)
	PLEARN_IMPLEMENT_OBJECT (DeterminantVariable,"The argument must be a square matrix Var and the result is its determinant","NO HELP")
	DeterminantVariable *.
	DECLARE_OBJECT_PTR (DeterminantVariable)
Var	det (Var m)
	PLEARN_IMPLEMENT_OBJECT (DiagonalizedFactorsProductVariable,"Variable that represents the leftmatrix*diag(vector)*rightmatrix product","The three parents are respectively the left matrix U, the center vector d,\n""and the right matrix V. Options allow to transpose the matrices.\n""The output value has elements (i,j) equal to sum_k U_{ik} d_k V_{kj}\n")
	DiagonalizedFactorsProductVariable *.
	DECLARE_OBJECT_PTR (DiagonalizedFactorsProductVariable)
Var	diagonalized_factors_product (Var left_matrix, Var center_diagonal, Var right_matrix)
	PLEARN_IMPLEMENT_OBJECT (DilogarithmVariable,"This Var computes the dilogarithm function","The dilogarithm function is useful to compute the primitive of the softplus.\n"" dilogarithm(x) = sum_{k=1}^\\infty x^k/k^2\n""so dilogarithm'(x) = -(1/x)log(1-x), i.e. e^x dilogarithm'(-e^x)=log(1+e^x)=softplus(x)\n""and primitive(softplus)(x) = -dilogarithm(-e^x)\n")
	DilogarithmVariable *.
	DECLARE_OBJECT_PTR (DilogarithmVariable)
Var	dilogarithm (Var v)
Var	softplus_primitive (Var v)
	PLEARN_IMPLEMENT_OBJECT (DivVariable,"Divide 2 matrix vars of same size elementwise","NO HELP")
	DivVariable *.
	DECLARE_OBJECT_PTR (DivVariable)
	PLEARN_IMPLEMENT_OBJECT (DotProductVariable,"Dot product between 2 matrices (or vectors) with same number of elements","NO HELP")
	DotProductVariable *.
	DECLARE_OBJECT_PTR (DotProductVariable)
Var	dot (Var v1, Var v2)
	dot product
	PLEARN_IMPLEMENT_OBJECT (DuplicateColumnVariable,"ONE LINE DESCR","NO HELP")
	DuplicateColumnVariable *.
	DECLARE_OBJECT_PTR (DuplicateColumnVariable)
Var	duplicateColumn (Var v, int the_width)
	PLEARN_IMPLEMENT_OBJECT (DuplicateRowVariable,"ONE LINE DESCR","NO HELP")
	DuplicateRowVariable *.
	DECLARE_OBJECT_PTR (DuplicateRowVariable)
Var	duplicateRow (Var v, int the_length)
	PLEARN_IMPLEMENT_OBJECT (DuplicateScalarVariable,"ONE LINE DESCR","NO HELP")
	DuplicateScalarVariable *.
	DECLARE_OBJECT_PTR (DuplicateScalarVariable)
Var	duplicateScalar (Var v, int the_length, int the_width)
	PLEARN_IMPLEMENT_OBJECT (ElementAtPositionVariable,"A variable of size length() x width(), filled with zeros except for the ""single element indexed by input2 =(i,j) or input2 = (k).","NO HELP")
	ElementAtPositionVariable *.
	DECLARE_OBJECT_PTR (ElementAtPositionVariable)
	PLEARN_IMPLEMENT_OBJECT (EqualConstantVariable,"A scalar var; equal 1 if input1==input2, 0 otherwise","NO HELP")
	EqualConstantVariable *.
	DECLARE_OBJECT_PTR (EqualConstantVariable)
Var	operator== (Var v1, real cte)
	result[i] = 1 if v1[i]==cte, 0 otherwise
Var	operator== (real cte, Var v1)
	result[i] = 1 if v1[i]==cte, 0 otherwise
	PLEARN_IMPLEMENT_OBJECT (EqualScalarVariable,"A scalar var; equal 1 if input1==input2, 0 otherwise","NO HELP")
	EqualScalarVariable *.
	DECLARE_OBJECT_PTR (EqualScalarVariable)
	PLEARN_IMPLEMENT_OBJECT (EqualVariable,"A scalar var; equal 1 if input1==input2, 0 otherwise","NO HELP")
	EqualVariable *.
Var	isequal (Var v1, Var v2)
	DECLARE_OBJECT_PTR (EqualVariable)
	PLEARN_IMPLEMENT_OBJECT (ErfVariable,"ONE LINE DESCR","NO HELP")
	ErfVariable *.
	DECLARE_OBJECT_PTR (ErfVariable)
Var	erf (Var v)
	PLEARN_IMPLEMENT_OBJECT (ExpVariable,"ONE LINE DESCR","NO HELP")
	ExpVariable *.
	DECLARE_OBJECT_PTR (ExpVariable)
Var	exp (Var v)
	PLEARN_IMPLEMENT_OBJECT (ExtendedVariable,"Variable that extends the input variable by appending rows at ""its top and bottom and columns at its left and right.","NO HELP")
	ExtendedVariable *.
	DECLARE_OBJECT_PTR (ExtendedVariable)
Var	extend (Var v, int top_extent, int bottom_extent, int left_extent, int right_extent, real fill_value=0.0)
	general extension of a matrix in any direction
Var	extend (Var v, real extension_value=1.0, int n_extend=1)
	simple extension of a vector (same semantic as old extend, when we only had vectors)
Func	operator/ (Func f, real value)
	PLEARN_IMPLEMENT_OBJECT (Function,"Implements a function defined as a var graph","NO HELP")
template<> void	deepCopyField (Func &field, CopiesMap &copies)
	DECLARE_OBJECT_PTR (Function)
	DECLARE_OBJECT_PP (Func, Function)
	PLEARN_IMPLEMENT_OBJECT (HardSlopeVariable,"This Var computes the hard_slope function","The hard_slope function is linear by parts function:\n""0 in [-infty,left], linear in [left,right], and 1 in [right,infty], and continuous.\n""If the arguments are vectors than the operation is performed element by element on all of them.\n")
	HardSlopeVariable *.
	DECLARE_OBJECT_PTR (HardSlopeVariable)
Var	hard_slope (Var x, Var left, Var right)
Var	d_hard_slope (Var x, Var left, Var right)
	PLEARN_IMPLEMENT_OBJECT (IfThenElseVariable,"Variable that represents the element-wise IF-THEN-ELSE","NO HELP")
	IfThenElseVariable *.
	DECLARE_OBJECT_PTR (IfThenElseVariable)
Var	ifThenElse (Var If, Var Then, Var Else)
	IT WOULD BE NICE IF WE COULD REDEFINE (:?).
	PLEARN_IMPLEMENT_OBJECT (IndexAtPositionVariable,"ONE LINE DESCR","NO HELP")
	IndexAtPositionVariable *.
	DECLARE_OBJECT_PTR (IndexAtPositionVariable)
	PLEARN_IMPLEMENT_OBJECT (InterValuesVariable,"if values = [x1,x2,...,x10], the resulting variable is [(x1+x2)/2,(x2+x3)/2, ... (x9+x10)/2]","NO HELP")
	InterValuesVariable *.
	DECLARE_OBJECT_PTR (InterValuesVariable)
Var	interValues (Var values)
	if values = [x1,x2,...,x10], the resulting variable is [(x1+x2)/2,(x2+x3)/2, ...
	PLEARN_IMPLEMENT_OBJECT (InvertElementsVariable,"ONE LINE DESCR","NO HELP")
	InvertElementsVariable *.
	DECLARE_OBJECT_PTR (InvertElementsVariable)
Var	invertElements (Var v)
	PLEARN_IMPLEMENT_OBJECT (IsAboveThresholdVariable,"ONE LINE DESCR","NO HELP")
	IsAboveThresholdVariable *.
	DECLARE_OBJECT_PTR (IsAboveThresholdVariable)
Var	isAboveThreshold (Var v, real threshold=0, real truevalue=1, real falsevalue=0, bool strict=false)
Var	operator>= (Var v, real threshold)
Var	operator<= (Var v, real threshold)
	PLEARN_IMPLEMENT_OBJECT (IsLargerVariable,"ONE LINE DESCR","NO HELP")
	IsLargerVariable *.
	DECLARE_OBJECT_PTR (IsLargerVariable)
Var	operator> (Var v1, Var v2)
Var	operator<= (Var v1, Var v2)
	PLEARN_IMPLEMENT_OBJECT (IsMissingVariable,"ONE LINE DESCR","NO HELP")
	IsMissingVariable *.
	DECLARE_OBJECT_PTR (IsMissingVariable)
Var	isMissing (Var x)
	PLEARN_IMPLEMENT_OBJECT (IsSmallerVariable,"ONE LINE DESCR","NO HELP")
	IsSmallerVariable *.
	DECLARE_OBJECT_PTR (IsSmallerVariable)
Var	operator< (Var v1, Var v2)
	++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Var	operator>= (Var v1, Var v2)
	PLEARN_IMPLEMENT_OBJECT (LeftPseudoInverseVariable,"ONE LINE DESCR","NO HELP")
	LeftPseudoInverseVariable *.
	DECLARE_OBJECT_PTR (LeftPseudoInverseVariable)
Var	leftPseudoInverse (Var v)
	PLEARN_IMPLEMENT_OBJECT (LiftOutputVariable,"The result is the output if the target is 1, and the opposite of the output ""otherwise. This variable is to be used with a LiftStatsCollector, in a""stochastic gradient descent.","NO HELP")
	LiftOutputVariable *.
	DECLARE_OBJECT_PTR (LiftOutputVariable)
Var	lift_output (Var linear_output, Var target)
	PLEARN_IMPLEMENT_OBJECT (LogAddVariable,"output = log(exp(input1)+exp(input2)) but it is ""computed in such a way as to preserve precision","NO HELP")
	LogAddVariable *.
	DECLARE_OBJECT_PTR (LogAddVariable)
Var	logadd (Var &input1, Var &input2)
	PLEARN_IMPLEMENT_OBJECT (LogSoftmaxVariable,"ONE LINE DESCR","NO HELP")
	LogSoftmaxVariable *.
	DECLARE_OBJECT_PTR (LogSoftmaxVariable)
Var	log_softmax (Var v)
	PLEARN_IMPLEMENT_OBJECT (LogSumVariable,"ONE LINE DESCR","NO HELP")
Var	logadd (Var input)
	PLEARN_IMPLEMENT_OBJECT (LogVariable,"ONE LINE DESCR","NO HELP")
	LogVariable *.
	DECLARE_OBJECT_PTR (LogVariable)
Var	log (Var v)
	PLEARN_IMPLEMENT_OBJECT (MarginPerceptronCostVariable,"Compute sigmoid of its first input, and then computes the negative ""cross-entropy cost","NO HELP")
	MarginPerceptronCostVariable *.
	DECLARE_OBJECT_PTR (MarginPerceptronCostVariable)
Var	margin_perceptron_cost (Var output, Var target, real margin)
	PLEARN_IMPLEMENT_OBJECT (MatrixAffineTransformFeedbackVariable,"Affine transformation of a MATRIX variable.","NO HELP")
	DECLARE_OBJECT_PTR (MatrixAffineTransformFeedbackVariable)
	PLEARN_IMPLEMENT_OBJECT (MatrixAffineTransformVariable,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (MatrixAffineTransformVariable)
	PLEARN_IMPLEMENT_OBJECT (MatrixElementsVariable,"Fills the elements of a matrix using the given scalar Variable ""expression, that depends on index variables i and j, that loop from ""0 to ni-1 and 0 to nj-1 respectively.","NO HELP")
	MatrixElementsVariable *.
	DECLARE_OBJECT_PTR (MatrixElementsVariable)
Var	matrixElements (Var expression, const Var &i, const Var &j, int ni, int nj, const VarArray &parameters)
	PLEARN_IMPLEMENT_OBJECT (MatrixInverseVariable,"Matrix inversions... ","NO HELP")
	MatrixInverseVariable *.
	DECLARE_OBJECT_PTR (MatrixInverseVariable)
Var	matrixInverse (Var v)
	PLEARN_IMPLEMENT_OBJECT (MatrixOneHotSquaredLoss,"ONE LINE DESCR","NO HELP")
	MatrixOneHotSquaredLoss *.
	DECLARE_OBJECT_PTR (MatrixOneHotSquaredLoss)
	PLEARN_IMPLEMENT_OBJECT (MatrixSoftmaxLossVariable,"ONE LINE DESCR","NO HELP")
	MatrixSoftmaxLossVariable *.
	DECLARE_OBJECT_PTR (MatrixSoftmaxLossVariable)
	PLEARN_IMPLEMENT_OBJECT (MatrixSoftmaxVariable,"ONE LINE DESCR","NO HELP")
	MatrixSoftmaxVariable *.
	DECLARE_OBJECT_PTR (MatrixSoftmaxVariable)
	PLEARN_IMPLEMENT_OBJECT (MatrixSumOfVariable,"ONE LINE DESCR","NO HELP")
	MatrixSumOfVariable *.
	DECLARE_OBJECT_PTR (MatrixSumOfVariable)
Var	sumOf (VMat distr, Func f, int nsamples, int input_size)
Var	meanOf (VMat distr, Func f, int nsamples, int input_size)
	PLEARN_IMPLEMENT_OBJECT (MatRowVariable,"Variable that is the row of matrix mat indexed by variable input","NO HELP")
	MatRowVariable *.
	DECLARE_OBJECT_PTR (MatRowVariable)
Var	accessRow (const Mat &m, Var index)
	PLEARN_IMPLEMENT_OBJECT (Max2Variable,"Elementwise max over 2 elements: max(v1,v2)[i] = max(v1[i],v2[i]) ""with same dimensions as the input vectors","NO HELP")
	Max2Variable *.
	DECLARE_OBJECT_PTR (Max2Variable)
Var	max (Var v1, Var v2)
	PLEARN_IMPLEMENT_OBJECT (MaxVariable,"ONE LINE DESCR","NO HELP")
	MaxVariable *.
	DECLARE_OBJECT_PTR (MaxVariable)
Var	max (Var v)
	PLEARN_IMPLEMENT_OBJECT (MiniBatchClassificationLossVariable,"ONE LINE DESCR","NO HELP")
	MiniBatchClassificationLossVariable *.
	DECLARE_OBJECT_PTR (MiniBatchClassificationLossVariable)
	PLEARN_IMPLEMENT_OBJECT (MinusColumnVariable,"ONE LINE DESCR","NO HELP")
	MinusColumnVariable *.
	DECLARE_OBJECT_PTR (MinusColumnVariable)
	PLEARN_IMPLEMENT_OBJECT (MinusRowVariable,"ONE LINE DESCR","NO HELP")
	MinusRowVariable *.
	DECLARE_OBJECT_PTR (MinusRowVariable)
	PLEARN_IMPLEMENT_OBJECT (MinusScalarVariable,"ONE LINE DESCR","NO HELP")
	MinusScalarVariable *.
	PLEARN_IMPLEMENT_OBJECT (MinusTransposedColumnVariable,"ONE LINE DESCR","NO HELP")
	MinusTransposedColumnVariable *.
	DECLARE_OBJECT_PTR (MinusTransposedColumnVariable)
	PLEARN_IMPLEMENT_OBJECT (MinusVariable,"ONE LINE DESCR","NO HELP")
	MinusVariable *.
	DECLARE_OBJECT_PTR (MinusVariable)
Var	minus (Var v, Var w)
	PLEARN_IMPLEMENT_OBJECT (MinVariable,"ONE LINE DESCR","NO HELP")
	MinVariable *.
	DECLARE_OBJECT_PTR (MinVariable)
Var	min (Var v)
	PLEARN_IMPLEMENT_OBJECT (MulticlassLossVariable,"ONE LINE DESCR","NO HELP")
	MulticlassLossVariable *.
	DECLARE_OBJECT_PTR (MulticlassLossVariable)
Var	multiclass_loss (Var network_output, Var targets)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (NaryVariable,"ONE LINE DESCR","NO HELP")
	NaryVariable *.
	PLEARN_IMPLEMENT_OBJECT (NegateElementsVariable,"Elementwise negation and inversion...","NO HELP")
	NegateElementsVariable *.
	DECLARE_OBJECT_PTR (NegateElementsVariable)
Var	negateElements (Var v)
	PLEARN_IMPLEMENT_OBJECT (NegCrossEntropySigmoidVariable,"Compute sigmoid of its first input, and then computes the negative ""cross-entropy cost","NO HELP")
	NegCrossEntropySigmoidVariable *.
	DECLARE_OBJECT_PTR (NegCrossEntropySigmoidVariable)
Var	stable_cross_entropy (Var linear_output, Var target)
	PLEARN_IMPLEMENT_OBJECT (NllSemisphericalGaussianVariable,"Computes the negative log-likelihood of a Gaussian on some data point, depending on the nearest neighbors."," This class implements the negative log-likelihood cost of a Markov chain that\n"" uses semispherical gaussian transition probabilities. The parameters of the\n"" semispherical gaussians are a tangent plane, two variances,\n"" one mean and the distance of the point with its nearest neighbors.\n"" The two variances correspond to the shared variance of every manifold directions\n"" and of every noise directions. \n"" This variable is used to do gradient descent on the parameters, but\n"" not to estimate de likelihood of the Markov chain a some point, which is\n"" more complex to estimate.\n")
	NllSemisphericalGaussianVariable *.
	DECLARE_OBJECT_PTR (NllSemisphericalGaussianVariable)
Var	nll_semispherical_gaussian (Var tangent_plane_var, Var mu_var, Var sm_var, Var sn_var, Var neighbors_dist_var, Var p_target_var, Var p_neighbors_var, Var noise, Var mu_noisy, bool use_noise=false, real epsilon=1e-6)
	PLEARN_IMPLEMENT_OBJECT (OneHotSquaredLoss,"Computes sum(square_i(netout[i]-(i==classnum ?hotval :coldval))","NO HELP")
	OneHotSquaredLoss *.
	DECLARE_OBJECT_PTR (OneHotSquaredLoss)
Var	onehot_squared_loss (Var network_output, Var classnum, real coldval=0., real hotval=1.)
	PLEARN_IMPLEMENT_OBJECT (OneHotVariable,"Represents a vector of a given lenth, that has value 1 at the index ""given by another variable and 0 everywhere else","NO HELP")
	OneHotVariable *.
	DECLARE_OBJECT_PTR (OneHotVariable)
Var	onehot (int the_length, Var hotindex, real coldvalue=0.0, real hotvalue=1.0)
	PLEARN_IMPLEMENT_OBJECT (PDistributionVariable,"Variable that represents a random variable according to some PDistribution object","")
	PDistributionVariable *.
	DECLARE_OBJECT_PTR (PDistributionVariable)
	PLEARN_IMPLEMENT_OBJECT (PLogPVariable,"Returns the elementwise x*log(x) in a (hopefully!) numerically stable way","NO HELP")
	PLogPVariable *.
	DECLARE_OBJECT_PTR (PLogPVariable)
Var	plogp (Var v)
	PLEARN_IMPLEMENT_OBJECT (PlusColumnVariable,"Adds a single-column var to each column of a matrix var","NO HELP")
	PlusColumnVariable *.
	DECLARE_OBJECT_PTR (PlusColumnVariable)
	PLEARN_IMPLEMENT_OBJECT (PlusConstantVariable,"Adds a scalar constant to a matrix var","NO HELP")
	PlusConstantVariable *.
	DECLARE_OBJECT_PTR (PlusConstantVariable)
	PLEARN_IMPLEMENT_OBJECT (PlusRowVariable,"Adds a single-row var to each row of a matrix var","NO HELP")
	PlusRowVariable *.
	DECLARE_OBJECT_PTR (PlusRowVariable)
	PLEARN_IMPLEMENT_OBJECT (PlusScalarVariable,"Adds a scalar var to a matrix var","NO HELP")
	PlusScalarVariable *.
	DECLARE_OBJECT_PTR (PlusScalarVariable)
	PLEARN_IMPLEMENT_OBJECT (PlusVariable,"Adds 2 matrix vars of same size","NO HELP")
	PlusVariable *.
	DECLARE_OBJECT_PTR (PlusVariable)
	PLEARN_IMPLEMENT_OBJECT (PowVariable,"Elementwise pow (returns 0 wherever input is negative)","NO HELP")
	PowVariable *.
	DECLARE_OBJECT_PTR (PowVariable)
Var	pow (Var v, real power)
Var	sqrt (Var v)
	PLEARN_IMPLEMENT_OBJECT (PowVariableVariable,"x^y where x and y are variables but y is scalar ""or it has the same size as x","NO HELP")
	PowVariableVariable *.
Var	pow (Var v, Var power)
	PLEARN_IMPLEMENT_OBJECT (ProductTransposeVariable,"Matrix product between matrix1 and transpose of matrix2","NO HELP")
	ProductTransposeVariable *.
	DECLARE_OBJECT_PTR (ProductTransposeVariable)
Var	productTranspose (Var &m1, Var &m2)
	PLEARN_IMPLEMENT_OBJECT (ProductVariable,"Matrix product","NO HELP")
	ProductVariable *.
	DECLARE_OBJECT_PTR (ProductVariable)
Var	product (Var v1, Var v2)
	general matrix product
	PLEARN_IMPLEMENT_OBJECT (ProjectionErrorVariable,"Computes the projection error of a set of vectors on a non-orthogonal basis.\n","The first input is a set of n_dim vectors (possibly seen as a single vector of their concatenation) f_i, each in R^n\n""The second input is a set of T vectors (possibly seen as a single vector of their concatenation) t_j, each in R^n\n""There are several options that control which kind of projection error is actually computed:\n""If !use_subspace_distance {the recommended setting}, the output is\n"" sum_j min_w || t_j - sum_i w_i f_i ||^2 / ||t_j||^2\n"" where the denominator can be eliminated (not recommended) by turning off the\n"" normalize_by_neighbor_distance option. In this expression, w is a local\n"" n_dim-vector that is optmized analytically.\n"" If the 'ordered_vectors' is set, the gradient is not computed truthfully\n"" but in such a way as to induce a natural ordering among the vectors f_i.\n"" For each f_i, the above criterion is applied using a projection that\n"" involves only the first i vectors f_1...f_i. In this way the first vector f_1\n"" tries to *explain* the vectors t_j as well as possible with a single dimension,\n"" and the vector f_2 learns to *explain* what f_2 did not already predict, etc...\n"" When this option is set, we also choose the w_i in the same greedy way, starting\n"" from w_1 chosen to minimize the projection error wrt f_1, w_2 chosen to minimize the\n"" residual projection error left on f_2, etc... Hence the cost minimized wrt f_k on neighbor j is\n"" ||t_j - sum_{i<=k} w_i f_i||^2 / ||t_j||^2\n"" (this cost is minimized to choose w_k, and to get a gradient on f_k as well).\n"" In that case no SVD is used, instead one obtains an analytic solution for w_k:\n"" w_k = (t_j . f_k - sum_{i<k} w_i f_i . f_k)/||f_k||^2.\n"" The output produced by fprop is sum_j || t_j - sum_i w_i f_i ||^2 / ||t_j||^2\n"" where the w_i are chosen as in the previous equation.\n""However, if use_subspace_distance (not recommended), the output is\n"" min_{w,u} || sum_i w_i f_i - sum_j u_j t_j ||^2 .\n""In both cases, if norm_penalization>0, an extra term is added:\n"" norm_penalization * sum_i (||f_i||^2 - 1)^2.\n""The 'epsilon' and 'regularization' options are used to regularize the SVD-based matrix\n""inversion involved in minimizing for w: only the singular values of F' that are\n""above 'epsilon' are inverted (and their singular vectors considered, and then they\n""are incremented by 'regularization' before inverting.\n")
	ProjectionErrorVariable *.
	DECLARE_OBJECT_PTR (ProjectionErrorVariable)
Var	projection_error (Var f, Var t, real norm_penalization=0, int n=-1, bool normalize_by_neighbor_distance=true, bool use_subspace_distance=false, real epsilon=0, real regularization=0, bool ordered_vectors=true)
	PLEARN_IMPLEMENT_OBJECT (ReshapeVariable,"Variable that views another variable, but with a different length() and width() ""(the only restriction being that length()*width() remain the same)","NO HELP")
	ReshapeVariable *.
	DECLARE_OBJECT_PTR (ReshapeVariable)
Var	reshape (Var v, int newlength, int newwidth)
	PLEARN_IMPLEMENT_OBJECT (RightPseudoInverseVariable,"ONE LINE DESCR","NO HELP")
	RightPseudoInverseVariable *.
	DECLARE_OBJECT_PTR (RightPseudoInverseVariable)
Var	rightPseudoInverse (Var v)
	PLEARN_IMPLEMENT_OBJECT (RowAtPositionVariable,"Variables positionned inside a larger zero variable ...","NO HELP")
	RowAtPositionVariable *.
	DECLARE_OBJECT_PTR (RowAtPositionVariable)
	PLEARN_IMPLEMENT_OBJECT (RowSumVariable,"Result is a single column that contains the sum of each row of the input","NO HELP")
	RowSumVariable *.
	DECLARE_OBJECT_PTR (RowSumVariable)
Var	rowSum (Var v)
	PLEARN_IMPLEMENT_OBJECT (SemiSupervisedProbClassCostVariable,"ONE LINE DESCR","NO HELP")
	SemiSupervisedProbClassVariable *.
	DECLARE_OBJECT_PTR (SemiSupervisedProbClassCostVariable)
	PLEARN_IMPLEMENT_OBJECT (SigmoidVariable,"ONE LINE DESCR","NO HELP")
	SigmoidVariable *.
Var	softmax (Var x1, Var x2, Var hardness)
	DECLARE_OBJECT_PTR (SigmoidVariable)
Var	sigmoid (Var v)
	PLEARN_IMPLEMENT_OBJECT (SignVariable,"sign(x) = 1 if x>0, -1 if x<0, 0 if x=0, all done element by element.","NO HELP")
	SignVariable *.
	DECLARE_OBJECT_PTR (SignVariable)
Var	sign (Var input)
	PLEARN_IMPLEMENT_OBJECT (SoftmaxLossVariable,"ONE LINE DESCR","NO HELP")
	SoftmaxLossVariable *.
	DECLARE_OBJECT_PTR (SoftmaxLossVariable)
Var	softmax (Var input, Var index)
	PLEARN_IMPLEMENT_OBJECT (SoftmaxVariable,"ONE LINE DESCR","NO HELP")
	SoftmaxVariable *.
	DECLARE_OBJECT_PTR (SoftmaxVariable)
Var	softmax (Var v)
	PLEARN_IMPLEMENT_OBJECT (SoftplusVariable,"This is the primitive of a sigmoid: log(1+exp(x))","NO HELP")
	SoftplusVariable *.
	DECLARE_OBJECT_PTR (SoftplusVariable)
Var	softplus (Var v)
	PLEARN_IMPLEMENT_OBJECT (SoftSlopeIntegralVariable,"This Var computes the integral of the soft_slope function in an interval.","Compute the integral of soft_slope(x,s,l,r) over x from a to b\n")
	SoftSlopeIntegralVariable *.
	DECLARE_OBJECT_PTR (SoftSlopeIntegralVariable)
Var	soft_slope_integral (Var smoothness, Var left, Var right, real a=0, real b=1)
	PLEARN_IMPLEMENT_OBJECT (SoftSlopeVariable,"This Var computes the soft_slope function","The soft_slope function is a soft version of linear by parts function.\n""(as smoothness goes to infty). More precisely it converges to a function that is\n""0 in [-infty,left], linear in [left,right], and 1 in [right,infty], and continuous\n""It is always monotonically increasing wrt x (positive derivative in x).\n""If the arguments are vectors than the operation is performed element by element on all of them.\n")
	SoftSlopeVariable *.
	DECLARE_OBJECT_PTR (SoftSlopeVariable)
Var	soft_slope (Var x, Var smoothness, Var left, Var right)
Var	d_soft_slope (Var x, Var smoothness, Var left, Var right)
Var	soft_slope_limit (Var x, Var smoothness, Var left, Var right)
	PLEARN_IMPLEMENT_OBJECT (SourceVariable,"ONE LINE DESCR","NO HELP")
	SourceVariable *.
	DECLARE_OBJECT_PTR (SourceVariable)
	PLEARN_IMPLEMENT_OBJECT (SquareRootVariable,"ONE LINE DESCR","NO HELP")
	SquareRootVariable *.
	DECLARE_OBJECT_PTR (SquareRootVariable)
Var	squareroot (Var v)
	PLEARN_IMPLEMENT_OBJECT (SquareVariable,"ONE LINE DESCR","NO HELP")
	SquareVariable *.
	DECLARE_OBJECT_PTR (SquareVariable)
Var	square (Var v)
	PLEARN_IMPLEMENT_OBJECT (SubMatTransposeVariable,"ONE LINE DESCR","NO HELP")
	SubMatTransposeVariable *.
	DECLARE_OBJECT_PTR (SubMatTransposeVariable)
Var	transpose (Var v)
	PLEARN_IMPLEMENT_OBJECT (SubMatVariable,"ONE LINE DESCR","NO HELP")
	SubMatVariable *.
	DECLARE_OBJECT_PTR (SubMatVariable)
Var	subMat (Var v, int i, int j, int l, int w)
	PLEARN_IMPLEMENT_OBJECT (SubsampleVariable,"A subsample var; equals subsample(input, the_subsamplefactor)","NO HELP")
	SubsampleVariable *.
	DECLARE_OBJECT_PTR (SubsampleVariable)
Var	subsample (Var input, int subsample_factor)
	PLEARN_IMPLEMENT_OBJECT (SumAbsVariable,"ONE LINE DESCR","NO HELP")
	SumAbsVariable *.
	DECLARE_OBJECT_PTR (SumAbsVariable)
Var	sumabs (Var v)
	PLEARN_IMPLEMENT_OBJECT (SumOfVariable,"Variable that sums the value of a Func evaluated on each row of a VMat","NO HELP")
	SumOfVariable *.
	DECLARE_OBJECT_PTR (SumOfVariable)
Var	sumOf (VMat distr, Func f, int nsamples)
	sumOf
Var	sumOf (Var output, const VarArray &inputs, VMat distr, int nsamples, VarArray parameters=VarArray())
	deprecated old version do not use!
Var	meanOf (VMat distr, Func f, int nsamples)
	meanOf
Var	meanOf (Var output, const VarArray &inputs, VMat distr, int nsamples, VarArray parameters=VarArray())
	deprecated old version do not use!
	PLEARN_IMPLEMENT_OBJECT (SumOverBagsVariable,"Variable that sums the value of a Func each time evaluated on a subsequence of a VMat\n","returns\n"" Sum_{bags in vmat} f(inputs and targets in bag)\n""(it can average this sum over the number of bags if the 'average' option is set).\n""By convention a bag is a sequence of rows of the vmat in which the last column of the target\n""indicates whether the row is the first one (and/or) the last one, with its two least significant bits:\n"" last_column_of_target == 1 ==> first row\n"" last_column_of_target == 2 ==> last row\n"" last_column_of_target == 0 ==> intermediate row\n"" last_column_of_target == 1+2==3 ==> single-row bag (both first and last).\n""The option n_samples controls how many terms in the sum are considered at a time:\n"" n_samples <= 0: sum over the whole vmat (e.g. for batch gradient computation)\n"" n_samples = 1: sum over a single bag at a time (e.g. for stochastic gradient)\n"" where each fprop or fbprop advances to the next bag\n"" otherwise: sum over n_samples bags at a time (e.g. for min-batch training)\n""The last column of the target is not given in the call to f, but a bag_size input is provided instead.\n""The inputs to f are: (matrix of bag inputs, the bag size, the bag target, [the bag weight])\n""(the bag weight is included only if there are weights in the original VMat).")
	SumOverBagsVariable *.
	DECLARE_OBJECT_PTR (SumOverBagsVariable)
Var	sumOverBags (VMat vmat, Func f, int max_bag_size, int nsamples, bool average=false, bool transpose=false)
	sumOf
	PLEARN_IMPLEMENT_OBJECT (SumSquareVariable,"ONE LINE DESCR","NO HELP")
	SumSquareVariable *.
	DECLARE_OBJECT_PTR (SumSquareVariable)
Var	sumsquare (Var v)
	PLEARN_IMPLEMENT_OBJECT (SumVariable,"ONE LINE DESCR","NO HELP")
	SumVariable *.
	DECLARE_OBJECT_PTR (SumVariable)
Var	sum (Var v)
	PLEARN_IMPLEMENT_OBJECT (TanhVariable,"ONE LINE DESCR","NO HELP")
	TanhVariable *.
	DECLARE_OBJECT_PTR (TanhVariable)
Var	tanh (Var v)
	PLEARN_IMPLEMENT_OBJECT (TimesColumnVariable,"Multiplies each column of a matrix var elementwise with a single column variable","NO HELP")
	TimesColumnVariable *.
	DECLARE_OBJECT_PTR (TimesColumnVariable)
	PLEARN_IMPLEMENT_OBJECT (TimesConstantVariable,"Multiplies a matrix var by a scalar constant","NO HELP")
	TimesConstantVariable *.
	DECLARE_OBJECT_PTR (TimesConstantVariable)
	PLEARN_IMPLEMENT_OBJECT (TimesRowVariable,"Multiplies each row of a matrix var elementwise with a single row variable","NO HELP")
	TimesRowVariable *.
	DECLARE_OBJECT_PTR (TimesRowVariable)
	PLEARN_IMPLEMENT_OBJECT (TimesScalarVariable,"Multiplies a matrix var by a scalar var","NO HELP")
	TimesScalarVariable *.
	DECLARE_OBJECT_PTR (TimesScalarVariable)
Var	timesScalar (Var v, Var scalar)
	PLEARN_IMPLEMENT_OBJECT (TimesVariable,"Multiplies 2 matrix vars of same size elementwise","NO HELP")
	TimesVariable *.
	DECLARE_OBJECT_PTR (TimesVariable)
Var	times (Var v, Var w)
	PLEARN_IMPLEMENT_OBJECT (TransposeProductVariable,"Matrix product between transpose of matrix1 and matrix2","NO HELP")
	TransposeProductVariable *.
	DECLARE_OBJECT_PTR (TransposeProductVariable)
Var	transposeProduct (Var &m1, Var &m2)
	PLEARN_IMPLEMENT_OBJECT (UnaryHardSlopeVariable,"Hard slope function whose Var input is only the argument of the function.","Maps x (elementwise) to 0 if x<left, 1 if x>right, and linear in between otherwise.")
	UnaryHardSlopeVariable *.
	DECLARE_OBJECT_PTR (UnaryHardSlopeVariable)
Var	unary_hard_slope (Var v, real l=-1, real r=1)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (UnaryVariable,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (UnequalConstantVariable,"A scalar var; equal 1 if input1!=c, 0 otherwise","NO HELP")
	UnequalConstantVariable *.
	DECLARE_OBJECT_PTR (UnequalConstantVariable)
Var	operator!= (Var v1, real cte)
	result[i] = 1 if v1[i]!=cte, 0 otherwise
Var	operator!= (real cte, Var v1)
	result[i] = 1 if v1[i]!=cte, 0 otherwise
	PLEARN_IMPLEMENT_OBJECT (UnfoldedFuncVariable,"Variable that puts in the rows of its output matrix the value\n""of a Func evaluated on each row of an input matrix.\n","The input_matrix and output matrix have n_unfold rows. A separate propagation path\n""is created that maps (using the Func as a template) each input row to each output row.\n""The parents of this variable include the non-input parents of the Func.\n")
	UnfoldedFuncVariable *.
	DECLARE_OBJECT_PTR (UnfoldedFuncVariable)
Var	unfoldedFunc (Var input_matrix, Func f, bool transpose=false)
	PLEARN_IMPLEMENT_OBJECT (UnfoldedSumOfVariable,"Variable that sums the value of a Func evaluated on each row of a matrix.\n","However, unlike the SumOfVariable, it does so by unfolding the Func (up to given maximum number\n""of times 'max_bag_size'), and it allows that number to be variable. Each of the unfolded Func\n""is applied on a different row of the input matrix. The number of rows to sum is specified on the\n""fly by another input, the bag_size.\n")
	UnfoldedSumOfVariable *.
	DECLARE_OBJECT_PTR (UnfoldedSumOfVariable)
Var	unfoldedSumOf (Var input_matrix, Var bag_size, Func f, int max_bag_size)
ostream &	operator<< (ostream &out, const Var &v)
Var	var (real init_value)
template<> void	deepCopyField (Var &field, CopiesMap &copies)
	Specialized in order to display a warning message.
Var	operator+ (Var v, real cte)
Var	operator+ (real cte, Var v)
Var	operator- (Var v, real cte)
Var	operator+ (Var v1, Var v2)
void	operator+= (Var &v1, const Var &v2)
Var	operator- (Var v1, Var v2)
Var	operator- (Var v)
void	operator-= (Var &v1, const Var &v2)
Var	operator- (real cte, Var v)
Var	operator * (Var v, real cte)
Var	operator * (real cte, Var v)
Var	operator * (Var v1, Var v2)
	element-wise multiplications
Var	operator/ (Var v, real cte)
Var	operator/ (real cte, Var v)
Var	operator/ (Var v1, Var v2)
Var	operator== (Var v1, Var v2)
Var	operator!= (Var v1, Var v2)
Var	isdifferent (Var v1, Var v2)
Var	mean (Var v)
Var	neg_log_pi (Var p, Var index)
Var	softmax (Var input, int index)
Var	pownorm (Var input, real n)
Var	norm (Var input, real n)
Var	entropy (Var v, bool normalize)
Var	distance (Var input1, Var input2, real n)
Var	powdistance (Var input1, Var input2, real n)
VarArray	propagationPath (const VarArray &inputs, const VarArray &outputs)
	The function that computes a propagation path *.
VarArray	propagationPath (const VarArray &outputs)
	returns the propagationpath going from all sources that influence the outputs to the outputs.
VarArray	propagationPathToParentsOfPath (const VarArray &inputs, const VarArray &outputs)
	from all sources to all direct non-inputs parents of the path inputs-->outputs
VarArray	nonInputParentsOfPath (VarArray inputs, VarArray outputs)
	Isn't this useless? as we have a constructor of VarArray from Var that should be called automatically !!!???? (Pascal).
VarArray	allSources (const VarArray &v)
	returns all sources that influence the given vars
VarArray	operator- (const VarArray &a, const VarArray &b)
	returns all variables of a that are not in b
VarArray	nonInputSources (const VarArray &inputs, const VarArray &outputs)
	returns all sources that influence outputs except those that influence it only through inputs
void	operator<< (VarArray &ar, const Array< Vec > &values)
void	operator>> (VarArray &ar, const Array< Vec > &values)
void	printInfo (VarArray &a)
void	printInfo (VarArray inputs, const Var &output, bool show_gradients)
template<> void	deepCopyField (VarArray &field, CopiesMap &copies)
void	operator<< (VarArray &ar, const Vec &datavec)
void	operator>> (VarArray &ar, const Vec &datavec)
VarArray	operator & (Var v1, Var v2)
	DECLARE_TYPE_TRAITS (VarArray)
PStream &	operator>> (PStream &in, VarArray &o)
PStream &	operator<< (PStream &out, const VarArray &o)
	PLEARN_IMPLEMENT_OBJECT (VarArrayElementVariable,"Selects one element of a VarArray according to a Var index */","NO HELP")
	VarArrayElementVariable *.
	DECLARE_OBJECT_PTR (VarArrayElementVariable)
	PLEARN_IMPLEMENT_OBJECT (VarColumnsVariable,"ONE LINE DESCR","NO HELP")
	VarColumnsVariable *.
	DECLARE_OBJECT_PTR (VarColumnsVariable)
	PLEARN_IMPLEMENT_OBJECT (VarElementVariable,"ONE LINE DESCR","NO HELP")
	VarElementVariable *.
	DECLARE_OBJECT_PTR (VarElementVariable)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (Variable,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (Variable)
	DECLARE_OBJECT_PP (Var, Variable)
	PLEARN_IMPLEMENT_OBJECT (VarRowsVariable,"Variable that is a subset of a matrix's rows; ""input1 : matrix from which rows are selected; ""input2 : vector whose elements are row indices in input1","NO HELP")
	VarRowsVariable *.
	DECLARE_OBJECT_PTR (VarRowsVariable)
	PLEARN_IMPLEMENT_OBJECT (VarRowVariable,"Variable that is the row of the input1 variable indexed by the input2 variable","NO HELP")
	VarRowVariable *.
	DECLARE_OBJECT_PTR (VarRowVariable)
	PLEARN_IMPLEMENT_OBJECT (VecElementVariable,"Variable that is the element of vector vec indexed by variable input","NO HELP")
	VecElementVariable *.
	DECLARE_OBJECT_PTR (VecElementVariable)
Var	accessElement (const Vec &v, Var index)
	PLEARN_IMPLEMENT_OBJECT (WeightedSumSquareVariable,"ONE LINE DESCR","NO HELP")
	WeightedSumSquareVariable *.
	DECLARE_OBJECT_PTR (WeightedSumSquareVariable)
Var	weighted_sumsquare (Var v, Var w)
	PLEARN_IMPLEMENT_OBJECT (AsciiVMatrix,"ONE LINE DESCR","AsciiVMatrix implements a file in ascii format")
	DECLARE_OBJECT_PTR (AsciiVMatrix)
	PLEARN_IMPLEMENT_OBJECT (AutoVMatrix,"Automatically builds an appropriate VMat given its specification.","AutoVMatrix tries to interpret the given 'specification' (it will call getDataSet) and\n""will be a wrapper around the appropriate VMatrix type, simply forwarding calls to it.\n""AutoVMatrix can be used to access the UCI databases.\n")
	DECLARE_OBJECT_PTR (AutoVMatrix)
	PLEARN_IMPLEMENT_OBJECT (BatchVMatrix,"ONE LINE DESCR","VMat class that replicates small parts of a matrix (mini-batches), so that each mini-batch appears twice (consecutively).")
	DECLARE_OBJECT_PTR (BatchVMatrix)
	PLEARN_IMPLEMENT_OBJECT (BootstrapSplitter,"A splitter whose splits are bootstrap samples of the original dataset","BootstrapSplitter implements a ...")
	DECLARE_OBJECT_PTR (BootstrapSplitter)
	PLEARN_IMPLEMENT_OBJECT (BootstrapVMatrix,"A VMatrix that sees a bootstrap subset of its parent VMatrix.\n""This is not a real bootstrap since a sample can only appear once.","")
	BootstrapVMatrix *.
	DECLARE_OBJECT_PTR (BootstrapVMatrix)
	PLEARN_IMPLEMENT_OBJECT (ByteMemoryVMatrix,"ONE_LINE_DESCR","ONE LINE HELP")
	DECLARE_OBJECT_PTR (ByteMemoryVMatrix)
	PLEARN_IMPLEMENT_OBJECT (CenteredVMatrix,"A VMatrix that centers a dataset.","The empirical mean is subtracted to each row of the underlying VMat.")
	DECLARE_OBJECT_PTR (CenteredVMatrix)
	PLEARN_IMPLEMENT_OBJECT (CompressedVMatrix,"ONE LINE DESCR","ONE LINE HELP")
	DECLARE_OBJECT_PTR (CompressedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (ConcatColumnsVMatrix,"ONE LINE DESCR","NO HELP")
	ConcatColumnsVMatrix *.
	DECLARE_OBJECT_PTR (ConcatColumnsVMatrix)
VMat	hconcat (VMat d1, VMat d2)
VMat	hconcat (Array< VMat > ds)
	PLEARN_IMPLEMENT_OBJECT (ConcatRowsSubVMatrix,"ONE LINE DESC","ONE LINE HELP")
	ConcatRowsSubVMatrix *.
	DECLARE_OBJECT_PTR (ConcatRowsSubVMatrix)
	PLEARN_IMPLEMENT_OBJECT (ConcatRowsVMatrix,"Concatenates the rows of a number of VMat.","It can also be used to select fields which are common to those VMat,\n""using the 'only_common_fields' option.\n""Otherwise, the fields are just assumed to be those of the first VMat.\n")
	ConcatRowsVMatrix *.
	DECLARE_OBJECT_PTR (ConcatRowsVMatrix)
VMat	vconcat (VMat d1, VMat d2)
VMat	vconcat (Array< VMat > ds)
	PLEARN_IMPLEMENT_OBJECT (CrossReferenceVMatrix,"ONE LINE DESC","ONE LINE HELP")
	CrossReferenceVMatrix *.
	DECLARE_OBJECT_PTR (CrossReferenceVMatrix)
	PLEARN_IMPLEMENT_OBJECT (CumVMatrix,"Add columns that a cumulated values of given columns","The user specifies one or more columns and for each such <column-name>\n""a cum-<column-name> column is created which will contain the sum from row 0\n""to the current row of <column-name>.\n")
	DECLARE_OBJECT_PTR (CumVMatrix)
	PLEARN_IMPLEMENT_OBJECT (DatedJoinVMatrix,"Join two vmatrices, taking into account a date field.","The two vmatrices play an asymmetric role. They are called\n""master and slave. The resulting vmatrix has one row for each row\n""of the master vmatrix (or optionally of the slave vmatrix). Its\n""columns are a concatenation of selected columns of the master vmatrix\n""and of selected columns of the slave which 'match' according to a rule\n""(always in the order: master fields, slave fields). Matchint is\n""obtained using shared 'key fields'. Optionally, for matching, a date field\n""in the master is forced to belong to a date interval in the slave,\n""as follows: slave_date_start < master_date <= slave_date_end.\n""If no match is found then the master (or slave) columns are left with missing values.\n""If more than one slave row matches, then the one with the latest\n""slave_date_start is used (and a warning is optionally issued). If\n""no slave_date_start field is provided then no date constraint is\n""enforced, and the last key-matching slave row is matched to a master row.\n""An option (output_the_slave) allows to output one row for each slave row\n""instead of the default which outputs one row for each master row.\n""Note that if (output_the_slave) then the non-matching master rows are 'lost'\n""whereas if (!output_the_slave) then the non-matching slave rows are 'lost'.\n""If output_the_slave and more than one master row matches with a given slave_row\n""then the SUM of the master fields is computed (i.e. be careful that their sum is meaningful)\n")
	DECLARE_OBJECT_PTR (DatedJoinVMatrix)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (DatedVMatrix,"ONE_LINE_DESC","ONE_LINE_HELP")
	DatedVMatrix *.
	DECLARE_OBJECT_PTR (DatedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (DBSplitter,"A Splitter that contains several databases.","The databases to be used can be specified with the 'databases' option. ")
	DECLARE_OBJECT_PTR (DBSplitter)
	PLEARN_IMPLEMENT_OBJECT (DiskVMatrix,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (DiskVMatrix)
	PLEARN_IMPLEMENT_OBJECT (ExplicitSplitter,"ONE LINE DESCR","ExplicitSplitter allows you to define a 'splitter' by giving explicitly the datasets for each split\n""as a matrix VMatrices.\n""(This splitter in effect ignores the 'dataset' it is given with setDataSet) \n")
	DECLARE_OBJECT_PTR (ExplicitSplitter)
	PLEARN_IMPLEMENT_OBJECT (ExtendedVMatrix,"ONE_LINE_DESC","ONE_LINE_HELP")
	ExtendedVMatrix *.
	DECLARE_OBJECT_PTR (ExtendedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (FileVMatrix,"ONE LINE DESCR","NO HELP")
	FileVMatrix *.
int	strlen (char *s)
	DECLARE_OBJECT_PTR (FileVMatrix)
	PLEARN_IMPLEMENT_OBJECT (FilteredVMatrix,"A filtered view of its source vmatrix","The filter is an exression in VPL language.\n""The filtered indexes are saved in the metadata directory, that NEEDS to\n""be provided.\n")
	DECLARE_OBJECT_PTR (FilteredVMatrix)
	PLEARN_IMPLEMENT_OBJECT (FilterSplitter,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (FilterSplitter)
	PLEARN_IMPLEMENT_OBJECT (FinancePreprocVMatrix,"ONE LINE DESCR","FinancePreprocVMatrix implements a VMatrix with extra preprocessing columns.")
	DECLARE_OBJECT_PTR (FinancePreprocVMatrix)
	PLEARN_IMPLEMENT_OBJECT (ForwardVMatrix,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (ForwardVMatrix)
	PLEARN_IMPLEMENT_OBJECT (FractionSplitter,"A Splitter that can extract several subparts of a dataset in each split.","Ranges of the dataset are specified explicitly as start:end positions,\n""that can be absolute or relative to the number of samples in the training set.")
	DECLARE_OBJECT_PTR (FractionSplitter)
	PLEARN_IMPLEMENT_OBJECT (GeneralizedOneHotVMatrix,"ONE LINE DESC","ONE LINE HELP")
	GeneralizedOneHotVMatrix *.
	DECLARE_OBJECT_PTR (GeneralizedOneHotVMatrix)
	PLEARN_IMPLEMENT_OBJECT (GetInputVMatrix,"This VMatrix only sees the input part of its source VMatrix.","")
	DECLARE_OBJECT_PTR (GetInputVMatrix)
	PLEARN_IMPLEMENT_OBJECT (GramVMatrix,"Computes the Gram matrix of a given kernel.","Currently, this class inherits from a MemoryVMatrix, and the Gram matrix\n""is stored in memory.\n")
	DECLARE_OBJECT_PTR (GramVMatrix)
	PLEARN_IMPLEMENT_OBJECT (IndexedVMatrix,"ONE LINE DESCR"," VMat class that sees a matrix as a collection of triplets (row, column, value)\n""Thus it is a N x 3 matrix, with N = the number of elements in the original matrix.\n")
	DECLARE_OBJECT_PTR (IndexedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (InterleaveVMatrix,"ONE LINE DESC","ONE LINE HELP")
	InterleaveVMatrix *.
	DECLARE_OBJECT_PTR (InterleaveVMatrix)
	PLEARN_IMPLEMENT_OBJECT (JoinVMatrix,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (JoinVMatrix)
	PLEARN_IMPLEMENT_OBJECT (JulianizeVMatrix,"ONE LINE DESCR","JulianizeVMatrix provides a conversion from a VMat containing dates\n""in an explicit 3-column (YYYY,MM,DD) or 6-column (YYYY,MM,DD,HH,MM,SS)\n""format to a Julian day number format (including fractional part to\n""represent the hour within the day). The dates can be at any columns,\n""not only columns 0-2 (or 0-5). More than a single date can be\n""converted.\n")
	DECLARE_OBJECT_PTR (JulianizeVMatrix)
	PLEARN_IMPLEMENT_OBJECT (KernelVMatrix,"ONE LINE DESC","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (KFoldSplitter,"K-fold cross-validation splitter.","KFoldSplitter implements K splits of the dataset into a training-set and a test-set.\n""If the number of splits is higher than the number of examples, leave-one-out cross-validation\n""will be performed.")
	DECLARE_OBJECT_PTR (KFoldSplitter)
	PLEARN_IMPLEMENT_OBJECT (KNNVMatrix,"A VMatrix that sees the nearest neighbours of each sample in the source VMat.","Each sample is followed by its (knn-1) nearest neighbours.\n""To each row is appended an additional target, which is:\n"" - 1 if it is the first of a bag of neighbours,\n"" - 2 if it is the last of a bag,\n"" - 0 if it is none of these,\n"" - 3 if it is both (only for knn == 1).\n""In addition, if a kernel_pij kernel is provided,, in the input part of the VMatrix\n""is appended p_ij, where\n"" p_ij = K(x_i,x_j) / \\sum_{k \\in knn(i), k != i} K(x_i,x_k)\n""where K = kernel_pij, and j != i (for j == i, p_ij = -1).")
	DECLARE_OBJECT_PTR (KNNVMatrix)
	PLEARN_IMPLEMENT_OBJECT (LearnerProcessedVMatrix,"ONE LINE DESCR","LearnerProcessedVMatrix implements a VMatrix processed on the fly by a learner (which will optionally be first trained on the source vmatrix)")
	DECLARE_OBJECT_PTR (LearnerProcessedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (LocalNeighborsDifferencesVMatrix,"Computes the difference between each input row and its nearest neighbors.","For each row x of the source VMatrix, the resulting row will be the\n""concatenation of n_neighbors vectors, each of which is the difference\n""between one of the nearest neighbors of x in the source and x itself.\n")
	DECLARE_OBJECT_PTR (LocalNeighborsDifferencesVMatrix)
VMat	local_neighbors_differences (VMat source, int n_neighbors, bool concat=false, bool append_indexes=false)
	PLEARN_IMPLEMENT_OBJECT (MemoryVMatrix,"A VMatrix whose data is stored in memory.","The data can either be given directly by a Mat, or by another VMat that\n""will be precomputed in memory at build time.\n")
	MemoryVMatrix *.
	DECLARE_OBJECT_PTR (MemoryVMatrix)
	PLEARN_IMPLEMENT_OBJECT (MovingAverageVMatrix,"Perform moving average of given columns","The user specifies one or more columns and for each such <column-name>\n""a moving average window size: a ma<windowsize>-<column-name> column is\n""created which will contain at row t the moving average from row t-<windowsize>+1\n""to t inclusively of <column-name>.\n")
	DECLARE_OBJECT_PTR (MovingAverageVMatrix)
	Declares a few other classes and functions related to this class.
	PLEARN_IMPLEMENT_OBJECT (MultiInstanceVMatrix,"Virtual Matrix for a multi instance dataset","In a multi-instance dataset examples come in 'bags' with only one target label\n""for each bag. This class is built upon a source text file that describes such\n""a dataset (see the help on the 'filename' option for format details).\n""The resulting VMatrix shows the following structure in its rows, with\n""all the rows of a bag being consecutive. Each row represents an instance and has:\n"" - the input features for the instance\n"" - the bag's source_targetsize target values (repeated over bag instances)\n"" - a bag signal integer that identifies the beginning and end of the bag:\n"" 1 means the first instance of the bag\n"" 2 means the last instance of the bag\n"" 3 is for a bag with a single row (= 1+2)\n"" 0 is for intermediate instances.\n""The targetsize of the VMatrix is automatically set to source_targetsize+1\n""since the bag_signal is included (appended) in the target vector\n")
	DECLARE_OBJECT_PTR (MultiInstanceVMatrix)
	PLEARN_IMPLEMENT_OBJECT (OneHotVMatrix,"ONE LINE DESC","NO HELP")
	OneHotVMatrix *.
VMat	onehot (VMat d, int nclasses, real cold_value=0.0, real hot_value=1.0)
	DECLARE_OBJECT_PTR (OneHotVMatrix)
	PLEARN_IMPLEMENT_OBJECT (PairsVMatrix,"ONE LINE DESC","NO HELP")
	PairsVMatrix *.
	DECLARE_OBJECT_PTR (PairsVMatrix)
	PLEARN_IMPLEMENT_OBJECT (PLearnerOutputVMatrix,"Use a PLearner (or a set of them) to transform the input part of a data set into the learners outputs","The input part of this VMatrix is obtained from the input part an original data set on which\n""one or more PLearner's computeOutput method is applied. The other columns of the original data set\n""are copied as is. Optionally, the raw input can be copied as well\n""always in the input part of the new VMatrix. The order of the elements of a new row is as follows:\n"" - the outputs of the learners (concatenated) when applied on the input part of the original data,\n"" - optionally, the raw input part of the original data,\n"" - all the non-input columns of the original data.")
	DECLARE_OBJECT_PTR (PLearnerOutputVMatrix)
	PLEARN_IMPLEMENT_OBJECT (PrecomputedVMatrix,"VMatrix that caches (pre-computes on disk) the content of a source vmatrix","This sub-class of SourceVMatrix pre-computes the content of a source vmatrix\n""in a dmat or pmat file. The name of the disk file is obtained from the metadatadir option\n""followed by precomp.dmat or precomp.pmat")
	DECLARE_OBJECT_PTR (PrecomputedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (ProcessingVMatrix,"ONE LINE DESCRIPTION","MULTI-LINE \nHELP")
	DECLARE_OBJECT_PTR (ProcessingVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RangeVMatrix,"ONE LINE DESC","NO HELP")
	Range VMatrix *.
VMat	vrange (real start, real end, real step=1.0)
	DECLARE_OBJECT_PTR (RangeVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RegularGridVMatrix,"ONE LINE DESCR","RegularGridVMatrix represents the list of coordinates along a regularly spaced grid.")
	DECLARE_OBJECT_PTR (RegularGridVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RemapLastColumnVMatrix,"ONE LINE DESC","NO HELP")
	RemapLastColumnVMatrix *.
VMat	remapLastColumn (VMat d, Mat mapping)
VMat	remapLastColumn (VMat d, real if_equals_value, real then_value=1.0, real else_value=-1.0)
	DECLARE_OBJECT_PTR (RemapLastColumnVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RemoveDuplicateVMatrix,"A VMatrix that removes any duplicated entry in its source VMat.","")
	DECLARE_OBJECT_PTR (RemoveDuplicateVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RemoveRowsVMatrix,"ONE LINE DESC","NO HELP")
	RemoveRowsVMatrix *.
VMat	removeRows (VMat d, Vec rownums)
VMat	removeRow (VMat d, int rownum)
	DECLARE_OBJECT_PTR (RemoveRowsVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RepeatSplitter,"Repeat a given splitter a certain amount of times, with the possibility to\n""shuffle randomly the dataset each time","NO HELP")
	DECLARE_OBJECT_PTR (RepeatSplitter)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (RowBufferedVMatrix,"A base class for VMatrices that keep the last row(s) in a buffer for faster access.","")
	RowBufferedVMatrix *.
	DECLARE_OBJECT_PTR (RowBufferedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (RowsSubVMatrix,"ONE LINE DESCRIPTION","MULTI-LINE \nHELP")
	DECLARE_OBJECT_PTR (RowsSubVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SelectColumnsVMatrix,"Selects variables from a source matrix according to given vector of indices.","Alternatively, the variables can be given by their names.")
	SelectColumnsVMatrix *.
	DECLARE_OBJECT_PTR (SelectColumnsVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SelectRowsFileIndexVMatrix,"ONE LINE DESC","NO HELP")
	SelectRowsFileIndexVMatrix *.
	DECLARE_OBJECT_PTR (SelectRowsFileIndexVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SelectRowsVMatrix,"VMat class that selects samples from a source matrix according to given vector of indices.","")
	SelectRowsVMatrix *.
	DECLARE_OBJECT_PTR (SelectRowsVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SequentialSplitter,"ONE LINE DESCR","SequentialSplitter implements several splits, TODO: Comments")
	DECLARE_OBJECT_PTR (SequentialSplitter)
	PLEARN_IMPLEMENT_OBJECT (ShiftAndRescaleVMatrix,"ONE LINE DESCR","ShiftAndRescaleVMatrix allows to shift and scale the first n_inputs columns of an underlying_vm.\n")
	ShiftAndRescaleVMatrix *.
	DECLARE_OBJECT_PTR (ShiftAndRescaleVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SortRowsVMatrix,"Sort the samples of a VMatrix according to one (or more) given columns.","The implementation is not efficient at all, feel free to improve it !")
	SortRowsVMatrix *.
	DECLARE_OBJECT_PTR (SortRowsVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SourceVMatrix,"Super-class for VMatrices that point to another one (the source vmatrix)","")
	DECLARE_OBJECT_PTR (SourceVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SourceVMatrixSplitter,"Returns the splits of an underlying splitter, seen by a SourceVMatrix.","")
	PLEARN_IMPLEMENT_OBJECT (SparseVMatrix,"ONE LINE DESC","NO HELP")
	SparseVMatrix *.
	DECLARE_OBJECT_PTR (SparseVMatrix)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (Splitter,"ONE LINE DESCR","NO HELP")
void	split (VMat d, real test_fraction, VMat &train, VMat &test, int i, bool use_all)
Vec	randomSplit (VMat d, real test_fraction, VMat &train, VMat &test)
void	split (VMat d, real validation_fraction, real test_fraction, VMat &train, VMat &valid, VMat &test, bool do_shuffle)
	Splits the dataset d into 3 subsets.
void	randomSplit (VMat d, real validation_fraction, real test_fraction, VMat &train, VMat &valid, VMat &test)
	Splits the dataset d into 3 subsets (similar to above).
	DECLARE_OBJECT_PTR (Splitter)
	PLEARN_IMPLEMENT_OBJECT (StrTableVMatrix,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (StrTableVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SubInputVMatrix,"A VMat that only takes part of the input of its source VMat.","This can be useful for instance to only take the first k components\n""after applying some dimensionality reduction method.")
	DECLARE_OBJECT_PTR (SubInputVMatrix)
	PLEARN_IMPLEMENT_OBJECT (SubVMatrix,"ONE LINE DESCR","NO HELP")
	SubVMatrix *.
	DECLARE_OBJECT_PTR (SubVMatrix)
	PLEARN_IMPLEMENT_OBJECT (TemporalHorizonVMatrix,"ONE LINE DESCR"," VMat class that delay the last entries of an underlying VMat by a certain horizon.\n")
	TemporalHorizonVMatrix *.
	DECLARE_OBJECT_PTR (TemporalHorizonVMatrix)
	PLEARN_IMPLEMENT_OBJECT (TestInTrainSplitter,"A splitter that adds the test points given by another splitter into the training set.","The underlying splitter should return train / test sets of constant size.\n""For instance, if the underlying splitter returns 3 splits of (train,test)\n""pairs with size 2000 and 500, this splitter will return:\n"" - for 'percentage_added' == 5%, 15 splits of size 2100 and 100, with each\n"" test point appearing once and only once in a train set and a test set\n"" - for 'percentage_added' == 20%, 6 splits of size 2400,400 and 2400,100, with\n"" each test point appearing once or more in a train set, and only once in a\n"" test set (note that the test points appearing more than once in a train set\n"" will be those at the beginning of the test sets returned by the underlying\n"" splitter)\n")
	DECLARE_OBJECT_PTR (TestInTrainSplitter)
VMat	thresholdVMat (VMat d, real threshold, real cold_value=0.0, real hot_value=1.0, bool gt_threshold=true)
	PLEARN_IMPLEMENT_OBJECT (ToBagSplitter,"A Splitter that makes any existing splitter operate on bags only.","The dataset provided must contain bag information, as described in\n""SumOverBagsVariable")
	DECLARE_OBJECT_PTR (ToBagSplitter)
	PLEARN_IMPLEMENT_OBJECT (TrainTestBagsSplitter,"Splits a dataset in two parts","TrainTestBagsSplitter implements a single split of the dataset into\n""a training set and a test set (the test part being the last few samples of the dataset)\n""Optionally a third set is provided which is the training set itself (in order to test on it)\n")
	DECLARE_OBJECT_PTR (TrainTestBagsSplitter)
	PLEARN_IMPLEMENT_OBJECT (TrainTestSplitter,"ONE LINE DESCR","TrainTestSplitter implements a single split of the dataset into a training-set and a test-set (the test part being the last few samples of the dataset)")
	DECLARE_OBJECT_PTR (TrainTestSplitter)
	PLEARN_IMPLEMENT_OBJECT (TrainValidTestSplitter,"This splitter will basically return [Train+Valid, Test].","The train test returned by the splitter is formed from the first n_train+n_valid\n""samples in the dataset. The other samples are returned in the test set.\n""The validation and test sets (given by the samples after the n_train-th one) can\n""be shuffled in order to get a different validation and test sets at each split.\n""However, the train set (the first n_train samples) remains fixed.")
	DECLARE_OBJECT_PTR (TrainValidTestSplitter)
	PLEARN_IMPLEMENT_OBJECT (TransposeVMatrix,"A VMatrix that sees the transpose of another VMatrix.","")
	DECLARE_OBJECT_PTR (TransposeVMatrix)
	PLEARN_IMPLEMENT_OBJECT (UniformizeVMatrix,"ONE LINE DESC","NO HELP")
	UniformizeVMatrix *.
	DECLARE_OBJECT_PTR (UniformizeVMatrix)
	PLEARN_IMPLEMENT_OBJECT (UniformVMatrix,"ONE LINE DESC","NO HELP")
	Uniform VMatrix *.
	DECLARE_OBJECT_PTR (UniformVMatrix)
	PLEARN_IMPLEMENT_OBJECT (UpsideDownVMatrix,"ONE LINE DESCRIPTION","MULTI-LINE \nHELP")
	DECLARE_OBJECT_PTR (UpsideDownVMatrix)
	PLEARN_IMPLEMENT_OBJECT (VecExtendedVMatrix,"ONE LINE DESC","NO HELP")
	VecExtendedVMatrix *.
	DECLARE_OBJECT_PTR (VecExtendedVMatrix)
template<> void	deepCopyField (VMat &field, CopiesMap &copies)
VMat	loadAsciiAsVMat (const string &filename)
	Load an ASCII file and return the corresponding VMat (this will be a MemoryVMatrix, since the entire file is loaded in memory).
	DECLARE_OBJECT_PP (VMat, VMatrix)
void	operator<< (const Mat &dest, const VMatrix &src)
void	operator>> (const VMatrix &src, const Mat &dest)
void	operator<< (const Mat &dest, const VMat &src)
void	operator>> (const VMat &src, const Mat &dest)
Array< VMat >	operator & (const VMat &d1, const VMat &d2)
ostream &	operator<< (ostream &out, const VMat &m)
void	computeWeightedMean (Vec weights, VMat d, Vec &meanvec)
	Statistics functions *.
void	computeRange (VMat d, Vec &minvec, Vec &maxvec)
void	computeRowMean (VMat d, Vec &meanvec)
void	computeMean (VMat d, Vec &meanvec)
Mat	computeBasicStats (VMat m)
void	computeStats (VMat m, VecStatsCollector &st, bool report_progress)
TVec< StatsCollector >	computeStats (VMat m, int maxnvalues, bool report_progress)
	Retirns the unconditional statistics of each field.
PP< ConditionalStatsCollector >	computeConditionalStats (VMat m, int condfield, TVec< RealMapping > ranges)
	returns the cooccurence statistics conditioned on the given field
Array< Mat >	computeConditionalMeans (VMat trainset, int targetsize, Mat &basic_stats)
void	computeMeanAndVariance (VMat d, Vec &meanvec, Vec &variancevec)
void	computeInputMean (VMat d, Vec &meanvec)
	Computes the (possibly weighted) mean and covariance of the input part of the dataset.
void	computeInputMeanAndCovar (VMat d, Vec &meanvec, Mat &covarmat)
void	computeInputMeanAndVariance (VMat d, Vec &meanvec, Vec &var)
void	computeWeightedMeanAndCovar (Vec weights, VMat d, Vec &meanvec, Mat &covarmat)
real	computeWeightedMeanAndCovar (VMat d, Vec &meanvec, Mat &covarmat, real threshold)
	Last column of d is supposed to contain the weight for each sample Samples with a weight less or equal to threshold will be ignored (returns the sum of all the weights actually used).
void	computeMeanAndCovar (VMat m, Vec &meanvec, Mat &covarmat, ostream &logstream)
	computes empirical mean and covariance in a single pass
void	computeMeanAndStddev (VMat d, Vec &meanvec, Vec &stddevvec)
void	autocorrelation_function (const VMat &data, Mat &acf)
VMat	normalize (VMat d, Vec meanvec, Vec stddevvec)
VMat	normalize (VMat d, int inputsize, int ntrain)
	Here, mean and stddev are estimated on d.subMat(0,0,ntrain,inputsize).
VMat	grep (VMat d, int col, Vec values, bool exclude)
map< real, int >	countOccurencesInColumn (VMat m, int col)
	returns a map mapping all different values appearing in column col to their number of occurences
map< real, TVec< int > >	indicesOfOccurencesInColumn (VMat m, int col)
	returns a map mapping all different values appearing in column col to a vector of the corresponding row indices in the VMat (this proceeds in 2 passes, first calling countOccurencesInColumn to allocate the exact memory)
VMat	grep (VMat d, int col, Vec values, const string &indexfile, bool exclude)
VMat	filter (VMat d, const string &indexfile)
VMat	shuffle (VMat d)
	returns a SelectRowsVMatrix that has d's rows shuffled
VMat	bootstrap (VMat d, bool reorder, bool norepeat)
	returns a SelectRowsVMatrix that has d's rows bootstrapped (sample with replacement and optionally re-ordered).
Mat	transposeProduct (VMat m)
	computes M'.M
Mat	transposeProduct (VMat m1, VMat m2)
	computes M1'.M2
Vec	transposeProduct (VMat m1, Vec v2)
	computes M1'.V2
Mat	productTranspose (VMat m1, VMat m2)
	computes M1.M2'
Mat	product (Mat m1, VMat m2)
	computes M1.M2
VMat	transpose (VMat m1)
	returns M1'
real	linearRegression (VMat inputs, VMat outputs, real weight_decay, Mat theta_t, bool use_precomputed_XtX_XtY, Mat XtX, Mat XtY, real &sum_squared_Y, bool return_squared_loss, int verbose_every, bool cholesky)
Mat	linearRegression (VMat inputs, VMat outputs, real weight_decay)
	Version that does all the memory allocations of XtX, XtY and theta_t. Returns theta_t.
real	weightedLinearRegression (VMat inputs, VMat outputs, VMat gammas, real weight_decay, Mat theta_t, bool use_precomputed_XtX_XtY, Mat XtX, Mat XtY, real &sum_squared_Y, real &sum_gammas, bool return_squared_loss, int verbose_every, bool cholesky)
	Linear regression where each input point is given a different importance weight (the gammas); returns weighted average of squared loss.
Mat	weightedLinearRegression (VMat inputs, VMat outputs, VMat gammas, real weight_decay)
	Version that does all the memory allocations of XtX, XtY and theta_t. Returns theta_t.
VMat	rebalanceNClasses (VMat inputs, int nclasses, const string &filename)
void	fullyRebalance2Classes (VMat inputs, const string &filename, bool save_indices)
	Rebalance a 2-class VMat such as to keep all the examples of the dominant class.
VMat	temporalThreshold (VMat distr, int threshold_date, bool is_before, int yyyymmdd_col)
VMat	temporalThreshold (VMat distr, int threshold_date, bool is_before, int yyyy_col, int mm_col, int dd_col)
void	correlations (const VMat &x, const VMat &y, Mat &r, Mat &pvalues)
	Compute the correlations between each of the columns of x and each of the columns of y.
void	computeNearestNeighbors (VMat dataset, Vec x, TVec< int > &neighbors, int ignore_row)
VMat	normalize (VMat d, int inputsize)
time_t	getDateOfCode (const string &codefile)
	PLEARN_IMPLEMENT_OBJECT (VMatLanguage,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_OBJECT (PreprocessingVMatrix,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (VMatLanguage)
	DECLARE_OBJECT_PTR (PreprocessingVMatrix)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (VMatrix,"ONE LINE DESCR","NO HELP")
	VMatrix *.
string	getHost ()
int	getPid ()
string	getUser ()
	DECLARE_OBJECT_PTR (VMatrix)
	PLEARN_IMPLEMENT_OBJECT (VMatrixFromDistribution,"A VMatrix built from sampling a distribution","VMatrixFromDistribution implements a VMatrix whose data rows are drawn from a distribution\n""or that contains the density or log density sampled on a grid (depending on \"mode\").\n""The matrix is computed in memory at build time\n")
	PLEARN_IMPLEMENT_OBJECT (VVec,"ONE LINE DESCR","NO HELP")
void	operator>> (const VVec &vv, const Vec &v)
void	operator<< (const VVec &vv, const Vec &v)
void	operator<< (const Vec &v, const VVec &vv)
void	operator>> (const Vec &v, const VVec &vv)
	PLEARN_IMPLEMENT_OBJECT (VVMatrix,"A VMat that reads a '.vmat' file.","")
	DECLARE_OBJECT_PTR (VVMatrix)
	PLEARN_IMPLEMENT_OBJECT (YMDDatedVMatrix,"ONE LINE DESC","NO HELP")
	DECLARE_OBJECT_PTR (YMDDatedVMatrix)
	PLEARN_IMPLEMENT_OBJECT (AdaBoost,"AdaBoost boosting algorithm for TWO-CLASS classification","Given a classification weak-learner, this algorithm \"boosts\" it in\n""order to obtain a much more powerful classification algorithm.\n""The classifier is two-class, returning 0 or 1, or a number between 0 and 1\n""(in that case the user can set the 'pseudo_loss_adaboost' option, which\n""computes a more precise notion of error taking into account the precise\n""value outputted by the soft classifier).\n""The nstages option from PLearner is used to specify the desired\n""number of boosting rounds (but the algorithm can stop earlier if\n""the next weak learner is unable to unable to make significant progress.\n")
	DECLARE_OBJECT_PTR (AdaBoost)
	PLEARN_IMPLEMENT_OBJECT (ClassifierFromDensity,"A classifier built from density estimators using Bayes' rule.","ClassifierFromDensity allowd to build a classifier\n""by building one density estimator for each class, \n""and using Bayes rule to combine them. \n")
	DECLARE_OBJECT_PTR (ClassifierFromDensity)
	PLEARN_IMPLEMENT_OBJECT (MultiInstanceNNet,"Multi-instance feedforward neural network for probabilistic classification","The data has the form of a set of input vectors x_i associated with a single\n""label y. Each x_i is an instance and the overall set of instance is called a bag.\n""We don't know which of the inputs is responsible for the label, i.e.\n""there are hidden (not observed) labels y_i associated with each of the inputs x_i.\n""We also know that y=1 if at least one of the y_i is 1, otherwise y=0, i.e.\n"" y = y_1 or y_2 or ... y_m\n""In terms of probabilities, it means that\n"" P(Y=0|x_1..x_m) = \\prod_{i=1}^m P(y_i=0|x_i)\n""which determines the likelihood of the observation (x_1...x_m,y).\n""The neural network implements the computation of P(y_i=1|x_i). The same\n""model is assumed for all instances in the bag. The number of instances is variable but\n""bounded a-priori (max_n_instances). The gradient is computed for a whole bag\n""at a time. The architectural parameters and hyper-parameters of the model\n""are otherwise the same as for the generic NNet class.\n""The bags within each data set are specified with a 2nd target column\n""(the first column is 0, 1 or missing; it should not be missing for the\n""last column of the bag). The second target column should be 0,1,2, or 3:\n"" 1: first row of a bag\n"" 2: last row of a bag\n"" 3: simultaneously first and last, there is only one row in this bag\n"" 0: intermediate row of a bag\n""following the protocol expected by the SumOverBagsVariable.\n")
	DECLARE_OBJECT_PTR (MultiInstanceNNet)
	PLEARN_IMPLEMENT_OBJECT (ConditionalDensityNet,"Neural Network that Implements a Positive Random Variable Conditional Density","The input vector is used to compute parameters of an output density or output\n""cumulative distribution as well as output expected value. The ASSUMPTIONS\n""on the generating distribution P(Y|X) are the following:\n"" * Y is a single real value\n"" * 0 <= Y <= maxY, with maxY a known finite value\n"" * the density has a mass point at Y=0\n"" * the density is continuous for Y>0\n""The form of the conditional cumulative of Y is the following (separate_mass_points=false):\n"" P(Y<=y|theta) = (1/Z) (s(a) + sum_i u_i s(b_i) g(y,theta,i))\n""or for separate_mass_point=true:\n"" P(Y<=y|theta) = sigmoid(a) + (1-sigmoid(a))(sum_i u_i s(b_i) (g(y,theta,i)-g(0,theta,i))/Z\n""where s(z)=log(1+exp(z)) is the softplus function, and g is a monotonic function\n""in y whose first derivative and indefinite integral are known analytically.\n""The u_i are fixed from the unconditional distribution, such that s(b_i)=1 gives\n""approximately the right unconditional cumulative function (for infinite hardness):\n"" u_i = P(mu_{i-1}<Y<=mu_i) [unconditional].\n""The parameters theta of Y's distribution are (a,b_1,b_2,...,c_1,c_2,...,mu_1,mu_2,...),\n""which are obtained as the unconstrained outputs (no output transfer function) of a neural network.\n""The normalization constant Z is computed analytically easily: (separate_mass_point=false)\n"" Z = s(a) + sum_i u_i s(b_i) g(y,theta,i)\n""or for separate_mass_point=true:\n"" Z = sum_i s(b_i) (g(y,theta,i)-g(0,theta,i))\n""The current implementation considers two choices for g:\n"" - sigmoid_steps: g(y,theta,i) = sigmoid(h*s(c_i)*(y-mu_i)/(mu_{i+1}-mu_i))\n"" - sloped_steps: g(y,theta,i) = 1 + s(s(c_i)*(mu_i-y))-s(s(c_i)*(mu_{i+1}-y))/(s(c_i)*(mu_{i+1}-mu_i))\n""where h is the 'initial_hardness' option.\n""The density is analytically obtained using the derivative g' of g and\n""expected value is analytically obtained using the primitive G of g.\n""For the mass point at the origin,\n"" P(Y=0|theta) = P(Y<=0|theta).\n""(which is simply sigmoid(a) if separate_mass_point).\n""For positive values of Y: (separate_mass_point=false)\n"" p(y|theta) = (1/Z) sum_i s(b_i) g'(y,theta,i).\n""or for separate_mass_point=true:\n"" p(y|theta) = (1-sigmoid(a)) (1/Z) sum_i s(b_i) g'(y,theta,i).\n""And the expected value of Y is obtained using the primitive: (separate_mass_point=false)\n"" E[Y|theta] = (1/Z)*s(a)*M + sum_i u_i s(b_i)(G(M,theta,i)-G(0,theta,i)))\n""or for separate_mass_point=true:\n"" E[Y|theta] = M - ((sigmoid(a)-(1-sigmoid(a)*(1/Z)*sum_i u_i s(b_i)g(0,theta,i))*M + (1-sigmoid(a))*(1/Z)*sum_i u_i s(b_i)(G(M,theta,i)-G(0,theta,0)))\n""Training the model can be done by maximum likelihood (minimizing the log of the\n""density) or by minimizing the average of squared error (y-E[Y|theta])^2\n""or a combination of the two (with the max_likelihood_vs_squared_error_balance option).\n""The step 'centers' mu_i are initialized according to some rule, in the interval [0,maxY]:\n"" - uniform: at regular intervals in [0,maxY]\n"" - log-scale: as the exponential of values at regular intervals in [0,log(1+maxY)], minus 1.\n""The c_i and b_i are initialized to inverse_softplus(1), and a using the empirical unconditional P(Y=0).\n""For the output curve options (outputs_def='L',D','C', or 'S'), the lower_bound and upper_bound\n""options of PDistribution are automatically set to 0 and maxY respectively.\n")
	DECLARE_OBJECT_PTR (ConditionalDensityNet)
	PLEARN_IMPLEMENT_OBJECT (ConditionalDistribution,"ONE LINE DESCR","You must call setInput to set the condition before using the distribution")
	DECLARE_OBJECT_PTR (ConditionalDistribution)
	PLEARN_IMPLEMENT_OBJECT (ConditionalGaussianDistribution,"ConditionalGaussianDistribution is a gaussian distribution ""in which the parameters could be learned or specified manually.","")
	DECLARE_OBJECT_PTR (ConditionalGaussianDistribution)
	PLEARN_IMPLEMENT_OBJECT (Distribution,"This class is deprecated, use PDistribution instead.","NO HELP")
	DECLARE_OBJECT_PTR (Distribution)
	PLEARN_IMPLEMENT_OBJECT (EmpiricalDistribution,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (EmpiricalDistribution)
	PLEARN_IMPLEMENT_OBJECT (GaussianDistribution,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (GaussianDistribution)
	PLEARN_IMPLEMENT_OBJECT (GaussianProcessRegressor,"Basic version of Gaussian Process regression.","NO HELP")
	DECLARE_OBJECT_PTR (GaussianProcessRegressor)
	PLEARN_IMPLEMENT_OBJECT (GaussMix,"Gaussian mixture, either set non-parametrically or trained by EM.","GaussMix implements a mixture of L gaussians.\n""There are 4 possible parametrization types:\n"" - spherical : gaussians have covar matrix = diag(sigma). Parameter used : sigma.\n"" - diagonal : gaussians have covar matrix = diag(sigma_i). Parameters used : diags.\n"" - general : gaussians have an unconstrained covariance matrix.\n"" The user specifies the number 'n_eigen' of eigenvectors kept when\n"" decomposing the covariance matrix. The remaining eigenvectors are\n"" considered as having a fixed eigenvalue equal to the next highest\n"" eigenvalue in the decomposition.\n"" - factor : (not implemented!) as in the general case, the gaussians are defined\n"" with K<=D vectors (through KxD matrix 'V'), but these need not be\n"" orthogonal/orthonormal.\n"" The covariance matrix used will be V(t)V + psi with psi a D-vector\n"" (through parameter diags).\n""2 parameters are common to all 4 types :\n"" - alpha : the ponderation factor of the gaussians\n"" - mu : their centers\n")
	DECLARE_OBJECT_PTR (GaussMix)
	PLEARN_IMPLEMENT_OBJECT (HistogramDistribution,"Represents and possibly learns (using a smoother) a univariate distribution as a histogram.","This class represents a univariate distribution with a set of bins and their densities\n""The bins can be fixed or learned by a Binner object, and the densities\n""can be learned from a training set. The empirical densities in the bins can also\n""be smoothed with a Smoother (which is a general purpose univariate function\n""smoothing mechanism. If the data is not univariate, then only the LAST column\n""is considered. The smoother can either smooth the density or the survival fn.\n")
	DECLARE_OBJECT_PTR (HistogramDistribution)
	PLEARN_IMPLEMENT_OBJECT (LocallyWeightedDistribution,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (LocallyWeightedDistribution)
	PLEARN_IMPLEMENT_OBJECT (ManifoldParzen2,"ManifoldParzen implements a manifold Parzen.","")
void	computeNearestNeighbors (Mat dataset, Vec x, Mat &neighbors, int ignore_row=-1)
void	computePrincipalComponents (Mat dataset, Vec &eig_values, Mat &eig_vectors)
void	computeLocalPrincipalComponents (Mat &dataset, int which_pattern, Mat &delta_neighbors, Vec &eig_values, Mat &eig_vectors, Vec &mean)
	DECLARE_OBJECT_PTR (ManifoldParzen2)
	PLEARN_IMPLEMENT_OBJECT (PConditionalDistribution,"(THIS CLASS IS DEPRECATED, use PDistribution instead). Conditional distribution or conditional density model P(Y|X)","Abstract superclass for conditional distribution classes.\n""It is a subclass of PDistribution, with the added method\n"" setInput(Vec& input)\n""to set X, that must be called before PDistribution methods such as\n""log_density,cdf,survival_fn,expectation,variance,generate.\n""The PDistribution option output_defs must be set to specify\n""what the PLearner method computeOutput will produce. If it is\n""set to 'l' (log_density), 'd' (density), 'c' (cdf), or 's' (survival_fn)\n""then the input part of the data should contain both the input X and\n""the 'target' Y values (targetsize()==0). Instead, if output_defs is set to\n"" 'e' (expectation) or 'v' (variance), then the input part of the data should\n""contain only X, while the target part should contain Y\n")
	PLEARN_IMPLEMENT_OBJECT (PDistribution,"PDistribution is the base class for distributions.\n","PDistributions derive from PLearner (as some of them may be fitted to data with train()),\n""but they have additional methods allowing for ex. to compute density or generate data points.\n""The default implementations of the learner-type methods for computing outputs and costs work as follows:\n"" - the outputs_def option allows to choose which outputs are produced\n"" - cost is a vector of size 1 containing only the negative log-likelihood (NLL), i.e. -log_density.\n""A PDistribution may be conditional P(Y|X), if the option 'conditional_flags' is set. If it is the case,\n""the input should always be made of both the 'input' part (X) and the 'target' part (Y), even if the\n""output may not need to use the Y part. The exception is when computeOutput() needs to be called\n""successively with the same value of X: in this case, after a first call with both X and Y, one may\n""only provide Y as input, and X will be assumed to be unchanged.\n")
	PLEARN_IMPLEMENT_OBJECT (SpiralDistribution,"Generates samples drawn from a 2D spiral","SpiralDistribution is a generative model that generates 2D (x,y) samples in the following manner:\n"" t ~ uniform([tmin, tmax])^uniformity \n"" x = lambda*t*sin(alpha*t) + N(0,sigma) \n"" y = lambda*t*cos(alpha*t) + N(0,sigma) \n")
	DECLARE_OBJECT_PTR (SpiralDistribution)
	PLEARN_IMPLEMENT_OBJECT (UnconditionalDistribution,"This class is a simplified version of PDistribution for unconditional distributions.","Its only goal is to hide the conditional side of PDistributions to make it simpler.")
	DECLARE_OBJECT_PTR (UnconditionalDistribution)
	PLEARN_IMPLEMENT_OBJECT (UniformDistribution,"Implements uniform distribution over intervals.","Currently, only very few methods are implemented.\n""For example, to sample points in 2D in [a,b] x [c,d], use\n"" min = [a c]\n"" max = [b d]\n")
	DECLARE_OBJECT_PTR (UniformDistribution)
	PLEARN_IMPLEMENT_OBJECT (AddCostToLearner,"A PLearner that just adds additional costs to another PLearner.","In addition, this learner can be used to compute costs on bags instead of\n""individual samples, using the option 'compute_costs_on_bags'.\n""\n""Feel free to make this class evolve by adding new costs, or rewriting it\n""in a better fashion, because this one is certainly not perfect.\n""To use the lift cost, do the following:\n"" (1) add a cost of type 1 to this object's option 'costs'\n"" (2) replace the template_stats_collector of your PTester with one like this:\n"" template_stats_collector =\n"" LiftStatsCollector (\n"" lift_fraction = 0.1 ;\n"" output_column = \"lift_output\" ;\n"" opposite_lift = 0 ; # to set to 1 if we want to optimize it\n"" sign_trick = 1 ;\n"" (3) ask for the lift in the stats:\n"" statnames = [\n"" \"E[test1.LIFT]\"\n"" \"E[test1.LIFT_MAX]\"\n"" ];")
	DECLARE_OBJECT_PTR (AddCostToLearner)
	PLEARN_IMPLEMENT_OBJECT (EmbeddedLearner,"Wraps an underlying learner","EmbeddedLearner implements nothing but forwarding \n""calls to an underlying learner. It is typically used as\n""baseclass for learners that are built on top of another learner")
	DECLARE_OBJECT_PTR (EmbeddedLearner)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (Learner,"DEPRECATED CLASS: Derive from PLearner instead","NO HELP")
void	prettyprint_test_results (ostream &out, const Learner &learner, const Vec &results)
	PLEARN_IMPLEMENT_OBJECT (NeighborhoodSmoothnessNNet,"Feedforward neural network whose hidden units are smoothed according to input neighborhood\n","TODO")
	DECLARE_OBJECT_PTR (NeighborhoodSmoothnessNNet)
	PLEARN_IMPLEMENT_OBJECT (NeuralNet,"DEPRECATED: Use NNet instead","NO HELP")
	DECLARE_OBJECT_PTR (NeuralNet)
	PLEARN_IMPLEMENT_OBJECT (NNet,"Ordinary Feedforward Neural Network with 1 or 2 hidden layers","Neural network with many bells and whistles...")
	DECLARE_OBJECT_PTR (NNet)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (PLearner,"The base class for all PLearn learners.","")
	PLEARN_IMPLEMENT_OBJECT (SelectInputSubsetLearner,"PLearner which selects a subset of the inputs for an embedded learner.","This learner class contains an embedded learner for which it selects a subset of the inputs.\n""The subset can be either selected explicitly or chosen randomly (the user chooses what fraction\n""of the original inputs will be selected).")
	DECLARE_OBJECT_PTR (SelectInputSubsetLearner)
	PLEARN_IMPLEMENT_OBJECT (StackedLearner,"Implements stacking, that combines two levels of learner, the 2nd level using the 1st outputs as inputs","Stacking is a generic strategy in which two levels (or more, recursively) of learners\n""are combined. The lower level may have one or more learners, and they may be trained\n""on the same or different data from the upper level single learner. The outputs of the\n""1st level learners are concatenated and serve as inputs to the second level learner.\n""IT IS ASSUMED THAT ALL BASE LEARNERS HAVE THE SAME NUMBER OF INPUTS AND OUTPUTS\n""There is also the option to copy the input of the 1st level learner as additional\n"" inputs for the second level (put_raw_input).\n""A Splitter can optionally be provided to specify how to split the data into\n""the training /validation sets for the lower and upper levels respectively\n")
	DECLARE_OBJECT_PTR (StackedLearner)
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (StatefulLearner,"PLearner with an internal state","PLearner with an internal state.\n""It replaces, for efficacity and compatibility reasons, SequentialLearner.")
	DECLARE_OBJECT_PTR (StatefulLearner)
	PLEARN_IMPLEMENT_OBJECT (TestingLearner,"ONE LINE DESCRIPTION","MULTI-LINE \nHELP")
	DECLARE_OBJECT_PTR (TestingLearner)
	PLEARN_IMPLEMENT_OBJECT (GraphicalBiText,"Probabilistically tag a bitext (english-other language) with senses from WordNet","NO HELP")
VMat	loadToVMat (string file, string name, int window, int n_examples)
bool	lessPair (pair< int, float > &p1, pair< int, float > &p2)
	PLEARN_IMPLEMENT_OBJECT (Dictionary,"Mapping string->int and int->string","MULTI LINE\nHELP")
	DECLARE_OBJECT_PTR (Dictionary)
	PLEARN_IMPLEMENT_OBJECT (TextSenseSequenceVMatrix,"VMat class that takes another VMat which contains a sequence (rows) ""of words/sense/POS triplets extracted from a corpus and implements a ""representation of a target word and its context.","")
void	samePos (ProbabilitySparseMatrix &m1, ProbabilitySparseMatrix &m2, string m1name, string m2name)
void	check_prob (ProbabilitySparseMatrix &pYX, string Yname, string Xname)
void	check_prob (Set Y, const map< int, real > &pYx)
void	update (ProbabilitySparseMatrix &pYX, ProbabilitySparseMatrix &nYX)
void	updateAndClearCounts (ProbabilitySparseMatrix &pYX, ProbabilitySparseMatrix &nYX)
ostream &	operator<< (ostream &out, ProbabilitySparseMatrix &pyx)
void	print (ostream &out, ProbabilitySparseMatrix &pyx, Set Y, Set X)
void	print (ostream &out, RowMapSparseMatrix< real > &m)
void	print (ostream &out, const map< int, real > &vec, int size)
void	print (ostream &out, const map< int, real > &vec)
void	print (ostream &out, const map< int, real > &vec, Set V)
PStream &	operator<< (PStream &out, const PPointableSet &pp_set)
PStream &	operator>> (PStream &in, PPointableSet &pp_set)
PPointableSet *	newSet ()
void	merge (Set a, Set b, Set res)
void	difference (Set a, Set b, Set res)
void	intersection (Set a, Set b, Set res)
ostream &	operator<< (ostream &out, Set s)
string	trimWord (string word)
bool	isLetter (char c)
bool	isDigit (char c)
bool	isAlpha (char c)
bool	isLegalPunct (char c)
string	stemWord (string &word)
string	stemWord (string &word, int wn_pos)
char *	cstr (string &str)
void	removeDelimiters (string &s, string delim, string replace)
bool	startsWith (string &base, string s)
void	replaceChars (string &str, string char_to_replace, string replacing_char)
	PLEARN_IMPLEMENT_OBJECT (Experiment,"DEPRECATED: use PTester instead","")
	DECLARE_OBJECT_PTR (Experiment)
void	DX_write_2D_fields (ostream &out, const string &basename, TVec< Mat > fields, real x0, real y0, real deltax, real deltay, TVec< string > fieldnames=TVec< string >())
void	DX_write_2D_fields (ostream &out, const string &basename, Vec X, Vec Y, TVec< Mat > fields)
TVec< Mat >	computeOutputFields (PP< PLearner > learner, Vec X, Vec Y)
TVec< Mat >	computeOutputFields (PP< PLearner > learner, int nx, int ny, real x0, real y0, real deltax, real deltay)
TVec< Mat >	computeOutputFieldsAutoRange (PP< PLearner > learner, VMat dataset, int nx, int ny, real &x0, real &y0, real &deltax, real &deltay, real extraspace=.10)
void	computeXYPositions (VMat dataset, int nx, int ny, Vec &X, Vec &Y, real extraspace=.10)
void	DX_create_dataset_outputs_file (const string &filename, PP< PLearner > learner, VMat dataset)
	Will write a file containing a field with the dataset positions "dset" field will be input -> target, outputs.
void	DX_create_grid_outputs_file (const string &filename, PP< PLearner > learner, VMat dataset, int nx, int ny, bool include_datapoint_grid=false, real xmin=MISSING_VALUE, real xmax=MISSING_VALUE, real ymin=MISSING_VALUE, real ymax=MISSING_VALUE, real extraspace=.10)
	The "outputs" field will contain sample-grid inputs -> outputs Where the sample grid is made of a regular grid of nx.ny points (in the range [xmin, xmax] x [ymin, ymax]) xmin, xmax, ymin and ymax may be left to MISSING_VALUE, in which case an automatic range will be determined from the range of the points in the given dataset extended by extraspace (ex: .10 == 10%).
	PLEARN_IMPLEMENT_OBJECT (GenerateDecisionPlot,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (GenerateDecisionPlot)
	PLEARN_IMPLEMENT_OBJECT (Grapher,"ONE LINE DESCR","NO HELP")
real	color (int colornum, real lightness)
	DECLARE_OBJECT_PTR (Grapher)
Mat	compute_learner_outputs (PP< PLearner > learner, VMat inputs)
void	determine_grid_for_dataset (VMat dataset, int nx, int ny, real &x0, real &y0, real &deltax, real &deltay, real extraspace)
double	determine_density_integral_from_log_densities_on_grid (Vec log_densities, real deltax, real deltay)
Mat	compute_learner_outputs_on_grid (PP< PLearner > learner, int nx, int ny, real x0, real y0, real deltax, real deltay)
	Returns a nx*ny x learner->outputsize() matrix of outputs corresponding to the nx*ny grid points.
void	DX_write_2D_data (ostream &out, const string &basename, Mat data)
	considers data to have 2d input (first 2 columns of data)
void	DX_write_2D_data_for_grid (ostream &out, const string &basename, int nx, int ny, real x0, real y0, real deltax, real deltay, Mat data)
	data must have nx*ny rows and must corresponds to values associated with the 2D positions of the grid (typically learner outputs on that grid)
void	DX_save_2D_data (const string &filename, const string &basename, Mat data)
	considers data to have 2d input (first 2 columns of data)
void	DX_save_2D_data_for_grid (const string &filename, const string &basename, int nx, int ny, real x0, real y0, real deltax, real deltay, Mat data)
	data must have nx*ny rows and must corresponds to values associated with the 2D positions of the grid (typically learner outputs on that grid)
TVec< string >	addprepostfix (const string &prefix, const TVec< string > &names, const string &postfix)
template<class T> TVec< T >	operator & (const T &x, const TVec< T > &v)
	PLEARN_IMPLEMENT_OBJECT (PTester,"Evaluates the performance of a PLearner","The PTester class allows you to describe a typical learning experiment that you wish to perform, \n""as a training/testing of a learning algorithm on a particular dataset.\n""The splitter is used to obtain one or several (such as for k-fold) splits of the dataset \n""and training/testing is performed on each split. \n""Requested statistics are computed, and all requested results are written in an appropriate \n""file inside the specified experiment directory. \n")
	DECLARE_OBJECT_PTR (PTester)
	PLEARN_IMPLEMENT_OBJECT (ConstantRegressor,"PLearner that outputs a constant (input-independent) vector.\n","ConstantRegressor is a PLearner that outputs a constant (input-independent\n""but training-data-dependent) vector. It is a regressor (i.e. during training\n""the constant vector is chosen to minimize the (possibly weighted) average\n""of the training set targets. Let\n"" N = number of training examples,\n"" M = target size (= output size),\n"" y_{ij} = the jth target value of the ith training example,\n"" w_i = weight associated to the ith training example,\n""then the j-th component of the learned vector is\n"" (sum_{i=1}^N w_i * y_ij) / (sum_{i=1}^N w_i)\n""The output can also be set manually with the 'constant_output' vector option\n")
	DECLARE_OBJECT_PTR (ConstantRegressor)
	PLEARN_IMPLEMENT_OBJECT (LinearRegressor,"Ordinary Least Squares and Ridge Regression, optionally weighted","This class performs OLS (Ordinary Least Squares) and Ridge Regression, optionally on weighted\n""data, by solving the linear equation (X'W X + weight_decay*n_examples*I) theta = X'W Y\n""where X is the (n_examples x (1+inputsize)) matrix of extended inputs (with a 1 in the first column),\n""Y is the (n_example x targetsize), W is a diagonal matrix of weights (one per example)\n""{the identity matrix if weightsize()==0 in the training set}, and theta is the resulting\n""set of parameters. W_{ii} is obtained from the weight column of the training set, if any.\n""This column must have width 0 (no weight) or 1.\n""A prediction (computeOutput) is obtained from an input vector as follows:\n"" output = theta * (1,input)\n""The criterion that is minimized by solving the above linear system is the squared loss""plus squared norm penalty (weight_decay*sum_{ij} theta_{ij}^2) PER EXAMPLE. This class also measures""the ordinary squared loss (||output-theta||^2). The two costs are named 'mse+penalty' and 'mse' respectively.\n""Training has two steps: (1) computing X'W X and X' W Y, (2) solving the linear system.\n""The first step takes time O(n_examples*inputsize^2 + n_examples*inputsize*outputsize).\n""The second step takes time O(inputsize^3).\n""If train() is called repeatedly with different values of weight_decay, without intervening\n""calls to forget(), then the first step will be done only once, and only the second step\n""is repeated.\n")
	DECLARE_OBJECT_PTR (LinearRegressor)
	PLEARN_IMPLEMENT_OBJECT (PLS,"Partial Least Squares Regression (PLSR).","You can use this learner to perform regression, and / or dimensionality\n""reduction.\n""PLS regression assumes the target Y and the data X are linked through:\n"" Y = T.Q' + E\n"" X = T.P' + F\n""The underlying coefficients T (the 'scores') and the loading matrices\n""Q and P are seeked. It is then possible to compute the prediction y for\n""a new input x, as well as its score vector t (its representation in\n""lower-dimensional coordinates).\n""The available algorithms to perform PLS (chosen by the 'method' option) are:\n""\n"" ==== PLS1 ====\n""The classical PLS algorithm, suitable only for a 1-dimensional target. The\n""following algorithm is taken from 'Factor Analysis in Chemistry', with an\n""additional loop that (I believe) was missing:\n"" (1) Let X (n x p) = the centered and normalized input data\n"" Let y (n x 1) = the centered and normalized target data\n"" Let k be the number of components extracted\n"" (2) s = y\n"" (3) lx' = s' X, s = X lx (normalized)\n"" (4) If s has changed by more than 'precision', loop to (3)\n"" (5) ly = s' y\n"" (6) lx' = s' X\n"" (7) Store s, lx and ly in the columns of respectively T, P and Q\n"" (8) X = X - s lx', y = y - s ly, loop to (2) k times\n"" (9) Set W = (T P')^(+) T, where the ^(+) is the right pseudoinverse\n""\n"" ==== Kernel ====\n""The code implements a NIPALS-PLS-like algorithm, which is a so-called\n""'kernel' algorithm (faster than more classical implementations).\n""The algorithm, inspired from 'Factor Analysis in Chemistry' and above all\n""www.statsoftinc.com/textbook/stpls.html, is the following:\n"" (1) Let X (n x p) = the centered and normalized input data\n"" Let Y (n x m) = the centered and normalized target data\n"" Let k be the number of components extracted\n"" (2) Initialize A_0 = X'Y, M_0 = X'X, C_0 = Identity(p), and h = 0\n"" (3) q_h = largest eigenvector of B_h = A_h' A_h, found by the NIPALS method:\n"" (3.a) q_h = a (normalized) randomn column of B_h\n"" (3.b) q_h = B_h q_h\n"" (3.c) normalize q_h\n"" (3.d) if q_h has changed by more than 'precision', go to (b)\n"" (4) w_h = C_h A_h q_h, normalize w_h and store it in a column of W (p x k)\n"" (5) p_h = M_h w_h, c_h = w_h' p_h, p_h = p_h / c_h and store it in a column\n"" of P (p x k)\n"" (6) q_h = A_h' w_h / c_h, and store it in a column of Q (m x k)\n"" (7) A_h+1 = A_h - c_h p_h q_h'\n"" M_h+1 = M_h - c_h p_h p_h',\n"" C_h+1 = C_h - w_h p_h\n"" (8) h = h+1, and if h < k, go to (3)\n""\n""The result is then given by:\n"" - Y = X B, with B (p x m) = W Q'\n"" - T = X W, where T is the score (reduced coordinates)\n""\n""You can choose to have the score (T) and / or the target (Y) in the output\n""of the learner (default is target only, i.e. regression).")
	DECLARE_OBJECT_PTR (PLS)
	PLEARN_IMPLEMENT_OBJECT (EmbeddedSequentialLearner,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (EmbeddedSequentialLearner)
	Declares a few other classes and functions related to this class.
	PLEARN_IMPLEMENT_OBJECT (MovingAverage,"ONE LINE DESCR","NO HELP")
	PLEARN_IMPLEMENT_ABSTRACT_OBJECT (SequentialLearner,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (SequentialLearner)
	Declares a few other classes and functions related to this class.
	PLEARN_IMPLEMENT_OBJECT (SequentialModelSelector,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (SequentialModelSelector)
	Declares a few other classes and functions related to this class.
	PLEARN_IMPLEMENT_OBJECT (SequentialValidation,"The SequentialValidation class allows you to describe a typical ""sequential validation experiment that you wish to perform.","NO HELP")
	DECLARE_OBJECT_PTR (SequentialValidation)
	Declares a few other classes and functions related to this class.
	PLEARN_IMPLEMENT_OBJECT (PTester,"Manages a learning experiment, with training and estimation of generalization error.","The PTester class allows you to describe a typical learning experiment that you wish to perform, \n""as a training/testing of a learning algorithm on a particular dataset.\n""The splitter is used to obtain one or several (such as for k-fold) splits of the dataset \n""and training/testing is performed on each split. \n""Requested statistics are computed, and all requested results are written in an appropriate \n""file inside the specified experiment directory. \n""Statistics can be either specified entirely from the 'statnames' option, or built from\n""'statnames' and 'statmask'. For instance, one may set:\n"" statnames = [ \"NLL\" \"mse\" ]\n"" statmask = [ [ \"E[*]\" ] [ \"test1.*\" \"test2.*\" ] [ \"E[*]\" \"STDERROR[*]\" ] ]\n""and this will compute:\n"" E[test1.E[NLL]], STDERROR[test1.E[NLL]], E[test2.E[NLL]], STDERROR[test2.E[NLL]]\n"" E[test1.E[mse]], STDERROR[test1.E[mse]], E[test2.E[mse]], STDERROR[test2.E[mse]]\n")
	PLEARN_IMPLEMENT_OBJECT (TestMethod,"ONE LINE DESCR","NO HELP")
	DECLARE_OBJECT_PTR (TestMethod)
	PLEARN_IMPLEMENT_OBJECT (Train,"An easy PTester","Sometimes, we want to train a learner easily.\n""But plearn learner train does not permit to use macro variable.\n""Train is the equivalent of plearn learner train but macro variables are usable\n""Example: plearn myTrain.plearn knn=12")
	PLEARN_IMPLEMENT_OBJECT (EntropyContrast,"Performs a EntropyContrast search","Detailed Description ")
	DECLARE_OBJECT_PTR (EntropyContrast)
Mat	smartInitialization (VMat v, int n, real c, real regularization)
	PLEARN_IMPLEMENT_OBJECT (GaussianContinuum,"Learns a continuous (uncountable) Gaussian mixture with non-local parametrization","This learner implicitly estimates the density of the data through\n""a generalization of the Gaussian mixture model and of the TangentLearner\n""algorithm (see help on that class). The density is the fixed point of\n""a random walk {z_t} that follows the following transition probabilities:\n"" z_{t+1} sampled from a Gaussian associated with z_t, centered\n"" at z_t + mu(z_t), with covariance matrix S(z_t).\n""The semantic of that random walk is the following (and that is how\n""it will be estimated). Given a point z_t, the sample z_{t+1} represents\n""a 'near neighbor' of z_t. We assume that the density is smooth enough\n""that the cloud of 'near neighbors' around z_t can be modeled by a Gaussian.\n""The functions mu(.) and S(.) have globally estimated parameters (for example\n""using neural nets or linear functions of x, or linear functions of a basis).\n""Here we suppose that the eigenvalues of S(.) come from two groups:\n""the first group should correspond to locally estimated principal\n""directions of variations and there are no constraints on these eigenvalues\n""(except that they are positive), while the second group should correspond\n""to 'noise' directions, that have all the same value sigma2_noise\n""i.e. it is not necessary to explicitly model the directions of variations\n""(the eigenvectors) associated with the second group. In general we expect\n""sigma2_noise to be small compared to the first group eigenvalues, which\n""means that the Gaussians are flat in the corresponding directions, and\n""that the first group variations correspond to modeling a manifold near\n""which most of the data lie. Optionally, an embedding corresponding\n""to variations associated with the first group of eigenvalues can be learnt\n""by choosing for the architecture_type option a value of the form embedding_*.\n""Although the density is not available in closed form, it is easy (but maybe slow)\n""to sample from it: pick one of the training examples at random and then\n""follow the random walk (ideally, a long time). It is also possible in\n""principle to obtain a numerical estimate of the density at a point x,\n""by sampling enough random walk points around x.\n")
	DECLARE_OBJECT_PTR (GaussianContinuum)
	PLEARN_IMPLEMENT_OBJECT (Isomap,"Performs ISOMAP dimensionality reduction.","Be careful that when looking for the 'knn' nearest neighbors of a point x,\n""we consider all points from the training data D, including x itself if it\n""belongs to D. Thus, to obtain the same result as with the classical ISOMAP\n""algorithm, one should use one more neighbor.\n""Note also that when used out-of-sample, this will result in a different output\n""than an algorithm applying the same formula, but considering one less neighbor.\n")
	DECLARE_OBJECT_PTR (Isomap)
	PLEARN_IMPLEMENT_OBJECT (IsomapTangentLearner,"Tangent learning based on Isomap Kernel","MULTI-LINE \nHELP")
	PLEARN_IMPLEMENT_OBJECT (KernelPCA,"Kernel Principal Component Analysis","Perform PCA in a feature space phi(x), defined by a kernel K such that\n"" K(x,y) = < phi(x), phi(y) >\n")
	PLEARN_IMPLEMENT_OBJECT (KernelProjection,"Performs dimensionality reduction by learning eigenfunctions of a kernel.","")
	PLEARN_IMPLEMENT_OBJECT (KPCATangentLearner,"Tangent learning based on KPCA Kernel","MULTI-LINE \nHELP")
	DECLARE_OBJECT_PTR (KPCATangentLearner)
	PLEARN_IMPLEMENT_OBJECT (LLE,"Performs Locally Linear Embedding.","")
	PLEARN_IMPLEMENT_OBJECT (PCA,"Performs a Principal Component Analysis preprocessing (projecting on the principal directions).","This learner finds the empirical covariance matrix of the input part of\n""the training data, and learns to project its input vectors along the\n""principal eigenvectors of that matrix, optionally scaling by the inverse\n""of the square root of the eigenvalues (to obtained 'sphered', i.e.\n""Normal(0,I) data).\n""Alternative EM algorithms are provided, that may be useful when there is\n""a lot of data or the dimension is very high.\n")
	DECLARE_OBJECT_PTR (PCA)
	PLEARN_IMPLEMENT_OBJECT (SpectralClustering,"Spectral Clustering dimensionality reduction.","The current code only performs dimensionality reduction, and does not do\n""clustering.")
	DECLARE_OBJECT_PTR (SpectralClustering)
	PLEARN_IMPLEMENT_OBJECT (TangentLearner,"Learns local tangent plane of the manifold near which the data lie.","This learner models a manifold near which the data are supposed to lie.\n""The manifold is represented by a function which predicts a basis for the\n""tangent planes at each point x, given x in R^n. Let f_i(x) be the predicted i-th tangent\n""vector (in R^n). Then we will optimize the parameters that define the d functions f_i by\n""pushing the f_i so that they span the local tangent directions. Three criteria are\n""possible, according to the 'training_targets', 'normalize_by_neighbor_distance' and\n""'use_subspace_distance' option. The default criterion is the recommanded one, with\n"" training_targets='local_neighbors', normalize_by_neighbor_distance=1,\n""and use_subspace_distance=0 (it really did not work well in our experiments with\n""use_subspace_distance=1). This corresponds to the following cost function:\n"" sum_x sum_j min_w ||t(x,j) - sum_i w_i f_i(x)||^2 / ||t(x,j)||^2\n""where x is an example, t(x,j) is the difference vector between x and its j-th neighbor,\n""and the w_i are chosen freely for each j and x and correspond to the weights given to\n""each basis vector f_i(x) to obtain the projection of t(x,j) on the tangent plane.\n""More generally, if use_subspace_distance,\n"" criterion = min_{w,u} || sum_i w_i f_i - sum_j u_j t(x,j) ||^2\n"" under the constraint that ||w||=1.\n"" else\n"" criterion = sum_x sum_j min_w ||t(x,j) - sum_i w_i f_i(x)||^2 / ||t(x,j)||^2\n"" where the first sum is over training examples and w is a free d-vector,\n"" t(x,j) estimates local tangent directions based on near neighbors, and the denominator\n"" ||t(x,j)||^2 is optional (normalize_by_neighbor_distance). t(x,j)\n"" is defined according to the training_targets option:\n"" 'local_evectors' : local principal components (based on n_neighbors of x)\n"" 'local_neighbors': difference between x and its n_neighbors.\n""An additional criterion option that applies only to use_subspace_criterion=0 is\n""the orderered_vectors option, which applies a separate cost to each of the f_i:\n""the f_1 vector tries to make the projection of t(x,j) on f_1 close to t(x,j), while\n""the f_2 vector tries to make the projection of t(x,j) on the (f_1,f_2) basis close to t(x,j),\n""etc... i.e. the gradient on f_i is computed based on a cost that involves only\n""the projection on the first i vectors. This is analogous to principal component analysis:\n""the first vector tries to capture as much as possible of the variance, the second as much\n""as possible of the remaining variance, etc...\n""Different architectures are possible for the f_i(x) (architecture_type option):\n"" - multi_neural_network: one neural net per basis function\n"" - single_neural_network: single neural network with matrix output (one row per basis vector)\n"" - linear: F_{ij}(x) = sum_k A_{ijk} x_k\n"" - embedding_neural_network: the embedding function e_k(x) (for k-th dimension)\n"" is an ordinary neural network, and F_{ki}(x) = d(e_k(x))/d(x_i). This allows to\n"" output the embedding, instead of, or as well as, the tangent plane (output_type option).\n"" - embedding_quadratic: the embedding function e_k(x) (for k-th dimension)\n"" is a 2nd order polynomial of x, and F_{ki}(x) = d(e_k(x))/d(x_i). This allows to\n"" output the embedding, instead of, or as well as, the tangent plane (output_type option).\n")
	DECLARE_OBJECT_PTR (TangentLearner)
map< string, string >	getModelAliases (const string &filename)
	reads a modelalias -> object_representation map from a model.aliases file
void	train_and_test (const string &modelalias, string trainalias, vector< string > testaliases)
vector< string >	getMultipleModelAliases (const string &model)
void	cross_valid (const string &modelalias, string trainalias, int kval)
void	use (const string &modelfile, const string &datasetalias)
void	usage ()
int	old_plearn_main (int argc, char **argv)
int	plearn_main (int argc, char **argv)
Variables
PLearnInit	_plearn_init_
const size_t	PL_HASH_NOMBRES_MAGIQUES [256]
ostream *	error_stream = &cerr
const string	dbdir_name = ""
const char	MissingString = '\0'
	A few constants for representing missing values.
const unsigned char	MissingCharacter = (unsigned char)SCHAR_MIN
const signed char	MissingSignedChar = (signed char)SCHAR_MIN
const short	MissingShort = SHRT_MIN
const int	MissingInt = INT_MIN
const float	MissingFloat = MISSING_VALUE
const double	MissingDouble = MISSING_VALUE
const PDate	MissingDate
const int	pl_dftbuflen = 4096
pl_stream_raw	raw
pl_stream_clear_flags	clear_flags
pl_stream_initiate	initiate
pl_nullstreambuf	null_streambuf
ostream	nullout
	a null ostream: writing to it does nothing
istream	nullin
	a null instream: reading from it does nothing
iostream	nullinout
	a null iostream: reading/writing from/to it does nothing
const char	DIGITsymbols [] = "0123456789"
const char	ALPHAsymbols [] = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
const char *	ORDINALS [] = {"d","nd","th","st",0}
const tRule	rules []
const unsigned int	Hash_UNUSED_TAG = 0xffffffffu
	DataType must have new, delete and copy constructor Keys are unique.
const void *	Hash_DELETED_SLOT = (void *)0x00000001
const unsigned int	Hash_NOMBRES_MAGIQUES [256]
double	pl_gammln_cof [7]
float	tanhtable [TANHTABLESIZE]
PLMathInitializer	pl_math_initializer
_plearn_nan_type	plearn_nan
long	the_seed = 0
int	iset = 0
real	gset
double	MAXLOG = 7.09782712893383996732E2
double	MINLOG = -7.451332191019412076235E2
double	MACHEP = 1.11022302462515654042E-16
double	big = 4.503599627370496e15
double	biginv = 2.22044604925031308085e-16
string	vmat_view_dataset
	The specification of the dataset viewed by the vmat program.
bool	displayvg = false
const int	STORAGE_UNUSED_HANDLE = -1

---

Detailed Description

< for swap

pl_fdstream.{h|cc} Defines a stream buffer than can be created from a POSIX file descriptor, along with a stream to use that buffer.

---

Typedef Documentation

typedef int(* PLearn::compare_function)(const void *, const void *)

Definition at line 236 of file general.h. Referenced by PLearn::RVArray::sort(), and PLearn::RVInstanceArray::sort().

typedef const map<int, real> PLearn::ConstSparseVec

Definition at line 14 of file ProbabilitySparseMatrix.h. Referenced by PLearn::ProbabilitySparseMatrix::removeExtra().

typedef map<const void*,void*> PLearn::CopiesMap

Global typedef to make the map of copied objects (needed by the deep copy mechanism in Object) more palatable. Definition at line 28 of file CopiesMap.h. Referenced by deepCopy(), PLearn::SequentialModelSelector::makeDeepCopyFromShallowCopy(), PLearn::SequentialLearner::makeDeepCopyFromShallowCopy(), PLearn::PLearner::makeDeepCopyFromShallowCopy(), PLearn::NNet::makeDeepCopyFromShallowCopy(), PLearn::NeuralNet::makeDeepCopyFromShallowCopy(), PLearn::NeighborhoodSmoothnessNNet::makeDeepCopyFromShallowCopy(), PLearn::MultiInstanceNNet::makeDeepCopyFromShallowCopy(), PLearn::Learner::makeDeepCopyFromShallowCopy(), PLearn::Kernel::makeDeepCopyFromShallowCopy(), PLearn::GradientOptimizer::makeDeepCopyFromShallowCopy(), PLearn::GaussianDistribution::makeDeepCopyFromShallowCopy(), PLearn::EmpiricalDistribution::makeDeepCopyFromShallowCopy(), PLearn::EmbeddedSequentialLearner::makeDeepCopyFromShallowCopy(), PLearn::ConjGradientOptimizer::makeDeepCopyFromShallowCopy(), PLearn::AdaptGradientOptimizer::makeDeepCopyFromShallowCopy(), and PLearn::Function::operator()().

typedef Ker PLearn::CostFunc

a cost function maps (output,target) to a loss ************ CostFunc * Definition at line 246 of file Kernel.h. Referenced by PLearn::ClassifierFromDensity::computeCostsFromOutputs(), PLearn::Learner::computeLeaveOneOutCosts(), and PLearn::QuadraticUtilityCostFunction::QuadraticUtilityCostFunction().

typedef Array<PSDBVMFieldDiscrete> PLearn::FieldArray

In general, if there are N fields, x_1...x_N, and each can take y_i values, then the discrete value is:. where {j=N+1}^N is defined to be 1. For convenience, this class inherites from SDBVMFieldDiscrete, but does not use the inherited source_ member. Definition at line 864 of file SDBVMat.h. Referenced by PLearn::SDBVMFieldMultiDiscrete::SDBVMFieldMultiDiscrete().

typedef OptionList&(* PLearn::GETOPTIONLIST_METHOD)()

Definition at line 54 of file TypeFactory.h. Referenced by PLearn::TypeFactory::register_type().

typedef map<string, string> PLearn::HAliases

Definition at line 60 of file HyperOptimizer.h. Referenced by PLearn::HTryCombinations::optimize(), PLearn::HCoordinateDescent::optimize(), PLearn::HTryAll::optimize(), and PLearn::HSetVal::optimize().

typedef Array<PP<HyperOptimizer> > PLearn::HStrategy

Definition at line 59 of file HyperOptimizer.h.

typedef bool(* PLearn::ISA_METHOD)(Object *o)

Definition at line 55 of file TypeFactory.h. Referenced by PLearn::TypeFactory::register_type().

typedef TinyVector<int, 7> PLearn::IVec

Definition at line 49 of file Tensor.h. Referenced by PLearn::TTensor< T >::begin(), and PLearn::TTensorSubTensorIterator< T >::TTensorSubTensorIterator().

typedef map<double, string> PLearn::map_double_string

Definition at line 33 of file CopiesMap.h.

typedef map<float, string> PLearn::map_float_string

Definition at line 34 of file CopiesMap.h.

typedef map<string, double> PLearn::map_string_double

Definition at line 32 of file CopiesMap.h.

typedef map<string, float> PLearn::map_string_float

Some typedefs to use the NODEEPCOPY macro with. Definition at line 31 of file CopiesMap.h.

typedef TMat<real> PLearn::Mat

Definition at line 802 of file TMat_decl.h. Referenced by PLearn::ToBagSplitter::build_(), PLearn::RemoveDuplicateVMatrix::build_(), PLearn::StatsCollector::cdf(), computeConditionalMeans(), PLearn::SourceKernel::computeGramMatrix(), PLearn::LLEKernel::computeGramMatrix(), PLearn::DivisiveNormalizationKernel::computeGramMatrix(), PLearn::AdditiveNormalizationKernel::computeGramMatrix(), PLearn::ReconstructionWeightsKernel::computeLLEMatrix(), PLearn::GeodesicDistanceKernel::computeNearestGeodesicNeighbour(), PLearn::Kernel::computeNearestNeighbors(), PLearn::PConditionalDistribution::computeOutput(), PLearn::KPCATangentLearner::computeOutput(), PLearn::IsomapTangentLearner::computeOutput(), PLearn::HistogramDistribution::computeOutput(), PLearn::ReconstructionWeightsKernel::computeWeights(), PLearn::MatlabInterface::eigs_r11(), PLearn::PLMPI::exchangeColumnBlocks(), PLearn::Function::fbbprop(), PLearn::QuantilesStatsIterator::finish(), PLearn::PLS::forget(), PLearn::KernelProjection::forget(), PLearn::MatrixAffineTransformVariable::fprop(), PLearn::MatrixAffineTransformFeedbackVariable::fprop(), PLearn::AffineTransformVariable::fprop(), PLearn::FunctionalRandomVariable::FunctionalRandomVariable(), PLearn::VMatrix::getMat(), PLearn::SubVMatrix::getMat(), PLearn::ForwardVMatrix::getMat(), PLearn::LocalNeighborsDifferencesVMatrix::getNewRow(), loadLetters(), PLearn::SequentialModelSelector::matlabSave(), PLearn::MatRowVariable::MatRowVariable(), PLearn::MemoryVMatrix::MemoryVMatrix(), PLearn::MultiInstanceVMatrix::MultiInstanceVMatrix(), PLearn::PairsVMatrix::PairsVMatrix(), PLearn::Gnuplot::plot(), PLearn::Grapher::plot_1D_regression(), PLearn::VMat::precompute(), PLearn::ConcatRowsVMatrix::putMat(), PLearn::RandomVar::RandomVar(), PLearn::RandomVariable::RandomVariable(), PLearn::RemapLastColumnVMatrix::RemapLastColumnVMatrix(), PLearn::RGBImage::RGBImage(), PLearn::RowMapSparseMatrix< real >::RowMapSparseMatrix(), PLearn::RowMapSparseValueMatrix< T >::RowMapSparseValueMatrix(), PLearn::TestDependencyCommand::run(), PLearn::TestDependenciesCommand::run(), PLearn::SDBVMFieldICBCTargets::SDBVMFieldICBCTargets(), PLearn::SourceVariable::SourceVariable(), PLearn::StrTableVMatrix::StrTableVMatrix(), PLearn::YMDDatedVMatrix::subDistrRelativeDates(), PLearn::Learner::test(), PLearn::SparseMatrix::toMat(), PLearn::MovingAverage::train(), PLearn::ManifoldParzen2::train(), PLearn::AdaBoost::train(), PLearn::UniformizeVMatrix::UniformizeVMatrix(), PLearn::StatsIterator::update(), PLearn::StatsItArray::update(), PLearn::PLearner::useOnTrain(), PLearn::Var::Var(), PLearn::UniformDistribution::variance(), PLearn::SpiralDistribution::variance(), PLearn::EmpiricalDistribution::variance(), PLearn::Distribution::variance(), PLearn::ConditionalDensityNet::variance(), PLearn::VMat::VMat(), PLearn::VMatLanguage::VMatLanguage(), PLearn::GhostScript::writeBitmapHexString1Bit(), and PLearn::YMDDatedVMatrix::YMDDatedVMatrix().

typedef RandomVar PLearn::MatRandomVar

Definition at line 418 of file RandomVar.h.

typedef bool(* PLearn::MeasurerCallbackFunction)(int t, const Vec &costs)

Definition at line 77 of file Measurer.h.

typedef Object*(* PLearn::NEW_OBJECT)()

Typedef for the "new instance" function type, which returns a default-initialized Object. Definition at line 53 of file TypeFactory.h. Referenced by PLearn::TypeFactory::register_type().

typedef vector< PP<OptionBase> > PLearn::OptionList

Definition at line 133 of file OptionBase.h. Referenced by PLearn::YMDDatedVMatrix::declareOptions(), PLearn::WeightedCostFunction::declareOptions(), PLearn::VVMatrix::declareOptions(), PLearn::VVec::declareOptions(), PLearn::VMatrixFromDistribution::declareOptions(), PLearn::VMatrix::declareOptions(), PLearn::PreprocessingVMatrix::declareOptions(), PLearn::VMatLanguage::declareOptions(), PLearn::VecStatsCollector::declareOptions(), PLearn::VecExtendedVMatrix::declareOptions(), PLearn::VecElementVariable::declareOptions(), PLearn::Variable::declareOptions(), PLearn::UpsideDownVMatrix::declareOptions(), PLearn::UniformVMatrix::declareOptions(), PLearn::UniformizeVMatrix::declareOptions(), PLearn::UniformDistribution::declareOptions(), PLearn::UnfoldedSumOfVariable::declareOptions(), PLearn::UnfoldedFuncVariable::declareOptions(), PLearn::UnequalConstantVariable::declareOptions(), PLearn::UnconditionalDistribution::declareOptions(), PLearn::UnaryVariable::declareOptions(), PLearn::UnaryHardSlopeVariable::declareOptions(), PLearn::UCISpecification::declareOptions(), PLearn::TransposeVMatrix::declareOptions(), PLearn::TrainValidTestSplitter::declareOptions(), PLearn::TrainTestSplitter::declareOptions(), PLearn::TrainTestBagsSplitter::declareOptions(), PLearn::Train::declareOptions(), PLearn::ToBagSplitter::declareOptions(), PLearn::TextSenseSequenceVMatrix::declareOptions(), PLearn::TestMethod::declareOptions(), PLearn::TestInTrainSplitter::declareOptions(), PLearn::TestingLearner::declareOptions(), PLearn::TemporalHorizonVMatrix::declareOptions(), PLearn::TangentLearner::declareOptions(), PLearn::SumOverBagsVariable::declareOptions(), PLearn::SumOfVariable::declareOptions(), PLearn::SubVMatrix::declareOptions(), PLearn::SubsampleVariable::declareOptions(), PLearn::SubMatVariable::declareOptions(), PLearn::SubMatTransposeVariable::declareOptions(), PLearn::SubInputVMatrix::declareOptions(), PLearn::QuantilesStatsIterator::declareOptions(), PLearn::LiftStatsIterator::declareOptions(), PLearn::MaxStatsIterator::declareOptions(), PLearn::MinStatsIterator::declareOptions(), PLearn::SharpeRatioStatsIterator::declareOptions(), PLearn::StderrStatsIterator::declareOptions(), PLearn::StddevStatsIterator::declareOptions(), PLearn::ExpMeanStatsIterator::declareOptions(), PLearn::MeanStatsIterator::declareOptions(), PLearn::StatsIterator::declareOptions(), PLearn::StatsCollector::declareOptions(), PLearn::StatefulLearner::declareOptions(), PLearn::StackedLearner::declareOptions(), PLearn::SquaredErrorCostFunction::declareOptions(), PLearn::SpiralDistribution::declareOptions(), PLearn::SpectralClustering::declareOptions(), PLearn::SparseVMatrix::declareOptions(), PLearn::SourceVMatrixSplitter::declareOptions(), PLearn::SourceVMatrix::declareOptions(), PLearn::SourceKernel::declareOptions(), PLearn::SortRowsVMatrix::declareOptions(), PLearn::SoftSlopeVariable::declareOptions(), PLearn::SoftSlopeIntegralVariable::declareOptions(), PLearn::Smoother::declareOptions(), PLearn::SigmoidPrimitiveKernel::declareOptions(), PLearn::SigmoidalKernel::declareOptions(), PLearn::ShiftAndRescaleVMatrix::declareOptions(), PLearn::ShellScript::declareOptions(), PLearn::SetOption::declareOptions(), PLearn::SequentialValidation::declareOptions(), PLearn::SequentialSplitter::declareOptions(), PLearn::SequentialModelSelector::declareOptions(), PLearn::SequentialLearner::declareOptions(), PLearn::SemiSupervisedProbClassCostVariable::declareOptions(), PLearn::SelectRowsVMatrix::declareOptions(), PLearn::SelectRowsFileIndexVMatrix::declareOptions(), PLearn::SelectInputSubsetLearner::declareOptions(), PLearn::SelectedOutputCostFunction::declareOptions(), PLearn::SelectColumnsVMatrix::declareOptions(), PLearn::ScaledGeneralizedDistanceRBFKernel::declareOptions(), PLearn::ScaledGaussianKernel::declareOptions(), PLearn::ScaledConditionalCDFSmoother::declareOptions(), PLearn::RunObject::declareOptions(), PLearn::RowsSubVMatrix::declareOptions(), PLearn::RowAtPositionVariable::declareOptions(), PLearn::ReshapeVariable::declareOptions(), PLearn::RepeatSplitter::declareOptions(), PLearn::RemoveRowsVMatrix::declareOptions(), PLearn::RemoveDuplicateVMatrix::declareOptions(), PLearn::RemapLastColumnVMatrix::declareOptions(), PLearn::RegularGridVMatrix::declareOptions(), PLearn::ReconstructionWeightsKernel::declareOptions(), PLearn::RealMapping::declareOptions(), PLearn::RangeVMatrix::declareOptions(), PLearn::QuadraticUtilityCostFunction::declareOptions(), PLearn::ProcessingVMatrix::declareOptions(), PLearn::PricingTransactionPairProfitFunction::declareOptions(), PLearn::PrecomputedVMatrix::declareOptions(), PLearn::PrecomputedKernel::declareOptions(), PLearn::PowVariable::declareOptions(), PLearn::PowDistanceKernel::declareOptions(), PLearn::PolynomialKernel::declareOptions(), PLearn::PlusConstantVariable::declareOptions(), PLearn::PLS::declareOptions(), PLearn::PLearnerOutputVMatrix::declareOptions(), PLearn::PLearner::declareOptions(), PLearn::PTester::declareOptions(), PLearn::PDistributionVariable::declareOptions(), PLearn::PDistribution::declareOptions(), PLearn::PConditionalDistribution::declareOptions(), PLearn::PCA::declareOptions(), PLearn::PairsVMatrix::declareOptions(), PLearn::Optimizer::declareOptions(), PLearn::OneHotVMatrix::declareOptions(), PLearn::OneHotVariable::declareOptions(), PLearn::OneHotSquaredLoss::declareOptions(), PLearn::ObjectGenerator::declareOptions(), PLearn::NormalizedDotProductKernel::declareOptions(), PLearn::NNet::declareOptions(), PLearn::NeuralNet::declareOptions(), PLearn::NeighborhoodSmoothnessNNet::declareOptions(), PLearn::NegLogProbCostFunction::declareOptions(), PLearn::NegKernel::declareOptions(), PLearn::NearestNeighborPredictionCost::declareOptions(), PLearn::NaryVariable::declareOptions(), PLearn::MultiInstanceVMatrix::declareOptions(), PLearn::MultiInstanceNNet::declareOptions(), PLearn::MovingAverageVMatrix::declareOptions(), PLearn::MovingAverage::declareOptions(), PLearn::MemoryVMatrix::declareOptions(), PLearn::MatRowVariable::declareOptions(), PLearn::MatrixSumOfVariable::declareOptions(), PLearn::MatrixOneHotSquaredLoss::declareOptions(), PLearn::MatrixElementsVariable::declareOptions(), PLearn::MarginPerceptronCostVariable::declareOptions(), PLearn::ManualBinner::declareOptions(), PLearn::LogOfGaussianDensityKernel::declareOptions(), PLearn::LocalNeighborsDifferencesVMatrix::declareOptions(), PLearn::LocallyWeightedDistribution::declareOptions(), PLearn::LLEKernel::declareOptions(), PLearn::LLE::declareOptions(), PLearn::LinearRegressor::declareOptions(), PLearn::LimitedGaussianSmoother::declareOptions(), PLearn::LiftStatsCollector::declareOptions(), PLearn::LiftBinaryCostFunction::declareOptions(), PLearn::LearnerProcessedVMatrix::declareOptions(), PLearn::Learner::declareOptions(), PLearn::LaplacianKernel::declareOptions(), PLearn::KPCATangentLearner::declareOptions(), PLearn::KNNVMatrix::declareOptions(), PLearn::KFoldSplitter::declareOptions(), PLearn::KernelVMatrix::declareOptions(), PLearn::KernelProjection::declareOptions(), PLearn::KernelPCA::declareOptions(), PLearn::Kernel::declareOptions(), PLearn::JulianizeVMatrix::declareOptions(), PLearn::JoinVMatrix::declareOptions(), PLearn::IsomapTangentLearner::declareOptions(), PLearn::Isomap::declareOptions(), PLearn::IsMissingVariable::declareOptions(), PLearn::IsAboveThresholdVariable::declareOptions(), PLearn::InterleaveVMatrix::declareOptions(), PLearn::IndexedVMatrix::declareOptions(), PLearn::IndexAtPositionVariable::declareOptions(), PLearn::HTryCombinations::declareOptions(), PLearn::HCoordinateDescent::declareOptions(), PLearn::HTryAll::declareOptions(), PLearn::HSetVal::declareOptions(), PLearn::HyperOptimizer::declareOptions(), PLearn::HistogramDistribution::declareOptions(), PLearn::GraphicalBiText::declareOptions(), PLearn::Grapher::declareOptions(), PLearn::GramVMatrix::declareOptions(), PLearn::GradientOptimizer::declareOptions(), PLearn::GetInputVMatrix::declareOptions(), PLearn::GeodesicDistanceKernel::declareOptions(), PLearn::GenerateDecisionPlot::declareOptions(), PLearn::GeneralizedOneHotVMatrix::declareOptions(), PLearn::GeneralizedDistanceRBFKernel::declareOptions(), PLearn::GaussMix::declareOptions(), PLearn::GaussianProcessRegressor::declareOptions(), PLearn::GaussianKernel::declareOptions(), PLearn::GaussianDistribution::declareOptions(), PLearn::GaussianDensityKernel::declareOptions(), PLearn::GaussianContinuum::declareOptions(), PLearn::Function::declareOptions(), PLearn::FractionSplitter::declareOptions(), PLearn::ForwardVMatrix::declareOptions(), PLearn::FinancePreprocVMatrix::declareOptions(), PLearn::FilterSplitter::declareOptions(), PLearn::FilteredVMatrix::declareOptions(), PLearn::FileVMatrix::declareOptions(), PLearn::FilePStreamBuf::declareOptions(), PLearn::ExtendedVMatrix::declareOptions(), PLearn::ExtendedVariable::declareOptions(), PLearn::ExplicitSplitter::declareOptions(), PLearn::Experiment::declareOptions(), PLearn::EqualConstantVariable::declareOptions(), PLearn::EntropyContrast::declareOptions(), PLearn::EmpiricalDistribution::declareOptions(), PLearn::EmbeddedSequentialLearner::declareOptions(), PLearn::EmbeddedLearner::declareOptions(), PLearn::ElementAtPositionVariable::declareOptions(), PLearn::DuplicateScalarVariable::declareOptions(), PLearn::DuplicateRowVariable::declareOptions(), PLearn::DuplicateColumnVariable::declareOptions(), PLearn::DivisiveNormalizationKernel::declareOptions(), PLearn::Distribution::declareOptions(), PLearn::DistanceKernel::declareOptions(), PLearn::DiskVMatrix::declareOptions(), PLearn::Dictionary::declareOptions(), PLearn::DBSplitter::declareOptions(), PLearn::DatedVMatrix::declareOptions(), PLearn::DatedJoinVMatrix::declareOptions(), PLearn::CutBelowThresholdVariable::declareOptions(), PLearn::CutAboveThresholdVariable::declareOptions(), PLearn::CumVMatrix::declareOptions(), PLearn::CrossReferenceVMatrix::declareOptions(), PLearn::ConvexBasisKernel::declareOptions(), PLearn::ConstantRegressor::declareOptions(), PLearn::ConjGradientOptimizer::declareOptions(), PLearn::ConditionalStatsCollector::declareOptions(), PLearn::ConditionalGaussianDistribution::declareOptions(), PLearn::ConditionalDensityNet::declareOptions(), PLearn::ConditionalCDFSmoother::declareOptions(), PLearn::ConcatRowsVMatrix::declareOptions(), PLearn::ConcatRowsSubVMatrix::declareOptions(), PLearn::ConcatOfVariable::declareOptions(), PLearn::ConcatColumnsVMatrix::declareOptions(), PLearn::CompactVMatrixPolynomialKernel::declareOptions(), PLearn::CompactVMatrixGaussianKernel::declareOptions(), PLearn::ClassMarginCostFunction::declareOptions(), PLearn::ClassifierFromDensity::declareOptions(), PLearn::ClassErrorCostFunction::declareOptions(), PLearn::CenteredVMatrix::declareOptions(), PLearn::BootstrapVMatrix::declareOptions(), PLearn::BootstrapSplitter::declareOptions(), PLearn::Binner::declareOptions(), PLearn::BinaryVariable::declareOptions(), PLearn::BatchVMatrix::declareOptions(), PLearn::AutoVMatrix::declareOptions(), PLearn::AsciiVMatrix::declareOptions(), PLearn::AffineTransformWeightPenalty::declareOptions(), PLearn::AdditiveNormalizationKernel::declareOptions(), PLearn::AddCostToLearner::declareOptions(), PLearn::AdaptGradientOptimizer::declareOptions(), PLearn::AdaBoost::declareOptions(), and PLearn::Object::readOptionVal().

typedef pair<real,StatsCollectorCounts*> PLearn::PairRealSCCType

Definition at line 65 of file StatsCollector.h. Referenced by sortIdComparator(), and PLearn::StatsCollector::sortIds().

typedef ofstream PLearn::pofstream

The stream classes. Definition at line 322 of file PLMPI.h.

typedef PP<Learner> PLearn::PPLearner

Definition at line 567 of file Learner.h.

typedef CostFunc PLearn::ProfitFunc

a profit function maps (output,target) to a profit ********************************************************************** FINANCIAL STUFF Definition at line 254 of file Kernel.h.

typedef PP<SDBVMField> PLearn::PSDBVMField

Definition at line 196 of file SDBVMat.h. Referenced by PLearn::SDBVMatrix::appendField().

typedef PP<SDBVMFieldDiscrete> PLearn::PSDBVMFieldDiscrete

Definition at line 661 of file SDBVMat.h.

typedef PP<SDBVMOutputCoder> PLearn::PSDBVMOutputCoder

Definition at line 139 of file SDBVMat.h.

typedef SimpleDB PLearn::SDB

A utility typedef for the common case. Definition at line 1178 of file SimpleDB.h. Referenced by PLearn::AutoSDBVMatrix::AutoSDBVMatrix(), halfShuffleRows(), PLearn::SDBVMatrix::SDBVMatrix(), and PLearn::SDBWithStats::SDBWithStats().

typedef PPointableSet::iterator PLearn::SetIterator

Definition at line 22 of file Set.h. Referenced by PLearn::Set::begin(), PLearn::TextSenseSequenceVMatrix::build_(), PLearn::GraphicalBiText::build_(), check_prob(), PLearn::GraphicalBiText::check_set_pA(), PLearn::GraphicalBiText::compute_BN_likelihood(), PLearn::GraphicalBiText::compute_efs_likelihood(), PLearn::GraphicalBiText::compute_likelihood(), PLearn::GraphicalBiText::compute_node_level(), PLearn::GraphicalBiText::compute_nodemap(), PLearn::GraphicalBiText::compute_pMC(), PLearn::GraphicalBiText::compute_pTC(), PLearn::GraphicalBiText::computeKL(), PLearn::WordNetOntology::computeWordSenseUniqueIds(), PLearn::GraphicalBiText::distribute_pS_on_ancestors(), PLearn::Set::end(), PLearn::WordNetOntology::extractAncestors(), PLearn::WordNetOntology::extractDescendants(), PLearn::WordNetOntology::extractStrictDescendants(), PLearn::WordNetOntology::extractWordHighLevelSenses(), PLearn::WordNetOntology::extractWordNounAndVerbHighLevelSenses(), PLearn::WordNetOntology::fillTempWordToHighLevelSensesTVecMap(), PLearn::WordNetOntology::fillTempWordToSensesTVecMap(), PLearn::Set::find(), PLearn::WordNetOntology::getCategoriesAtLevel(), PLearn::WordNetOntology::getCategoriesUnderLevel(), PLearn::GraphicalBiText::getDeepestCommonAncestor(), PLearn::WordNetOntology::getDescendantCategoriesAtLevel(), PLearn::WordNetOntology::getDownToUpParentCategoriesAtLevel(), PLearn::WordNetOntology::getPredominentSyntacticClassForWord(), PLearn::WordNetOntology::getSecondLevelSensesForWord(), PLearn::WordNetOntology::getSyntacticClassesForWord(), PLearn::WordNetOntology::getThirdLevelSensesForWord(), PLearn::WordNetOntology::getWordAncestors(), PLearn::WordNetOntology::getWordSenseIdForSenseKey(), PLearn::GraphicalBiText::init(), PLearn::WordNetOntology::intersectAncestorsAndSenses(), PLearn::WordNetOntology::isWordUnknown(), PLearn::WordNetOntology::load(), PLearn::Set::merge(), operator<<(), PLearn::WordNetOntology::overlappingSynsets(), PLearn::WordNetOntology::print(), print(), PLearn::GraphicalBiText::print(), PLearn::GraphicalBiText::print_sensemap(), PLearn::WordNetOntology::printInvertedSynsetOntology(), PLearn::GraphicalBiText::printNode(), PLearn::WordNetOntology::printNodes(), PLearn::WordNetOntology::printOntology(), PLearn::WordNetOntology::printSynsetAncestors(), PLearn::WordNetOntology::printWordAncestors(), PLearn::WordNetOntology::printWordOntology(), PLearn::WordNetOntology::propagatePOSTypes(), PLearn::WordNetOntology::reduceWordPolysemy(), PLearn::WordNetOntology::reduceWordPolysemy_preserveSenseOverlapping(), PLearn::ProbabilitySparseMatrix::removeExtra(), PLearn::WordNetOntology::removeNonReachableSynsets(), PLearn::ProbabilitySparseMatrix::removeRow(), samePos(), PLearn::WordNetOntology::save(), PLearn::GraphicalBiText::set_nodemap(), PLearn::GraphicalBiText::test_WSD(), PLearn::WordNetOntology::unvisitDownward(), update(), updateAndClearCounts(), and PLearn::WordNetOntology::visitUpward().

typedef map<int, real> PLearn::SparseVec

Definition at line 13 of file ProbabilitySparseMatrix.h.

typedef PP<StatsIterator> PLearn::StatsIt

Definition at line 354 of file StatsIterator.h. Referenced by PLearn::StatsItArray::StatsItArray().

typedef TTensor<real> PLearn::Tensor

Definition at line 402 of file Tensor.h.

typedef int PLearn::tFileHandle

Definition at line 47 of file MemoryMap.h. Referenced by MemoryMap().

typedef real(* PLearn::tRealFunc)(real)

Definition at line 394 of file pl_math.h. Referenced by PLearn::MixtureRandomVariable::EMBprop(), and PLearn::Function::verifyGradient().

typedef real(* PLearn::tRealReadFunc)(real, real)

Definition at line 395 of file pl_math.h.

typedef map<string,TypeMapEntry> PLearn::TypeMap

Definition at line 85 of file TypeFactory.h. Referenced by displayObjectHelp(), and PLearn::TypeFactory::getTypeMap().

typedef TVec<real> PLearn::Vec

Definition at line 774 of file TVec_decl.h. Referenced by PLearn::SourceKernel::addDataForKernelMatrix(), PLearn::RowMapSparseValueMatrix< T >::averageAcrossRowsAndColumns(), bootstrap(), PLearn::MatrixAffineTransformVariable::bprop(), PLearn::VMatrixFromDistribution::build_(), PLearn::GaussianContinuum::build_(), PLearn::ByteMemoryVMatrix::ByteMemoryVMatrix(), PLearn::CompactVMatrix::CompactVMatrix(), PLearn::CompressedVMatrix::CompressedVMatrix(), PLearn::VecCompressor::compressVec(), PLearn::Grapher::computeAutoGridrange(), PLearn::DivisiveNormalizationKernel::computeAverage(), PLearn::AdditiveNormalizationKernel::computeAverage(), PLearn::PCA::computeCostsFromOutputs(), PLearn::TestingLearner::computeOutput(), PLearn::TangentLearner::computeOutput(), PLearn::StatefulLearner::computeOutput(), PLearn::StackedLearner::computeOutput(), PLearn::PDistribution::computeOutput(), PLearn::KPCATangentLearner::computeOutput(), PLearn::IsomapTangentLearner::computeOutput(), PLearn::EmbeddedLearner::computeOutput(), PLearn::SequentialLearner::computeOutputAndCosts(), PLearn::ReconstructionWeightsKernel::computeWeights(), PLearn::SDBVMFieldICBCClassification::convertField(), PLearn::SDBVMFieldSumClaims::convertField(), PLearn::SDBVMFieldHasClaim::convertField(), PLearn::SDBVMFieldICBCTargets::convertField(), PLearn::SDBVMFieldDateGreater::convertField(), PLearn::SDBVMFieldDiscrete::convertField(), PLearn::SDBVMFieldDateDiff::convertField(), PLearn::SDBVMFieldMonths::convertField(), PLearn::SDBVMFieldDay::convertField(), PLearn::SDBVMFieldDate::convertField(), PLearn::SDBVMFieldFunc2::convertField(), PLearn::SDBVMFieldFunc1::convertField(), PLearn::SDBVMFieldSignedPower::convertField(), PLearn::SDBVMFieldPosAffine::convertField(), PLearn::SDBVMFieldAffine::convertField(), PLearn::SDBVMFieldDivSigma::convertField(), PLearn::SDBVMFieldNormalize::convertField(), PLearn::SDBVMFieldAsIs::convertField(), PLearn::SDBVMField::convertMissing(), PLearn::VarArray::copyFrom(), PLearn::TemporalHorizonVMatrix::dot(), PLearn::ConcatRowsVMatrix::dot(), PLearn::MatlabInterface::eigs_r11(), PLearn::NonRandomVariable::EMBprop(), PLearn::ConcatColumnsRandomVariable::EMBprop(), PLearn::ExtendedRandomVariable::EMBprop(), PLearn::SubVecRandomVariable::EMBprop(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::ElementWiseDivisionRandomVariable::EMBprop(), PLearn::LogRandomVariable::EMBprop(), PLearn::ExpRandomVariable::EMBprop(), PLearn::NegRandomVariable::EMBprop(), PLearn::RVArrayRandomElementRandomVariable::EMBprop(), PLearn::RandomElementOfRandomVariable::EMBprop(), PLearn::JointRandomVariable::EMBprop(), PLearn::WeightedCostFunction::evaluate(), PLearn::SquaredErrorCostFunction::evaluate(), PLearn::SigmoidPrimitiveKernel::evaluate(), PLearn::SigmoidalKernel::evaluate(), PLearn::SelectedOutputCostFunction::evaluate(), PLearn::QuadraticUtilityCostFunction::evaluate(), PLearn::PricingTransactionPairProfitFunction::evaluate(), PLearn::PrecomputedKernel::evaluate(), PLearn::PowDistanceKernel::evaluate(), PLearn::PolynomialKernel::evaluate(), PLearn::NormalizedDotProductKernel::evaluate(), PLearn::NegOutputCostFunction::evaluate(), PLearn::NegLogProbCostFunction::evaluate(), PLearn::NegKernel::evaluate(), PLearn::MulticlassErrorCostFunction::evaluate(), PLearn::LogOfGaussianDensityKernel::evaluate(), PLearn::LLEKernel::evaluate(), PLearn::LiftBinaryCostFunction::evaluate(), PLearn::LaplacianKernel::evaluate(), PLearn::GeodesicDistanceKernel::evaluate(), PLearn::GeneralizedDistanceRBFKernel::evaluate(), PLearn::GaussianDensityKernel::evaluate(), PLearn::DotProductKernel::evaluate(), PLearn::DistanceKernel::evaluate(), PLearn::DirectNegativeCostFunction::evaluate(), PLearn::DifferenceKernel::evaluate(), PLearn::ConvexBasisKernel::evaluate(), PLearn::CompactVMatrixPolynomialKernel::evaluate(), PLearn::CompactVMatrixGaussianKernel::evaluate(), PLearn::ClassMarginCostFunction::evaluate(), PLearn::ClassErrorCostFunction::evaluate(), PLearn::ClassDistanceProportionCostFunction::evaluate(), PLearn::Distribution::expectation(), PLearn::KernelProjection::forget(), PLearn::UnfoldedFuncVariable::fprop(), PLearn::MatrixSoftmaxVariable::fprop(), PLearn::MatrixAffineTransformFeedbackVariable::fprop(), PLearn::Function::fprop(), PLearn::FunctionalRandomVariable::FunctionalRandomVariable(), PLearn::GeneralizedOneHotVMatrix::GeneralizedOneHotVMatrix(), PLearn::VVMatrix::generateFilterIndexFile(), PLearn::MemoryVMatrix::getColumn(), PLearn::SDBVMFieldRemapIntervals::getIntervals(), PLearn::PreprocessingVMatrix::getNewRow(), PLearn::UpsideDownVMatrix::getNewRow(), PLearn::TransposeVMatrix::getNewRow(), PLearn::ThresholdVMatrix::getNewRow(), PLearn::TextSenseSequenceVMatrix::getNewRow(), PLearn::SubInputVMatrix::getNewRow(), PLearn::RowsSubVMatrix::getNewRow(), PLearn::RemapLastColumnVMatrix::getNewRow(), PLearn::RegularGridVMatrix::getNewRow(), PLearn::ProcessingVMatrix::getNewRow(), PLearn::PrecomputedVMatrix::getNewRow(), PLearn::PLearnerOutputVMatrix::getNewRow(), PLearn::PairsVMatrix::getNewRow(), PLearn::OneHotVMatrix::getNewRow(), PLearn::MultiInstanceVMatrix::getNewRow(), PLearn::MovingAverageVMatrix::getNewRow(), PLearn::LocalNeighborsDifferencesVMatrix::getNewRow(), PLearn::LearnerProcessedVMatrix::getNewRow(), PLearn::JulianizeVMatrix::getNewRow(), PLearn::FinancePreprocVMatrix::getNewRow(), PLearn::FileVMatrix::getNewRow(), PLearn::ExtendedVMatrix::getNewRow(), PLearn::DatedJoinVMatrix::getNewRow(), PLearn::CumVMatrix::getNewRow(), PLearn::ConcatColumnsVMatrix::getNewRow(), PLearn::AutoSDBVMatrix::getNewRow(), PLearn::AsciiVMatrix::getNewRow(), PLearn::Kernel::getParameters(), PLearn::CrossReferenceVMatrix::getRow(), PLearn::TrainTestBagsSplitter::getSplit(), PLearn::UniformVMatrix::getSubRow(), PLearn::SubVMatrix::getSubRow(), PLearn::SelectRowsFileIndexVMatrix::getSubRow(), PLearn::NistDB::getSubRow(), PLearn::InterleaveVMatrix::getSubRow(), PLearn::ForwardVMatrix::getSubRow(), PLearn::ConcatRowsSubVMatrix::getSubRow(), PLearn::Gnuplot::Gnuplot(), PLearn::QuantilesStatsIterator::init(), PLearn::LiftStatsIterator::init(), PLearn::MaxStatsIterator::init(), PLearn::MinStatsIterator::init(), PLearn::SharpeRatioStatsIterator::init(), PLearn::StderrStatsIterator::init(), PLearn::StddevStatsIterator::init(), PLearn::ExpMeanStatsIterator::init(), PLearn::MeanStatsIterator::init(), PLearn::SpiralDistribution::log_density(), PLearn::LocallyWeightedDistribution::log_density(), PLearn::EmpiricalDistribution::log_density(), PLearn::ManualBinner::ManualBinner(), PLearn::VarMeasurer::measure(), PLearn::Measurer::measure(), PLearn::CallbackMeasurer::measure(), PLearn::FilteredVMatrix::openIndex(), PLearn::Ker::operator()(), PLearn::Func::operator()(), PLearn::HTryAll::optimize(), PLearn::GradientOptimizer::optimize(), printvec(), PLearn::RandomVar::RandomVar(), PLearn::RandomVariable::RandomVariable(), PLearn::HTryCombinations::recursive_optimize(), removeRow(), PLearn::RemoveRowsVMatrix::RemoveRowsVMatrix(), PLearn::Variable::resize(), PLearn::RGBImagesVMatrix::RGBImagesVMatrix(), PLearn::RGBImageVMatrix::RGBImageVMatrix(), PLearn::VMat::rows(), PLearn::VMatLanguage::run(), PLearn::TestDependenciesCommand::run(), PLearn::SequentialValidation::run(), PLearn::KolmogorovSmirnovCommand::run(), PLearn::Experiment::run(), PLearn::RandomVarVMatrix::sample(), PLearn::SelectColumnsVMatrix::SelectColumnsVMatrix(), PLearn::SelectRowsVMatrix::SelectRowsVMatrix(), PLearn::SentencesBlocks::SentencesBlocks(), PLearn::UnconditionalDistribution::setInput(), PLearn::PConditionalDistribution::setInput(), PLearn::ConditionalDistribution::setInput(), PLearn::SDBVMOutputCoder::setOutput(), PLearn::ShiftAndRescaleVMatrix::ShiftAndRescaleVMatrix(), PLearn::GhostScript::show(), PLearn::ScaledConditionalCDFSmoother::smooth(), PLearn::LimitedGaussianSmoother::smooth(), PLearn::ConditionalCDFSmoother::smooth(), PLearn::SourceVariable::SourceVariable(), PLearn::SparseVMatrix::SparseVMatrix(), PLearn::CompactVMatrix::squareDifference(), PLearn::StrTableVMatrix::StrTableVMatrix(), PLearn::EmbeddedSequentialLearner::test(), PLearn::MovingAverage::train(), PLearn::ManifoldParzen2::train(), PLearn::HistogramDistribution::train(), PLearn::ConstantRegressor::train(), PLearn::RealMapping::transform(), PLearn::UniformizeVMatrix::UniformizeVMatrix(), PLearn::VecStatsCollector::update(), PLearn::StatsItArray::update(), PLearn::LiftStatsCollector::update(), PLearn::ConditionalStatsCollector::update(), PLearn::Distribution::use(), PLearn::Var::Var(), PLearn::VecElementVariable::VecElementVariable(), PLearn::VecExtendedVMatrix::VecExtendedVMatrix(), and PLearn::VVec::VVec().

typedef void(* PLearn::VOIDFUNC)()

Definition at line 53 of file StaticInitializer.h. Referenced by PLearn::StaticInitializer::StaticInitializer().

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Enumeration Type Documentation

enum PLearn::eNumericType

Enumeration values: NT_NOT_NUMERIC  NT_ORDINAL  NT_CARDINAL  NT_CURRENCY  NT_PREFIXED  NT_SUFFIXED  NT_RANGE  NT_TIME  NT_CODE  NT_PERCENT  NT_UNKNOWN_NUMERIC_TYPE  looks numeric, but none of the above (ana or something) Definition at line 66 of file TypesNumeriques.h.

enum PLearn::FieldType

A schema is simply a vector of field definitions. A field definition is a structure containing a field type and a precision. The meaning of precision is always the length in byte of the type. Type : What Precision is: Enumeration values: Unknown  StringType  CharacterType  SignedCharType  ShortType  IntType  FloatType  DoubleType  DateType  Definition at line 108 of file SimpleDB.h. Referenced by PLearn::RowIterator::getFieldType(), PLearn::FieldValue::operator *(), PLearn::FieldValue::operator+(), PLearn::FieldValue::operator-(), PLearn::FieldValue::operator/(), PLearn::FieldValue::operator<(), and PLearn::FieldValue::operator==().

enum PLearn::SDBVMOutputCoding

Code a real number into a vector. Possible codings are one-hot-like variations, and, yes, identity. At the moment, the coding is specified by a simple enum, but later could be upgraded to support derived classes as well. This class supports remapping MISSING_VALUEs that are passed to setOutput onto some arbitrary real number (including MISSING_VALUE). One-hot coding supports a special treatment regarding missing values: if a MISSING_VALUE is passed to setOutput, and the missing_values_mapping leaves it as-is, and one-hot coding is in effect, all the elements of the one-hot vector are set to MISSING_VALUE. Enumeration values: SDBVMUnknownCoding  SDBVMNumeric  straight output SDBVMOneHot  classic one-hot SDBVMOneHotMinus1  all but first element (which is skipped) Definition at line 65 of file SDBVMat.h. Referenced by PLearn::SDBVMFieldDiscrete::getOutputCoding(), and PLearn::SDBVMOutputCoder::getOutputCoding().

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Function Documentation

Var abs (  Var  v  )  [inline]

Definition at line 73 of file AbsVariable.h. Referenced by PLearn::RepeatSplitter::build_(), PLearn::KernelProjection::computeCostsFromOutputs(), PLearn::AddCostToLearner::computeCostsFromOutputs(), entropy(), PLearn::ConjGradientOptimizer::findDirection(), PLearn::ConjGradientOptimizer::fletcherSearchMain(), PLearn::ProductRandomVariable::invertible(), PLearn::Learner::measure(), PLearn::ConjGradientOptimizer::minCubic(), PLearn::ConjGradientOptimizer::minQuadratic(), PLearn::ConjGradientOptimizer::newtonSearch(), norm(), pownorm(), PLearn::SequentialModelSelector::sequenceCost(), PLearn::RealRange::span(), PLearn::SumAbsVariable::symbolicBprop(), PLearn::PLS::train(), PLearn::PCA::train(), and PLearn::HistogramDistribution::variance().

CostFunc PLearn::absolute_deviation (  int  singleoutputindex = -1  )  [inline]

Definition at line 133 of file DistanceKernel.cc.

string PLearn::abspath (  const string &  path  )

returns the absolute path of the (possibly relative) specified path. if it's a directory, then there will be a trailing slash. Definition at line 108 of file fileutils.cc. References append_slash(), chdir(), extract_directory(), extract_filename(), getcwd(), and isdir(). Referenced by PLearn::PLearner::build_(), PLearn::PTester::build_(), PLearn::GaussianProcessRegressor::build_(), PLearn::FileVMatrix::FileVMatrix(), locateDatasetAliasesDir(), matlabSave(), readFileAndMacroProcess(), PLearn::PLearner::setExperimentDirectory(), PLearn::PTester::setExperimentDirectory(), PLearn::Learner::setExperimentDirectory(), and PLearn::VMatrix::setMetaDataDir().

Var accessElement (  const Vec &  v, Var  index )  [inline]

Definition at line 82 of file VecElementVariable.h. Referenced by PLearn::AddCostToLearner::build_().

Var accessRow (  const Mat &  m, Var  index )  [inline]

Definition at line 84 of file MatRowVariable.h.

template<class T> void add (  const TMat< T > &  m1, const TMat< T > &  m2, TMat< T > &  destination )

Definition at line 4596 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void add (  const TVec< T > &  source1, T  source2, TVec< T > &  destination )

Definition at line 1484 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLearn::TVec< T >::resize().

template<class T> void add (  const TVec< T > &  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1467 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize().

SparseMatrix PLearn::add (  Array< SparseMatrix > &  matrices  )

add a bunch of sparse matrices and return result Definition at line 297 of file SparseMatrix.cc. References PLearn::SparseMatrix::beginRow, PLearn::TVec< T >::clear(), PLearn::TVec< T >::data(), PLearn::SparseMatrix::endRow, k, PLERROR, PLearn::TVec< T >::resize(), PLearn::SparseMatrix::row, PLearn::TVec< T >::size(), and PLearn::SparseMatrix::values. Referenced by PLearn::Hash< KeyType, DataType >::addAndResize(), PLearn::MinusRandomVariable::EMBprop(), PLearn::FieldConvertCommand::FieldConvertCommand(), PLearn::SequentialModelSelector::matlabSave(), PLearn::SequentialLearner::matlabSave(), matlabSave(), operator+(), PLearn::ScaledGradientOptimizer::optimize(), and substract().

template<class T> void addIfNonMissing (  const TVec< T > &  source, const TVec< int > &  nnonmissing, TVec< T >  destination )

Definition at line 5742 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::GradientOptimizer::optimize(), PLearn::ExpMeanStatsIterator::update(), and PLearn::MeanStatsIterator::update().

string addpostfix (  const string &  text, const string &  postfix )  [inline]

Returns a string with the postfix appended to each *line* of the text string. Definition at line 192 of file stringutils.h. References addprepostfix().

vector<string> addpostfix (  const vector< string > &  names, const string &  postfix )  [inline]

returns the list of names, but with an appended postfix Definition at line 178 of file stringutils.h. References addprepostfix().

string addprefix (  const string &  prefix, const string &  text )  [inline]

Returns a string with the prefix prepended to each *line* of the text string. Definition at line 187 of file stringutils.h. References addprepostfix().

vector<string> addprefix (  const string &  prefix, const vector< string > &  names )  [inline]

returns the list of names, but with a prepended prefix Definition at line 174 of file stringutils.h. References addprepostfix(). Referenced by displayObjectHelp(), and lsdir_fullpath().

TVec< string > PLearn::addprepostfix (  const string &  prefix, const TVec< string > &  names, const string &  postfix )

Definition at line 49 of file PExperiment.cc. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::end(), and PLearn::TVec< T >::size().

string PLearn::addprepostfix (  const string &  prefix, const string &  text, const string &  postfix )

Returns a string with the prefix prepended and the postfix appended to each *line* of the text string. Definition at line 546 of file stringutils.cc. References removenewline().

vector< string > PLearn::addprepostfix (  const string &  prefix, const vector< string > &  names, const string &  postfix )

returns the list of names, but with a prepended prefix and an appended postfix Definition at line 532 of file stringutils.cc. Referenced by addpostfix(), and addprefix().

template<class T> void addToColumns (  const TMat< T > &  mat, const TVec< T >  col, bool  ignored )

Definition at line 3720 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void addToDiagonal (  const TMat< T > &  mat, const TVec< T > &  lambda )

Definition at line 3634 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::mod(), and PLERROR.

template<class T> void addToDiagonal (  const TMat< T > &  mat, T  lambda )

Definition at line 3623 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), and PLearn::TMat< T >::mod(). Referenced by logPFittedGaussian(), and PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost().

template<class T> void addToMat (  const TMat< T > &  mat, T  scalar, bool  ignored )

Definition at line 3760 of file TMat_maths_impl.h.

template<class T> void addToRows (  const TMat< T > &  mat, const TVec< T >  row, bool  ignored )

Definition at line 3709 of file TMat_maths_impl.h. References PLearn::TMat< T >::length().

template<class T> void addXandX2IfNonMissing (  const TVec< T > &  source, const TVec< int > &  nnonmissing, TVec< T >  somme, TVec< T >  somme2 )

Definition at line 5762 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::StderrStatsIterator::update(), and PLearn::StddevStatsIterator::update().

Var affine_transform (  Var  vec, Var  transformation )  [inline]

first row of transformation is the bias. Definition at line 84 of file AffineTransformVariable.h. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), and PLearn::ConditionalDensityNet::build_().

Var affine_transform_weight_penalty (  Var  transformation, real  weight_decay, real  bias_decay = 0, bool  L1_penalty = false )  [inline]

weight decay and bias decay terms This has not been tested yet [Pascal: a tester]. Definition at line 82 of file AffineTransformWeightPenalty.h. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), and PLearn::ConditionalDensityNet::build_().

template<class T> void affineMatrixInitialize (  TMat< T >  W, bool  output_on_columns = true, real  scale = 1.0 )  [inline]

Definition at line 5554 of file TMat_maths_impl.h. References PLearn::TMat< T >::clear(), fill_random_uniform(), PLearn::TMat< T >::length(), and PLearn::TMat< T >::width().

void PLearn::affineNormalization (  Mat  data, Mat  W, Vec  bias, real  regularizer = 0 )

Definition at line 460 of file plapack.cc. References computeMeanAndCovar(), eigen_SymmMat(), sqrt(), and PLearn::TMat< T >::width().

VarArray PLearn::allSources (  const VarArray &  v  )

returns all sources that influence the given vars Definition at line 1094 of file VarArray.cc. References PLearn::VarArray::sources(), and PLearn::VarArray::unmarkAncestors().

string PLearn::append_slash (  const string &  path  )

appends a trailing slash to path if there isn't already one Definition at line 254 of file stringutils.cc. References slash, and slash_char. Referenced by abspath(), PLearn::DiskVMatrix::build_(), PLearn::VMatrix::getFieldInfos(), PLearn::VMatrix::loadFieldInfos(), PLearn::VMatrix::lockMetaDataDir(), PLearn::SequentialModelSelector::matlabSave(), PLearn::SequentialLearner::matlabSave(), matlabSave(), PLearn::PTester::perform(), PLearn::SequentialValidation::run(), PLearn::Experiment::run(), PLearn::VMatrix::saveFieldInfos(), PLearn::SequentialValidation::setExperimentDirectory(), PLearn::SequentialModelSelector::setExperimentDirectory(), PLearn::SequentialModelSelector::test(), PLearn::SequentialModelSelector::train(), PLearn::AdaBoost::train(), PLearn::VMatrix::unlockMetaDataDir(), and PLearn::PrecomputedVMatrix::usePrecomputed().

template<class T> void apply (  T(*  func)(const TVec< T > &, const TVec< T > &), const TMat< T > &  m1, const TMat< T > &  m2, TMat< T > &  dest )

Definition at line 5337 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and PLERROR.

template<class T> void apply (  T(*  func)(const TVec< T > &), const TMat< T > &  m, TMat< T > &  dest )

Definition at line 5327 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and PLERROR.

template<class T, class U, class V> void apply (  const TVec< T > &  src1, const TVec< U > &  src2, TVec< V > &  dest, V(*  func)(T, U) )

Transform a vector of T and a vector of U into a vector of V, through a binary function. Definition at line 1400 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

template<class T, class U> void apply (  const TVec< T > &  source, TVec< U > &  destination, U(*  func)(T) )

Transform a vector of T into a vector of U through a unary function. Definition at line 1385 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

template<class T, class U, class V> TVec<U> apply (  const TVec< T > &  vec, U(*  func)(V) )

Transform a vector of T into a vector of U through a unary function. Note: output type need not be specified in this case Definition at line 1376 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(). Referenced by PLearn::Kernel::apply(), PLearn::LogAddVariable::bprop(), PLearn::MixtureRandomVariable::EMBprop(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::MultinomialRandomVariable::EMUpdate(), PLearn::MixtureRandomVariable::EMUpdate(), PLearn::DiagonalNormalRandomVariable::EMUpdate(), exp(), PLearn::LogAddVariable::fprop(), and PLearn::Function::verifyGradient().

Var argmax (  Var  v  )  [inline]

Definition at line 76 of file ArgmaxVariable.h.

template<class T> int argmax (  const TMat< T > &  m  )

return maxi*width+maxj Definition at line 4000 of file TMat_maths_impl.h. References argmax(), and PLearn::TMat< T >::width().

template<class T> void argmax (  const TMat< T > &  mat, int &  maxi, int &  maxj )

Definition at line 3969 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> int argmax (  const TVec< T > &  vec, bool  ignore_missing )

Definition at line 575 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), MISSING_VALUE, and PLERROR.

template<class T> int argmax (  const TVec< T > &  vec  )

Definition at line 556 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by argmax(), classification_confusion_matrix(), columnArgmax(), PLearn::ClassMarginCostFunction::evaluate(), PLearn::ClassErrorCostFunction::evaluate(), PLearn::ClassDistanceProportionCostFunction::evaluate(), findClosestPairsOfDifferentClass(), PLearn::MiniBatchClassificationLossVariable::fprop(), PLearn::ClassificationLossVariable::fprop(), PLearn::ArgmaxVariable::fprop(), PLearn::GraphicalBiText::init(), loadUSPS(), PLearn::Grapher::plot_2D_classification(), rowArgmax(), PLearn::MaxVariable::symbolicBprop(), and PLearn::SequentialModelSelector::train().

Var argmin (  Var  v  )  [inline]

Definition at line 76 of file ArgminVariable.h.

template<class T> int argmin (  const TMat< T > &  m  )

return mini*width+minj Definition at line 3991 of file TMat_maths_impl.h. References argmin(), and PLearn::TMat< T >::width().

template<class T> void argmin (  const TMat< T > &  mat, int &  mini, int &  minj )

Stores the position of the min in the 'mini' & 'minj' arg. Definition at line 3947 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> int argmin (  const TVec< T > &  vec, bool  ignore_missing )

Definition at line 625 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), MISSING_VALUE, and PLERROR.

template<class T> int argmin (  const TVec< T > &  vec  )

Definition at line 606 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by argmin(), columnArgmin(), PLearn::ArgminVariable::fprop(), PLearn::HTryAll::optimize(), PLearn::HTryCombinations::recursive_optimize(), rowArgmin(), PLearn::MinVariable::symbolicBprop(), and PLearn::SequentialModelSelector::train().

Var argminOf (  Var  v, Var  expression, Var  values_of_v, VarArray  inputs )  [inline]

returns the value of v within the_values_of_v that gives the lowest value of expression (which may depend on inputs). Definition at line 86 of file ArgminOfVariable.h.

void PLearn::autocorrelation_function (  const VMat &  data, Mat &  acf )

Definition at line 611 of file VMat_maths.cc. References count, k, PLearn::VMat::length(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), sqrt(), square(), and PLearn::VMat::width().

template<class T> void averageAcrossRowsAndColumns (  const TMat< T > &  mat, TVec< T > &  avg_across_rows, TVec< T > &  avg_across_columns, bool  ignored )

Definition at line 3684 of file TMat_maths_impl.h. References PLearn::TVec< T >::clear(), PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLearn::TVec< T >::resize(), and PLearn::TMat< T >::width().

template<class T> T avgdev (  const TVec< T > &  vec, T  meanval )

Definition at line 349 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

string PLearn::backslash_to_slash (  string  str  )

replaces all backslashes with slash Definition at line 355 of file stringutils.cc.

Var binary_classification_loss (  Var  network_output, Var  classnum )  [inline]

Definition at line 78 of file BinaryClassificationLossVariable.h. References PLERROR. Referenced by PLearn::NNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), and PLearn::MultiInstanceNNet::build_().

template<class T> int binary_search (  const TMat< T > &  src, int  c, T  x )

Definition at line 313 of file TMat_sort.h. References k, PLearn::TMat< T >::length(), and x.

template<class T> int binary_search (  const TVec< T > &  src, T  x )

Definition at line 281 of file TMat_sort.h. References k, PLearn::TVec< T >::length(), and x. Referenced by estimatedCumProb(), PLearn::SDBVMFieldRemapIntervals::getDiscreteValue(), positionOfClosestElement(), and rebalanceNClasses().

real binomial_sample (  real  prob1  )  [inline]

alias Definition at line 106 of file random.h. References bnldev(). Referenced by PLearn::CompactVMatrix::perturb().

template<class T> void binread (  istream &  in, TVec< T > &  v )

Definition at line 172 of file TVec_impl.h. References binread(), PLearn::TVec< T >::data(), and PLearn::TVec< T >::resize().

void binread (  FILE *  in, double *  x, int  n )  [inline]

Definition at line 226 of file pl_io.h. References binread(), and x.

void binread (  FILE *  in, float *  x, int  n )  [inline]

Definition at line 224 of file pl_io.h. References x.

void binread (  FILE *  in, unsigned short *  x, int  n )  [inline]

Definition at line 220 of file pl_io.h. References x.

void binread (  FILE *  in, short *  x, int  n )  [inline]

Definition at line 218 of file pl_io.h. References x.

void binread (  FILE *  in, unsigned int *  x, int  n )  [inline]

Definition at line 216 of file pl_io.h. References x.

void binread (  FILE *  in, int *  x, int  n )  [inline]

Definition at line 214 of file pl_io.h. References x.

void binread (  FILE *  in, double &  x )  [inline]

Definition at line 203 of file pl_io.h. References binread(), and x.

void binread (  FILE *  in, float &  x )  [inline]

Definition at line 201 of file pl_io.h. References x.

void binread (  FILE *  in, bool &  x )  [inline]

Definition at line 194 of file pl_io.h. References binread(), and x.

void binread (  FILE *  in, unsigned short &  x )  [inline]

Definition at line 189 of file pl_io.h. References x.

void binread (  FILE *  in, short &  x )  [inline]

Definition at line 187 of file pl_io.h. References x.

void binread (  FILE *  in, unsigned int &  x )  [inline]

Definition at line 185 of file pl_io.h. References x.

void binread (  FILE *  in, int &  x )  [inline]

Definition at line 183 of file pl_io.h. References x.

void binread (  FILE *  in, unsigned char &  x )  [inline]

Definition at line 181 of file pl_io.h. References x.

void binread (  FILE *  in, char &  x )  [inline]

Definition at line 179 of file pl_io.h. References x.

template<class A, class B> void binread (  FILE *  in, pair< A, B > &  x )  [inline]

Definition at line 173 of file pl_io.h. References binread(), and x.

template<class T> void binread (  FILE *  in, T *  x, int  n )  [inline]

Definition at line 165 of file pl_io.h. References binread(), and x.

void binread (  istream &  in, double *  x, int  n )  [inline]

Definition at line 140 of file pl_io.h. References binread(), and x.

void binread (  istream &  in, float *  x, int  n )  [inline]

Definition at line 138 of file pl_io.h. References x.

void binread (  istream &  in, unsigned short *  x, int  n )  [inline]

Definition at line 134 of file pl_io.h. References x.

void binread (  istream &  in, short *  x, int  n )  [inline]

Definition at line 132 of file pl_io.h. References x.

void binread (  istream &  in, unsigned int *  x, int  n )  [inline]

Definition at line 130 of file pl_io.h. References x.

void binread (  istream &  in, int *  x, int  n )  [inline]

Definition at line 128 of file pl_io.h. References x.

void binread (  istream &  in, double &  x )  [inline]

Definition at line 117 of file pl_io.h. References binread(), and x.

void binread (  istream &  in, float &  x )  [inline]

Definition at line 115 of file pl_io.h. References x.

void binread (  istream &  in, bool &  x )  [inline]

Definition at line 108 of file pl_io.h. References binread(), and x.

void binread (  istream &  in, unsigned short &  x )  [inline]

Definition at line 102 of file pl_io.h. References x.

void binread (  istream &  in, short &  x )  [inline]

Definition at line 100 of file pl_io.h. References x.

void binread (  istream &  in, unsigned int &  x )  [inline]

Definition at line 98 of file pl_io.h. References x.

void binread (  istream &  in, int &  x )  [inline]

Definition at line 96 of file pl_io.h. References x.

void binread (  istream &  in, unsigned char &  x )  [inline]

Definition at line 94 of file pl_io.h. References x.

void binread (  istream &  in, char &  x )  [inline]

Definition at line 92 of file pl_io.h. References x.

template<class A, class B> void binread (  istream &  in, pair< A, B > &  x )  [inline]

Definition at line 86 of file pl_io.h. References binread(), and x.

template<class T> void binread (  istream &  in, T *  x, int  n )  [inline]

Definition at line 78 of file pl_io.h. References x. Referenced by binread(), binread_compressed(), binread_double(), binreadField(), PLearn::RowMapSparseMatrix< real >::read(), read_compr_mode_and_size(), and PLearn::Learner::test().

void binread_ (  PStream &  in, unsigned long *  x, unsigned int  n, unsigned char  typecode )

void binread_ (  PStream &  in, long *  x, unsigned int  n, unsigned char  typecode )

void binread_ (  PStream &  in, unsigned int *  x, unsigned int  n, unsigned char  typecode )

void binread_ (  PStream &  in, int *  x, unsigned int  n, unsigned char  typecode )

void binread_ (  PStream &  in, unsigned short *  x, unsigned int  n, unsigned char  typecode )

void binread_ (  PStream &  in, short *  x, unsigned int  n, unsigned char  typecode )

void binread_ (  PStream &  in, unsigned char *  x, unsigned int  n, unsigned char  typecode )  [inline]

Definition at line 877 of file PStream.h. References binread_(), and x.

void binread_ (  PStream &  in, signed char *  x, unsigned int  n, unsigned char  typecode )  [inline]

Definition at line 874 of file PStream.h. References binread_(), and x.

void binread_ (  PStream &  in, char *  x, unsigned int  n, unsigned char  typecode )  [inline]

Definition at line 861 of file PStream.h. References PLERROR, PLearn::PStream::read(), and x.

template<class Iterator> void binread_ (  PStream &  in, Iterator  it, unsigned int  n, unsigned char  typecode )

Definition at line 847 of file PStream.h. References PLERROR.

void PLearn::binread_ (  PStream &  in, float *  x, unsigned int  n, unsigned char  typecode )

Definition at line 1457 of file PStream.cc. References endianswap(), PLERROR, PLearn::PStream::read(), val, and x.

void PLearn::binread_ (  PStream &  in, double *  x, unsigned int  n, unsigned char  typecode )

The binread_ for float and double are special. Definition at line 1411 of file PStream.cc. References endianswap(), PLERROR, val, and x.

void PLearn::binread_ (  PStream &  in, bool *  x, unsigned int  n, unsigned char  typecode )

Definition at line 1360 of file PStream.cc. References PLearn::PStream::get(), PLERROR, and x. Referenced by binread_(), PLearn::TMat< pair< real, real > >::read(), and readSequence().

void PLearn::binread_compressed (  FILE *  in, float *  data, int  l )

Definition at line 380 of file pl_io.cc. References binread(), mode, PLERROR, read_compr_mode_and_size(), and val.

void PLearn::binread_compressed (  FILE *  in, double *  data, int  l )

Definition at line 332 of file pl_io.cc. References binread(), mode, PLERROR, and read_compr_mode_and_size().

void PLearn::binread_compressed (  istream &  in, float *  data, int  l )

Definition at line 217 of file pl_io.cc. References binread(), mode, PLERROR, read_compr_mode_and_size(), and val.

void PLearn::binread_compressed (  istream &  in, double *  data, int  l )

Definition at line 129 of file pl_io.cc. References binread(), mode, PLERROR, and read_compr_mode_and_size(). Referenced by PLearn::DiskVMatrix::getNewRow().

template<class T> void binread_double (  istream &  in, TVec< T > &  v )

Definition at line 195 of file TVec_impl.h. References binread(), binread_double(), PLearn::TVec< T >::data(), and PLearn::TVec< T >::resize().

void binread_double (  FILE *  in, float *  x, int  n )  [inline]

Definition at line 232 of file pl_io.h. References binread(), and x.

void binread_double (  FILE *  in, double *  x, int  n )  [inline]

Definition at line 230 of file pl_io.h. References x.

void binread_double (  FILE *  in, float &  x )  [inline]

Definition at line 209 of file pl_io.h. References binread_double(), and x.

void binread_double (  FILE *  in, double &  x )  [inline]

Definition at line 207 of file pl_io.h. References x.

void binread_double (  istream &  in, float *  x, int  n )  [inline]

Definition at line 146 of file pl_io.h. References binread(), and x.

void binread_double (  istream &  in, double *  x, int  n )  [inline]

Definition at line 144 of file pl_io.h. References x.

void binread_double (  istream &  in, float &  x )  [inline]

Definition at line 123 of file pl_io.h. References binread_double(), and x.

void binread_double (  istream &  in, double &  x )  [inline]

Definition at line 121 of file pl_io.h. References x. Referenced by binread_double(), and binreadField_double().

template<class T> void binreadField (  istream &  in, const string &  fieldname, T &  x )

Definition at line 244 of file pl_io_deprecated.h. References binread(), readFieldName(), and x.

template<class T> void binreadField_double (  istream &  in, const string &  fieldname, T &  x )

Definition at line 252 of file pl_io_deprecated.h. References binread_double(), readFieldName(), and x.

template<class T> void binwrite (  ostream &  out, const TVec< T > &  v )

Definition at line 161 of file TVec_impl.h. References binwrite(), PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

void binwrite (  FILE *  out, const double *  x, int  n )  [inline]

Definition at line 225 of file pl_io.h. References binwrite(), and x.

void binwrite (  FILE *  out, const float *  x, int  n )  [inline]

Definition at line 223 of file pl_io.h. References x.

void binwrite (  FILE *  out, const unsigned short *  x, int  n )  [inline]

Definition at line 219 of file pl_io.h. References x.

void binwrite (  FILE *  out, const short *  x, int  n )  [inline]

Definition at line 217 of file pl_io.h. References x.

void binwrite (  FILE *  out, const unsigned int *  x, int  n )  [inline]

Definition at line 215 of file pl_io.h. References x.

void binwrite (  FILE *  out, const int *  x, int  n )  [inline]

multi-element versions, giving address and number of elements Definition at line 213 of file pl_io.h. References x.

void binwrite (  FILE *  out, double  x )  [inline]

Definition at line 202 of file pl_io.h. References binwrite(), and x.

void binwrite (  FILE *  out, float  x )  [inline]

Definition at line 200 of file pl_io.h. References x.

void binwrite (  FILE *  out, bool  x )  [inline]

note that bool are saved as unsigned short Definition at line 191 of file pl_io.h. References binwrite(), and x.

void binwrite (  FILE *  out, unsigned short  x )  [inline]

Definition at line 188 of file pl_io.h. References x.

void binwrite (  FILE *  out, short  x )  [inline]

Definition at line 186 of file pl_io.h. References x.

void binwrite (  FILE *  out, unsigned int  x )  [inline]

Definition at line 184 of file pl_io.h. References x.

void binwrite (  FILE *  out, int  x )  [inline]

Definition at line 182 of file pl_io.h. References x.

void binwrite (  FILE *  out, unsigned char  x )  [inline]

Definition at line 180 of file pl_io.h. References x.

void binwrite (  FILE *  out, char  x )  [inline]

binwrite and binread for a few basic types Definition at line 178 of file pl_io.h. References x.

template<class A, class B> void binwrite (  FILE *  out, const pair< A, B >  x )  [inline]

Definition at line 169 of file pl_io.h. References binwrite(), and x.

template<class T> void binwrite (  FILE *  out, const T *  x, int  n )  [inline]

general purpose (but less efficient) version for pointers to things that have a binwrite/binread function Definition at line 161 of file pl_io.h. References binwrite(), and x.

void binwrite (  ostream &  out, const double *  x, int  n )  [inline]

Definition at line 139 of file pl_io.h. References binwrite(), and x.

void binwrite (  ostream &  out, const float *  x, int  n )  [inline]

Definition at line 137 of file pl_io.h. References x.

void binwrite (  ostream &  out, const unsigned short *  x, int  n )  [inline]

Definition at line 133 of file pl_io.h. References x.

void binwrite (  ostream &  out, const short *  x, int  n )  [inline]

Definition at line 131 of file pl_io.h. References x.

void binwrite (  ostream &  out, const unsigned int *  x, int  n )  [inline]

Definition at line 129 of file pl_io.h. References x.

void binwrite (  ostream &  out, const int *  x, int  n )  [inline]

multi-element versions, giving address and number of elements Definition at line 127 of file pl_io.h. References x.

void binwrite (  ostream &  out, double  x )  [inline]

Definition at line 116 of file pl_io.h. References binwrite(), and x.

void binwrite (  ostream &  out, float  x )  [inline]

Definition at line 114 of file pl_io.h. References x.

void binwrite (  ostream &  out, bool  x )  [inline]

note that bool are saved as unsigned short Definition at line 104 of file pl_io.h. References binwrite(), and x.

void binwrite (  ostream &  out, unsigned short  x )  [inline]

Definition at line 101 of file pl_io.h. References x.

void binwrite (  ostream &  out, short  x )  [inline]

Definition at line 99 of file pl_io.h. References x.

void binwrite (  ostream &  out, unsigned int  x )  [inline]

Definition at line 97 of file pl_io.h. References x.

void binwrite (  ostream &  out, int  x )  [inline]

Definition at line 95 of file pl_io.h. References x.

void binwrite (  ostream &  out, unsigned char  x )  [inline]

Definition at line 93 of file pl_io.h. References x.

void binwrite (  ostream &  out, char  x )  [inline]

binwrite and binread for a few basic types Definition at line 91 of file pl_io.h. References x.

template<class A, class B> void binwrite (  ostream &  out, const pair< A, B >  x )  [inline]

Definition at line 82 of file pl_io.h. References binwrite(), and x.

template<class T> void binwrite (  ostream &  out, const T *  x, int  n )  [inline]

general purpose (but less efficient) version for pointers to things that have a binwrite/binread function Definition at line 74 of file pl_io.h. References x. Referenced by binwrite(), binwrite_compressed(), binwrite_double(), binwriteField(), main(), PLearn::Learner::test(), PLearn::RowMapSparseMatrix< real >::write(), and write_compr_mode_and_size().

void binwrite_ (  PStream &  out, double *  x, unsigned int  n )  [inline]

Definition at line 841 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const double *  x, unsigned int  n )  [inline]

Definition at line 839 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, float *  x, unsigned int  n )  [inline]

Definition at line 836 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const float *  x, unsigned int  n )  [inline]

Definition at line 834 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, unsigned long *  x, unsigned int  n )  [inline]

Definition at line 831 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const unsigned long *  x, unsigned int  n )  [inline]

Definition at line 829 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, long *  x, unsigned int  n )  [inline]

Definition at line 826 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const long *  x, unsigned int  n )  [inline]

Definition at line 824 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, unsigned int *  x, unsigned int  n )  [inline]

Definition at line 821 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const unsigned int *  x, unsigned int  n )  [inline]

Definition at line 819 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, int *  x, unsigned int  n )  [inline]

Definition at line 816 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const int *  x, unsigned int  n )  [inline]

Definition at line 814 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, unsigned short *  x, unsigned int  n )  [inline]

Definition at line 811 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const unsigned short *  x, unsigned int  n )  [inline]

Definition at line 809 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, short *  x, unsigned int  n )  [inline]

Definition at line 806 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const short *  x, unsigned int  n )  [inline]

Definition at line 804 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, unsigned char *  x, unsigned int  n )  [inline]

Definition at line 801 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const unsigned char *  x, unsigned int  n )  [inline]

Definition at line 799 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, signed char *  x, unsigned int  n )  [inline]

Definition at line 796 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const signed char *  x, unsigned int  n )  [inline]

Definition at line 794 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, char *  x, unsigned int  n )  [inline]

Definition at line 791 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const char *  x, unsigned int  n )  [inline]

Definition at line 789 of file PStream.h. References PLearn::PStream::write(), and x.

void binwrite_ (  PStream &  out, const bool *  x, unsigned int  n )  [inline]

Definition at line 778 of file PStream.h. References PLearn::PStream::put(), and x.

template<class Iterator> void binwrite_ (  PStream &  out, Iterator &  it, unsigned int  n )

Serialization of sequences *. Definition at line 765 of file PStream.h. References PLearn::PStream::outmode. Referenced by PLearn::TMat< pair< real, real > >::write(), and writeSequence().

void PLearn::binwrite_compressed (  FILE *  out, const float *  data, int  l )

Definition at line 420 of file pl_io.cc. References PLERROR.

void PLearn::binwrite_compressed (  FILE *  out, const double *  data, int  l )

Definition at line 375 of file pl_io.cc. References PLERROR.

void PLearn::binwrite_compressed (  ostream &  out, const float *  data, int  l )

Definition at line 257 of file pl_io.cc. References binwrite(), val, and write_compr_mode_and_size().

void PLearn::binwrite_compressed (  ostream &  out, const double *  data, int  l )

version for compressed array (efficient for sparse data, and small integer values) (format is detailed in .cc, see write_compr_mode_and_size function in general.cc) Definition at line 172 of file pl_io.cc. References binwrite(), val, and write_compr_mode_and_size(). Referenced by PLearn::DiskVMatrix::appendRow().

template<class T> void binwrite_double (  ostream &  out, const TVec< T > &  v )

Definition at line 184 of file TVec_impl.h. References binwrite(), binwrite_double(), PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

void binwrite_double (  FILE *  out, const float *  x, int  n )  [inline]

Definition at line 231 of file pl_io.h. References binwrite(), and x.

void binwrite_double (  FILE *  out, const double *  x, int  n )  [inline]

Definition at line 229 of file pl_io.h. References x.

void binwrite_double (  FILE *  out, float  x )  [inline]

Definition at line 208 of file pl_io.h. References binwrite_double(), and x.

void binwrite_double (  FILE *  out, double  x )  [inline]

Definition at line 206 of file pl_io.h. References x.

void binwrite_double (  ostream &  out, const float *  x, int  n )  [inline]

Definition at line 145 of file pl_io.h. References binwrite(), and x.

void binwrite_double (  ostream &  out, const double *  x, int  n )  [inline]

Definition at line 143 of file pl_io.h. References x.

void binwrite_double (  ostream &  out, float  x )  [inline]

Definition at line 122 of file pl_io.h. References binwrite_double(), and x.

void binwrite_double (  ostream &  out, double  x )  [inline]

Definition at line 120 of file pl_io.h. References x. Referenced by binwrite_double(), and binwriteField_double().

template<class T> void binwriteField (  ostream &  out, const string &  fieldname, const T &  x )

generic field BINARY writing and reading Definition at line 240 of file pl_io_deprecated.h. References binwrite(), writeFieldName(), and x.

template<class T> void binwriteField_double (  ostream &  out, const string &  fieldname, const T &  x )

Definition at line 248 of file pl_io_deprecated.h. References binwrite_double(), writeFieldName(), and x.

real PLearn::bnldev (  real  pp, int  n = 1 )

returns a binomial random number with probability = 'pp' and trials number = 'n' Definition at line 468 of file random.cc. References exp(), log(), log_gamma(), Pi, sqrt(), and uniform_sample(). Referenced by binomial_sample().

VMat PLearn::bootstrap (  VMat  d, bool  reorder = true, bool  norepeat = true )

returns a SelectRowsVMatrix that has d's rows bootstrapped (sample with replacement and optionally re-ordered). Optionally the repeated rows are eliminated (this is actually done by shuffling and taking the first 2/3 of the rows, so the length() will be always the same). Note that the default values are fine for "on-line" learning algorithms but does not correspond to the usual "bootstrap". Definition at line 782 of file VMat_maths.cc. References PLearn::VMat::length(), PLearn::TVec< T >::resize(), PLearn::VMat::rows(), shuffleElements(), sortElements(), PLearn::TVec< T >::subVec(), uniform_multinomial_sample(), and Vec.

template<class T> void bootstrap_rows (  const TMat< T > &  source, TMat< T >  destination )

sample with replacement the rows of source and put them in destination. Definition at line 116 of file random.h. References PLearn::TMat< T >::length(), PLearn::TMat< T >::resize(), uniform_multinomial_sample(), and PLearn::TMat< T >::width().

real PLearn::bounded_uniform (  real  a, real  b )

returns a random number uniformly distributed between a and b Definition at line 287 of file random.cc. References RNMX, and uniform_sample(). Referenced by PLearn::UniformSampleVariable::fprop(), PLearn::UniformDistribution::generate(), and PLearn::SpiralDistribution::generate().

template<class T> void bprop_tanh (  const TVec< T > &  tanh_x, const TVec< T > &  d_tanh_x, TVec< T > &  d_x )

Definition at line 939 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize().

void bprop_update_layer (  real *  dy, real *  x, real *  dx, real *  w, int  n_y, int  n_x, real  learning_rate, real  weight_decay )  [inline]

Definition at line 398 of file TMat_maths_specialisation.h. References prefetchnta, and x.

template<class T> void bpropCholeskyDecomposition (  const TMat< T > &  A, const TMat< T > &  L, TMat< T > &  dC_dA, TMat< T > &  dC_dL )

Definition at line 4862 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), and PLearn::TMat< T >::resize().

template<class T> void bpropCholeskySolve (  const TMat< T > &  L, const TVec< T > &  x, const TVec< T > &  y, TMat< T > &  dC_dL, TVec< T > &  dC_db, TVec< T > &  dC_dx )

Definition at line 5027 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), k, PLearn::TMat< T >::length(), and x.

char byte_order (   )  [inline]

Definition at line 53 of file byte_order.h. References BIG_ENDIAN_ORDER, and LITTLE_ENDIAN_ORDER. Referenced by PLearn::IntVecFile::append(), PLearn::DiskVMatrix::build_(), PLearn::IntVecFile::getVec(), PLearn::IntVecFile::IntVecFile(), PLearn::PStream::operator>>(), PLearn::TMat< pair< real, real > >::read(), readSequence(), PLearn::TMat< pair< real, real > >::write(), PLearn::IntVecFile::writeFileSignature(), and writeSequence().

string PLearn::center (  const string &  s, size_t  width, char  padding = ' ' )

Definition at line 72 of file stringutils.cc. Referenced by PLearn::TextProgressBarPlugin::addProgressBar(), computeLocalPrincipalComponents(), PLearn::GaussMix::computeMeansAndCovariances(), displayVarGraph(), PLearn::ConditionalDensityNet::initialize_mu(), OldDisplayVarGraph(), operator<<(), PLearn::ManifoldParzen2::train(), and PLearn::TextProgressBarPlugin::update().

int PLearn::chdir (  const string &  path  )

change current directory Definition at line 100 of file fileutils.cc. References PLERROR. Referenced by abspath(), and readAndMacroProcess().

void check_prob (  Set  Y, const map< int, real > &  pYx )  [inline]

Definition at line 487 of file ProbabilitySparseMatrix.h. References PLearn::Set::contains(), and PLERROR.

void check_prob (  ProbabilitySparseMatrix &  pYX, string  Yname, string  Xname )  [inline]

Definition at line 463 of file ProbabilitySparseMatrix.h. References PLearn::Set::begin(), PLearn::Set::end(), PLearn::ProbabilitySparseMatrix::getPYx(), PLERROR, SetIterator, x, and PLearn::ProbabilitySparseMatrix::X.

template<class T> void choleskyDecomposition (  const TMat< T > &  A, TMat< T > &  L )

Definition at line 4794 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, PLWARNING, PLearn::TMat< T >::resize(), sqrt(), sum(), and PLearn::TMat< T >::width(). Referenced by choleskyInvert(), choleskySolve(), logOfNormal(), solveLinearSystemByCholesky(), and solveTransposeLinearSystemByCholesky().

template<class T> TMat<T> choleskyInvert (  const TMat< T > &  A  )

Definition at line 5164 of file TMat_maths_impl.h. References choleskyInvert(), and PLearn::TMat< T >::length().

template<class T> real choleskyInvert (  const TMat< T > &  A, TMat< T > &  Ainv )

Definition at line 5094 of file TMat_maths_impl.h. References choleskyDecomposition(), PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), log(), PLearn::TMat< T >::resize(), and sum(). Referenced by choleskyInvert().

template<class T> void choleskySolve (  const TMat< T > &  L, TVec< T >  b, TVec< T >  x )  [inline]

Parameters: x  So that y be optional Definition at line 5733 of file TMat_maths_impl.h. References choleskySolve(), and x.

template<class T> TVec<T> choleskySolve (  const TMat< T > &  A, const TVec< T > &  b )

Definition at line 5151 of file TMat_maths_impl.h. References choleskyDecomposition(), choleskySolve(), PLearn::TMat< T >::length(), and x.

template<class T> void choleskySolve (  const TMat< T > &  L, const TMat< T > &  B, TMat< T > &  X, TVec< T > &  y )

Definition at line 4953 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, sum(), and PLearn::TMat< T >::width().

template<class T> void choleskySolve (  const TMat< T > &  L, TVec< T >  b, TVec< T >  x, TVec< T > &  y )

Definition at line 4915 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, sum(), PLearn::TMat< T >::width(), and x. Referenced by choleskySolve(), logOfNormal(), solveLinearSystemByCholesky(), and solveTransposeLinearSystemByCholesky().

CostFunc class_distance_proportion (   )  [inline]

if outputs are neg distances to each class: dist_to_correct_class/(dist_to_correct_class+dist_to_closest_other_class) Definition at line 79 of file ClassDistanceProportionCostFunction.h.

CostFunc class_error (  bool  output_is_classnum = false  )  [inline]

Definition at line 98 of file ClassErrorCostFunction.h. Referenced by PLearn::ClassifierFromDensity::computeCostsFromOutputs().

CostFunc class_lift (  bool  make_positive = false  )  [inline]

Definition at line 87 of file LiftBinaryCostFunction.h.

CostFunc class_margin (  bool  binary_target_is_01 = false, bool  output_is_positive = false )  [inline]

difference between correct class score and max of other class' scores Definition at line 96 of file ClassMarginCostFunction.h.

template<class T> void classification_confusion_matrix (  TMat< T >  outputs, TMat< T >  target_classes, TMat< T >  confusion_matrix )

Definition at line 5618 of file TMat_maths_impl.h. References argmax(), and PLearn::TMat< T >::length().

Var classification_loss (  Var  network_output, Var  classnum )  [inline]

Definition at line 79 of file ClassificationLossVariable.h. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), and PLearn::NeighborhoodSmoothnessNNet::build_().

template<class T> void clear (  const TMat< T > &  x  )

Definition at line 609 of file TMat_impl.h. References x.

void clear_1 (  bool &  x  )  [inline]

Definition at line 189 of file general.h. References x.

void clear_1 (  double &  x  )  [inline]

Definition at line 188 of file general.h. References x.

void clear_1 (  float &  x  )  [inline]

Definition at line 187 of file general.h. References x.

void clear_1 (  unsigned long &  x  )  [inline]

Definition at line 186 of file general.h. References x.

void clear_1 (  long &  x  )  [inline]

Definition at line 185 of file general.h. References x.

void clear_1 (  unsigned int &  x  )  [inline]

Definition at line 184 of file general.h. References x.

void clear_1 (  int &  x  )  [inline]

Definition at line 183 of file general.h. References x.

void clear_1 (  unsigned short &  x  )  [inline]

Definition at line 182 of file general.h. References x.

void clear_1 (  short &  x  )  [inline]

Definition at line 181 of file general.h. References x.

void clear_1 (  signed char &  x  )  [inline]

Definition at line 180 of file general.h. References x.

void clear_1 (  unsigned char &  x  )  [inline]

Definition at line 179 of file general.h. References x.

void clear_1 (  char &  x  )  [inline]

Definition at line 178 of file general.h. References x.

template<class T> void clear_1 (  T &  x  )  [inline]

clearing an element (that's called by clear_n...) Default implementation for clearing any type (will work for objects, but not for base types like int, because the default "constructor" for int leaves it uninitialised... Hence the specialisations below Definition at line 175 of file general.h. References x. Referenced by clear_n().

void clear_n (  unsigned long *  begin, int  n )  [inline]

Definition at line 233 of file general.h.

void clear_n (  long *  begin, int  n )  [inline]

Definition at line 230 of file general.h.

void clear_n (  unsigned int *  begin, int  n )  [inline]

Definition at line 227 of file general.h.

void clear_n (  int *  begin, int  n )  [inline]

Definition at line 224 of file general.h.

void clear_n (  unsigned short *  begin, int  n )  [inline]

Definition at line 221 of file general.h.

void clear_n (  short *  begin, int  n )  [inline]

Definition at line 218 of file general.h.

void clear_n (  unsigned char *  begin, int  n )  [inline]

Definition at line 215 of file general.h.

void clear_n (  char *  begin, int  n )  [inline]

Definition at line 212 of file general.h.

void clear_n (  bool *  begin, int  n )  [inline]

Definition at line 209 of file general.h.

void clear_n (  double *  begin, int  n )  [inline]

Definition at line 206 of file general.h.

void clear_n (  float *  begin, int  n )  [inline]

efficient specialisation for built-in types Definition at line 203 of file general.h.

template<class For> void clear_n (  For  begin, int  n )  [inline]

clears n elements starting at iterator position begin Definition at line 193 of file general.h. References clear_1(). Referenced by PLearn::TVec< pair< real, real > >::clear(), PLearn::TMat< pair< real, real > >::clear(), PLearn::Storage< pair< real, real > >::mem_alloc(), and PLearn::Storage< pair< real, real > >::resize().

Vec closestPointOnHyperplane (  const Vec &  x, const Mat &  points, real  weight_decay = 0. )  [inline]

closest point to x on hyperplane that passes through all points (with weight decay) Definition at line 615 of file plapack.h. References constrainedLinearRegression(), transposeProduct(), and x. Referenced by hyperplaneDistance().

real color (  int  colornum, real  lightness )

Definition at line 559 of file Grapher.cc. References PLERROR, and rgb2real(). Referenced by displayPoints(), PLearn::RGBImage::fill(), PLearn::Grapher::plot_2D_classification(), and PLearn::GraphicalBiText::printNode().

template<class T> void columnArgmax (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4252 of file TMat_maths_impl.h. References argmax(), PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void columnArgmin (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4267 of file TMat_maths_impl.h. References argmin(), PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> TMat<T> columnmatrix (  const TVec< T > &  v  )  [inline]

returns a view of this vector as a single column matrix Definition at line 739 of file TMat_impl.h. References PLearn::TVec< T >::length(), and PLearn::TVec< T >::toMat(). Referenced by PLearn::GaussMix::computeMeansAndCovariances(), PLearn::LocallyWeightedDistribution::log_density(), PLearn::GaussMix::updateFromConditionalSorting(), and PLearn::GaussMix::updateSampleWeights().

template<class T> void columnMax (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4230 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), max(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::MatrixSoftmaxVariable::fprop().

template<class T> void columnMean (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4170 of file TMat_maths_impl.h. References columnSum(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by computeLocalPrincipalComponents(), computeMean(), computeMeanAndCovar(), computeMeanAndStddev(), computeMeanAndVariance(), PLearn::StackedLearner::computeOutput(), PLearn::MovingAverage::test(), PLearn::MovingAverage::train(), and PLearn::GaussianProcessRegressor::train().

template<class T> void columnMin (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4241 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), min(), PLERROR, and PLearn::TMat< T >::width().

Var columnSum (  Var  v  )  [inline]

Definition at line 73 of file ColumnSumVariable.h.

template<class T> void columnSum (  const TMat< T > &  mat, TVec< T > &  result )

all the operations below result in a row vector and are obtained by iterating (e.g. summing) over the row index, e.g. yielding the sum of each column in the result. Definition at line 4146 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by columnMean(), PLearn::GaussMix::computeMeansAndCovariances(), PLearn::TimesRowVariable::symbolicBprop(), PLearn::PlusRowVariable::symbolicBprop(), PLearn::MinusTransposedColumnVariable::symbolicBprop(), PLearn::MinusRowVariable::symbolicBprop(), and PLearn::DuplicateRowVariable::symbolicBprop().

template<class T> void columnSumOfSquares (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4159 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TVec< T >::length(), PLERROR, sum_of_squares(), and PLearn::TMat< T >::width().

template<class T> void columnVariance (  const TMat< T > &  mat, TVec< T > &  result, const TVec< T > &  columnmean )

Definition at line 4199 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, variance(), and PLearn::TMat< T >::width(). Referenced by computeMeanAndStddev(), and computeMeanAndVariance().

template<class T> void columnWeightedMean (  const TMat< T > &  mat, TVec< T > &  result )

Definition at line 4181 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, PLearn::TMat< T >::toVecCopy(), weighted_mean(), and PLearn::TMat< T >::width().

template<class T> void columnWeightedVariance (  const TMat< T > &  mat, TVec< T > &  result, const TVec< T > &  column_weighted_mean )

Definition at line 4210 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), mean(), PLERROR, PLearn::TMat< T >::toVecCopy(), weighted_variance(), and PLearn::TMat< T >::width().

void PLearn::compactRepresentation (  char *  t  )

gives a (intermediate) code for a numeric string (starting with #) Definition at line 202 of file TypesNumeriques.cc. References compactRepresentationRangesAndOrdinals(), compactRepresentationShrinkNum(), and compactRepresentationTranslate(). Referenced by numericType().

void compactRepresentationRangesAndOrdinals (  char *  t  )

Definition at line 178 of file TypesNumeriques.cc. Referenced by compactRepresentation().

void compactRepresentationShrinkNum (  char *  t  )

Definition at line 148 of file TypesNumeriques.cc. Referenced by compactRepresentation().

void compactRepresentationTranslate (  char *  t  )

Definition at line 119 of file TypesNumeriques.cc. References ALPHAsymbols, DIGITsymbols, elementOf(), ORDINALS, and stringPos(). Referenced by compactRepresentation().

int compare_string_pointers (  const void *  ts1, const void *  ts2 )  [static]

Definition at line 1050 of file MatIO.cc. Referenced by loadSTATLOG(), and loadUCIMLDB().

template<class T> void complement_indices (  TVec< T > &  indices, int  n, TVec< T > &  complement_indices, TVec< T > &  buffer )

Definition at line 1676 of file TMat_maths_impl.h. References complement_indices(), PLearn::TVec< T >::data(), PLearn::TVec< T >::fill(), PLearn::TVec< T >::length(), and PLearn::TVec< T >::resize(). Referenced by complement_indices().

void PLearn::compress_vec (  char *  comprbuf, const double *  data, int  l, bool  double_stored_as_float = false )

Definition at line 535 of file pl_io.cc. References val, and write_compr_mode_and_size_ptr().

template<class T> void compressedTransposeProductAcc (  const TVec< T > &  result, const TMat< T > &  m, char *  comprbufvec )

Definition at line 2366 of file TMat_maths_impl.h. References endl(), PLearn::TMat< T >::length(), mode, PLERROR, and read_compr_mode_and_size_ptr().

Mat PLearn::compute2dGridOutputs (  Learner &  learner, real  min_x = -1, real  max_x = +1, real  min_y = -1, real  max_y = +1, int  length = 200, int  width = 200, real  singleoutput_threshold = 0. )

This will return a length*width matrix containing the computed outputs for a learner which has 2 dimensional input, where the inputs are taken on a regular grid ranging [min_x,max_x]x[min_y,max_y]. The mapping to the matrix m is m(i,j) = f(min_x+i*(max_x-min_x)/(length-1), min_y+j*(max_y-min_y)/(width-1)) If the output is of length 1: (class depends on which side of the threshold we are) the result put in m is output[0] - singleoutput_threshold If the output is of length 2: (score for each class) the result put in m is output[0]-output[1] Definition at line 622 of file DisplayUtils.cc. References PLearn::Learner::inputsize(), PLearn::Learner::outputsize(), PLERROR, and PLearn::Learner::use(). Referenced by displayDecisionSurface().

template<class T> void compute_fastsigmoid (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 1000 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), fastsigmoid(), PLearn::TVec< T >::length(), and PLERROR. Referenced by fastsigmoid().

template<class T> void compute_fasttanh (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 963 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), fasttanh(), PLearn::TVec< T >::length(), and PLERROR. Referenced by fasttanh().

template<class T> void compute_inverse_sigmoid (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 1018 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), inverse_sigmoid(), PLearn::TVec< T >::length(), and PLERROR. Referenced by inverse_sigmoid().

Mat PLearn::compute_learner_outputs (  PP< PLearner >  learner, VMat  dataset )

Definition at line 48 of file learner_utils.cc. References PLearn::VMat::length().

Mat PLearn::compute_learner_outputs_on_grid (  PP< PLearner >  learner, int  nx, int  ny, real  x0, real  y0, real  deltax, real  deltay )

Returns a nx*ny x learner->outputsize() matrix of outputs corresponding to the nx*ny grid points. Definition at line 88 of file learner_utils.cc. References PLERROR, tostring(), PLearn::ProgressBar::update(), and x.

template<class T> void compute_log (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 862 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), log(), and PLERROR. Referenced by log().

template<class T> void compute_safelog (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 898 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and safelog(). Referenced by safelog().

template<class T> void compute_sigmoid (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 981 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and sigmoid(). Referenced by sigmoid().

template<class T> void compute_sqrt (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 880 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and sqrt(). Referenced by sqrt().

template<class T> void compute_tanh (  const TVec< T > &  src, const TVec< T > &  dest )

Definition at line 925 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and tanh(). Referenced by PLearn::EntropyContrast::computeNNcontinuous_hidden(), and tanh().

Mat PLearn::computeBasicStats (  VMat  m  )

The returned Mat is structured as follows: row 0: mean row 1: stddev row 2: min row 3: max row 4: nmissing row 5: nzero (==0) row 6: npositive (>0) row 7: nnegative (<0) row 8: mean of positive row 9: stddev of positive Definition at line 128 of file VMat_maths.cc. References PLearn::TVec< T >::data(), PLearn::TVec< T >::fill(), is_missing(), PLearn::TVec< T >::length(), PLearn::VMat::length(), MAX_ROW, MEAN_ROW, MEANPOS_ROW, MIN_ROW, NMISSING_ROW, NNEGATIVE_ROW, NPOSITIVE_ROW, NZERO_ROW, sqrt(), square(), STDDEV_ROW, STDDEVPOS_ROW, val, and PLearn::VMat::width(). Referenced by computeConditionalMeans().

template<class T> void computeColumnsMeanAndStddev (  const TMat< T > &  m, TMat< T > &  meanvec, TMat< T > &  stddevvec )

compute the mean and standard deviations of the colums of m (looping over s) (the result is stored in column vectors meanvec and stddevvec) Definition at line 4345 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), rowMean(), rowVariance(), and sqrt().

Array< Mat > PLearn::computeConditionalMeans (  VMat  trainset, int  targetsize, Mat &  basic_stats )

Computes conditional mean and variance of each target, conditoned on the values of categorical integer input feature. The basic_stats matrix may be passed if previously computed (see computeBasicStats) or an empty matrix may be passed otherwise, that will compute the basic statistics. An input feature #i is considered a categorical integer input if its min and max (as found in basic_stats) are integers and are not too far apart. For these, the correponding returned array[i] matrix will contain max-min+1 rows (one for each integer value between min and max inclusive), each row containing the corresponding input value, the number of times it occured, and mean and variance for each target. The returned matrix array[i] for input features that are not considered categorical integers are empty. Definition at line 258 of file VMat_maths.cc. References computeBasicStats(), is_integer(), PLearn::TMat< T >::isNotEmpty(), k, PLearn::TMat< T >::length(), PLearn::VMat::length(), PLearn::TVec< T >::length(), Mat, MAX_ROW, mean(), MIN_ROW, sqrt(), square(), PLearn::TVec< T >::subVec(), sum(), sumsquare(), variance(), and PLearn::VMat::width().

PP< ConditionalStatsCollector > PLearn::computeConditionalStats (  VMat  m, int  condfield, TVec< RealMapping >  ranges )

returns the cooccurence statistics conditioned on the given field Definition at line 235 of file VMat_maths.cc. References endl(), PLearn::VMat::length(), and PLearn::VMat::width().

template<class T> void computeCovar (  const TMat< T > &  m, const TVec< T > &  meanvec, TMat< T > &  covarmat )

Definition at line 4302 of file TMat_maths_impl.h. References externalProductScaleAcc(), PLearn::TMat< T >::length(), PLearn::TMat< T >::resize(), transposeProduct(), and PLearn::TMat< T >::width().

void PLearn::computeInputMean (  VMat  d, Vec &  meanvec )

Computes the (possibly weighted) mean and covariance of the input part of the dataset. This will only call d->getExamplev Definition at line 358 of file VMat_maths.cc. References PLearn::TVec< T >::clear(), PLearn::VMat::getExample(), PLearn::VMat::length(), multiplyAcc(), and PLearn::TVec< T >::resize().

void PLearn::computeInputMeanAndCovar (  VMat  d, Vec &  meanvec, Mat &  covarmat )

Definition at line 377 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), PLearn::TVec< T >::clear(), externalProductScaleAcc(), PLearn::VMat::getExample(), PLearn::VMat::length(), multiplyAcc(), PLearn::TMat< T >::resize(), and PLearn::TVec< T >::resize(). Referenced by PLearn::GaussMix::computeMeansAndCovariances(), and PLearn::PCA::train().

void PLearn::computeInputMeanAndVariance (  VMat  d, Vec &  meanvec, Vec &  var )

Definition at line 406 of file VMat_maths.cc. References PLearn::TVec< T >::clear(), PLearn::VMat::getExample(), PLearn::VMat::length(), multiplyAcc(), PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), and var(). Referenced by PLearn::GaussMix::computeMeansAndCovariances().

void computeLocalPrincipalComponents (  Mat &  dataset, int  which_pattern, Mat &  delta_neighbors, Vec &  eig_values, Mat &  eig_vectors, Vec &  mean )

Definition at line 139 of file ManifoldParzen2.cc. References center(), columnMean(), computeNearestNeighbors(), computePrincipalComponents(), mean(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ManifoldParzen2::train().

void PLearn::computeMean (  VMat  d, Vec &  meanvec )

Definition at line 115 of file VMat_maths.cc. References PLearn::TVec< T >::clear(), PLearn::VMat::length(), PLearn::TVec< T >::resize(), and PLearn::VMat::width().

template<class T> void computeMean (  const TMat< T > &  m, TVec< T > &  meanvec )  [inline]

compute the mean of the rows of m (looping over columns) Definition at line 4291 of file TMat_maths_impl.h. References columnMean(). Referenced by PLearn::CenteredVMatrix::build_(), computeMeanAndVariance(), and PLearn::EmpiricalDistribution::expectation().

void PLearn::computeMeanAndCovar (  VMat  d, Vec &  meanvec, Mat &  covarmat, ostream &  logstream = cerr )

computes empirical mean and covariance in a single pass < Parallel implementation < default sequential implementation Definition at line 518 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), PLearn::TVec< T >::clear(), computeMeanAndCovar(), PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), externalProductAcc(), externalProductScaleAcc(), PLearn::TMat< T >::isCompact(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::VMat::length(), PLERROR, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::size(), PLearn::VMat::toMat(), PLearn::TMat< T >::width(), and PLearn::VMat::width().

template<class T> void computeMeanAndCovar (  const TMat< T > &  m, TVec< T > &  meanvec, TMat< T > &  covarmat )

Definition at line 4312 of file TMat_maths_impl.h. References columnMean(), externalProductScaleAcc(), PLearn::TMat< T >::length(), PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), transposeProduct(), and PLearn::TMat< T >::width(). Referenced by affineNormalization(), computeMeanAndCovar(), logPFittedGaussian(), PLearn::GaussianDistribution::train(), PLearn::ConditionalGaussianDistribution::train(), and PLearn::EmpiricalDistribution::variance().

void PLearn::computeMeanAndStddev (  VMat  d, Vec &  meanvec, Vec &  stddevvec )

Definition at line 604 of file VMat_maths.cc. References computeMeanAndVariance(), PLearn::TVec< T >::length(), and sqrt().

template<class T> void computeMeanAndStddev (  const TMat< T > &  m, TVec< T > &  meanvec, TVec< T > &  stddevvec )

compute the mean and standard deviations of the rows of m (looping over columns) Definition at line 4333 of file TMat_maths_impl.h. References columnMean(), columnVariance(), PLearn::TVec< T >::length(), and sqrt(). Referenced by PLearn::ShiftAndRescaleVMatrix::build_(), loadClassificationDataset(), normalize(), normalizeDataSet(), normalizeDataSets(), and PLearn::TestDependenciesCommand::run().

void PLearn::computeMeanAndVariance (  VMat  d, Vec &  meanvec, Vec &  variancevec )

Definition at line 336 of file VMat_maths.cc. References PLearn::TVec< T >::clear(), computeMean(), PLearn::VMat::length(), multiply(), PLearn::TVec< T >::resize(), substract(), and PLearn::VMat::width().

template<class T> void computeMeanAndVariance (  const TMat< T > &  m, TVec< T > &  meanvec, TVec< T > &  variancevec )

compute the mean and variance of the rows of m (looping over columns) Definition at line 4295 of file TMat_maths_impl.h. References columnMean(), and columnVariance(). Referenced by computeMeanAndStddev(), and PLearn::PCA::train().

void computeNearestNeighbors (  Mat  dataset, Vec  x, Mat &  neighbors, int  ignore_row = -1 )

Definition at line 96 of file ManifoldParzen2.cc. References PLearn::TVec< T >::first(), PLearn::BottomNI< T >::getBottomN(), k, PLearn::TMat< T >::length(), PLearn::BottomNI< T >::nZeros(), PLERROR, powdistance(), PLearn::BottomNI< T >::sort(), PLearn::BottomNI< T >::update(), and x.

void PLearn::computeNearestNeighbors (  VMat  dataset, Vec  x, TVec< int > &  neighbors, int  ignore_row = -1 )

Definition at line 1282 of file VMat_maths.cc. References PLearn::TVec< T >::first(), PLearn::BottomNI< T >::getBottomN(), k, PLearn::VMat::length(), PLearn::TVec< T >::length(), PLearn::BottomNI< T >::nZeros(), PLERROR, powdistance(), PLearn::BottomNI< T >::sort(), PLearn::BottomNI< T >::update(), PLearn::VMat::width(), and x. Referenced by PLearn::LocalNeighborsDifferencesVMatrix::build_(), computeLocalPrincipalComponents(), PLearn::GaussianContinuum::get_image_matrix(), and PLearn::GaussianContinuum::train().

TVec< Mat > PLearn::computeOutputFields (  PP< PLearner >  learner, int  nx, int  ny, real  x0, real  y0, real  deltax, real  deltay )

Definition at line 184 of file GenerateDecisionPlot.cc. References k, PLearn::TVec< T >::resize(), tostring(), PLearn::ProgressBar::update(), and x.

TVec< Mat > PLearn::computeOutputFields (  PP< PLearner >  learner, Vec  X, Vec  Y )

Definition at line 151 of file GenerateDecisionPlot.cc. References k, PLearn::TVec< T >::length(), PLearn::TVec< T >::resize(), tostring(), and PLearn::ProgressBar::update(). Referenced by computeOutputFieldsAutoRange().

TVec< Mat > PLearn::computeOutputFieldsAutoRange (  PP< PLearner >  learner, VMat  dataset, int  nx, int  ny, real &  x0, real &  y0, real &  deltax, real &  deltay, real  extraspace = .10 )

Definition at line 217 of file GenerateDecisionPlot.cc. References computeOutputFields(), computeRange(), and PLearn::VMat::subMatColumns().

void computePrincipalComponents (  Mat  dataset, Vec &  eig_values, Mat &  eig_vectors )

Definition at line 120 of file ManifoldParzen2.cc. References eigenVecOfSymmMat(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, PLearn::TMat< T >::resize(), transposeProduct(), and PLearn::TMat< T >::width(). Referenced by computeLocalPrincipalComponents().

void PLearn::computeRange (  VMat  d, Vec &  minvec, Vec &  maxvec )

Definition at line 83 of file VMat_maths.cc. References PLearn::TVec< T >::fill(), PLearn::VMat::length(), max(), min(), PLearn::TVec< T >::resize(), and PLearn::VMat::width(). Referenced by computeOutputFieldsAutoRange(), computeXYPositions(), determine_grid_for_dataset(), and DX_create_grid_outputs_file().

TVec< RealMapping > PLearn::computeRanges (  TVec< StatsCollector >  stats, int  discrete_mincount, int  continuous_mincount )

Definition at line 592 of file StatsCollector.cc. References k, PLearn::TVec< T >::length(), and PLearn::TVec< T >::resize(). Referenced by PLearn::VMatrix::getRanges().

template<class T> void computeRanks (  const TMat< T > &  mat, TMat< T > &  ranks )

For each column of mat, sort the elements and put in the 'ranks' matrix (of the same dimensions) the rank of original elements. More precisely, Let mat(i,j) be the k-th largest element of column j, than ranks(i,j) will be k. Definition at line 177 of file random.h. References PLearn::TMat< T >::length(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), shuffleRows(), sortRows(), and PLearn::TMat< T >::width(). Referenced by SpearmanRankCorrelation().

void PLearn::computeRowMean (  VMat  d, Vec &  meanvec )

Definition at line 104 of file VMat_maths.cc. References PLearn::VMat::length(), mean(), PLearn::TVec< T >::resize(), and PLearn::VMat::width().

TVec< StatsCollector > PLearn::computeStats (  VMat  m, int  maxnvalues, bool  report_progress = true )

Retirns the unconditional statistics of each field. Definition at line 212 of file VMat_maths.cc. References PLearn::VMat::length(), PLearn::ProgressBar::update(), and PLearn::VMat::width().

void PLearn::computeStats (  VMat  m, VecStatsCollector &  st, bool  report_progress = true )

Definition at line 191 of file VMat_maths.cc. References PLearn::VecStatsCollector::finalize(), PLearn::VecStatsCollector::forget(), PLearn::VMat::length(), PLearn::VecStatsCollector::setFieldNames(), PLearn::ProgressBar::update(), PLearn::VecStatsCollector::update(), and PLearn::VMat::width(). Referenced by PLearn::ShiftAndRescaleVMatrix::build_(), and PLearn::VMatrix::getStats().

void PLearn::computeWeightedMean (  Vec  weights, VMat  d, Vec &  meanvec )

Statistics functions *. Definition at line 64 of file VMat_maths.cc. References PLearn::TVec< T >::clear(), PLearn::TVec< T >::length(), PLearn::VMat::length(), PLERROR, PLearn::TVec< T >::resize(), sum(), and PLearn::VMat::width(). Referenced by computeWeightedMeanAndCovar().

real PLearn::computeWeightedMeanAndCovar (  VMat  d, Vec &  meanvec, Mat &  covarmat, real  threshold = 0 )

Last column of d is supposed to contain the weight for each sample Samples with a weight less or equal to threshold will be ignored (returns the sum of all the weights actually used). Definition at line 470 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), PLearn::TVec< T >::clear(), externalProductScaleAcc(), PLearn::VMat::length(), multiplyAcc(), PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), substract(), PLearn::TVec< T >::subVec(), and PLearn::VMat::width().

void PLearn::computeWeightedMeanAndCovar (  Vec  weights, VMat  d, Vec &  meanvec, Mat &  covarmat )

Definition at line 446 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), computeWeightedMean(), externalProductScaleAcc(), PLearn::VMat::length(), PLearn::TMat< T >::resize(), substract(), and PLearn::VMat::width(). Referenced by PLearn::GaussianDistribution::train().

void PLearn::computeXYPositions (  VMat  dataset, int  nx, int  ny, Vec &  X, Vec &  Y, real  extraspace = .10 )

Definition at line 233 of file GenerateDecisionPlot.cc. References computeRange(), PLearn::TVec< T >::data(), PLearn::VMat::length(), PLearn::TVec< T >::resize(), PLearn::VMat::subMatColumns(), and x.

template<class T> TVec< T > PLearn::concat (  const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 202 of file TMat_impl.h. References PLearn::TVec< T >::length().

template<class T> TVec<T> concat (  const Array< TVec< T > > &  varray  )

Definition at line 149 of file Array_impl.h. References PLearn::TVec< T >::data(), k, and PLearn::TVec< T >::length(). Referenced by PLearn::Learner::computeTestStatistics(), local_neighbors_differences(), PLearn::SequentialModelSelector::matlabSave(), removeElement(), and PLearn::Learner::test().

Var concatOf (  Var  output, const VarArray &  inputs, VMat  distr, int  nsamples, VarArray  parameters = VarArray() )  [inline]

deprecated old version, do not use! Definition at line 92 of file ConcatOfVariable.h. References concatOf().

Var concatOf (  VMat  distr, Func  f )  [inline]

concatOf Definition at line 88 of file ConcatOfVariable.h. Referenced by concatOf().

CostFunc condprob_cost (  bool  normalize = false, bool  smooth_map_outputs = false )  [inline]

negative log conditional probability Definition at line 103 of file NegLogProbCostFunction.h. References normalize(). Referenced by PLearn::ClassifierFromDensity::computeCostsFromOutputs().

Vec PLearn::constrainedLinearRegression (  const Mat &  Xt, const Vec &  Y, real  lambda = 0. )

Returns w that minimizes ||X.w - Y||^2 + lambda.||w||^2 under constraint w_i = 1 Xt is the transposed of the input matrix X; Y is the target vector. This doesn't include any bias term. Definition at line 375 of file plapack.cc. References dot(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, solveLinearSystem(), PLearn::TVec< T >::subVec(), and PLearn::TMat< T >::width(). Referenced by closestPointOnHyperplane().

bool PLearn::containsChar (  const char *  s, const char *  symbols )

true if string s contains any one of the characters in symbols. Definition at line 79 of file TypesNumeriques.cc. Referenced by looksNumeric().

Var convolve (  Var  input, Var  mask )  [inline]

Definition at line 74 of file ConvolveVariable.h.

template<class T> void convolve (  TMat< T >  m, TMat< T >  mask, TMat< T >  result )

Definition at line 5581 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, sum(), and PLearn::TMat< T >::width(). Referenced by PLearn::ConvolveVariable::fprop().

template<class In, class Out> Out copy_cast (  In  first, In  last, Out  res )  [inline]

Like std::copy, but with an explicit cast to the destination type. Definition at line 160 of file general.h. Referenced by operator<<().

template<class T> T correlation (  const TVec< T > &  x, const TVec< T > &  y )

Definition at line 3888 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), PLERROR, sqrt(), and x.

template<class T> T correlation (  const TMat< T > &  mat  )

Definition at line 3862 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLERROR, sqrt(), PLearn::TMat< T >::width(), and x. Referenced by PLearn::FieldConvertCommand::FieldConvertCommand(), PLearn::TestDependenciesCommand::TestDependenciesCommand(), and PLearn::TestDependencyCommand::TestDependencyCommand().

void PLearn::correlations (  const VMat &  x, const VMat &  y, Mat &  r, Mat &  pvalues )

Compute the correlations between each of the columns of x and each of the columns of y. The results are in the x.width() by y.width() matrix r. The p-values of the corresponding test (no correlation) are stored in the same-sized matrix pvalues. Definition at line 1228 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), PLearn::TMat< T >::length(), PLearn::VMat::length(), PLERROR, PLWARNING, PLearn::TMat< T >::resize(), sqrt(), testNoCorrelationAsymptotically(), PLearn::TMat< T >::width(), PLearn::VMat::width(), and x. Referenced by PLearn::TestDependencyCommand::run(), and PLearn::TestDependenciesCommand::run().

int PLearn::countNonBlankLinesOfFile (  const string &  filename  )

Will return the number of non-blank lines of file #-style comments are considered blank. Definition at line 471 of file fileutils.cc. References count, and PLERROR. Referenced by PLearn::AsciiVMatrix::build_(), parseSizeFromRemainingLines(), and PLearn::StringTable::StringTable().

map< real, int > PLearn::countOccurencesInColumn (  VMat  m, int  col )

returns a map mapping all different values appearing in column col to their number of occurences Definition at line 704 of file VMat_maths.cc. References is_missing(), PLearn::VMat::length(), PLERROR, and val. Referenced by indicesOfOccurencesInColumn().

template<class T> T covariance (  const TVec< T > &  vec1, const TVec< T > &  vec2, T  mean1, T  mean2 )

Definition at line 425 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::VecStatsCollector::getCovariance().

void PLearn::cp (  const string &  srcpath, const string &  destpath )

calls system with cp -R to recursively copy source to destination Definition at line 358 of file fileutils.cc. Referenced by loadSTATLOG(), and loadUCIMLDB().

Var cross_entropy (  Var  network_output, Var  targets )  [inline]

Definition at line 76 of file CrossEntropyVariable.h. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), and PLearn::AddCostToLearner::build_().

void cross_valid (  const string &  modelalias, string  trainalias, int  kval )

Definition at line 279 of file old_plearn_main.cc. References endl(), exitmsg(), getDataSet(), getDatasetAliases(), getModelAliases(), isfile(), PLearn::TMat< T >::length(), PLearn::VMat::length(), loadAscii(), PLWARNING, read(), PLearn::TMat< T >::resize(), save(), PLearn::TVec< T >::size(), split(), tostring(), PLearn::TMat< T >::width(), and PLearn::VMat::width(). Referenced by old_plearn_main().

char * PLearn::cstr (  string &  s  )

Definition at line 2880 of file WordNetOntology.cc. References cstr(). Referenced by cstr(), PLearn::WordNetOntology::extractSenses(), PLearn::WordNetOntology::extractTaggedWordFrequencies(), PLearn::WordNetOntology::getWordSenseIdForSenseKey(), PLearn::WordNetOntology::hasSenseInWordNet(), and stemWord().

Var cutAboveThreshold (  Var  v, real  threshold )  [inline]

Definition at line 76 of file CutAboveThresholdVariable.h. Referenced by negative().

Var cutBelowThreshold (  Var  v, real  threshold )  [inline]

Definition at line 76 of file CutBelowThresholdVariable.h. Referenced by positive().

Var d_hard_slope (  Var  x, Var  left, Var  right )  [inline]

Definition at line 79 of file HardSlopeVariable.h. References ifThenElse(), invertElements(), left(), right(), var(), and x.

Var d_soft_slope (  Var  x, Var  smoothness, Var  left, Var  right )  [inline]

Definition at line 85 of file SoftSlopeVariable.h. References left(), right(), sigmoid(), and x.

real d_soft_slope (  real  x, real  smoothness = 1, real  left = 0, real  right = 1 )  [inline]

Definition at line 368 of file pl_math.h. References left(), right(), sigmoid(), and x. Referenced by PLearn::ConditionalDensityNet::build_().

string * PLearn::data_filename_2_filenames (  const string &  filename, int &  nb_files )

take a filename containing the name of a file per line, and return theses names as a string* of length nb_files Definition at line 187 of file stringutils.cc. References fname, and PLERROR.

float PLearn::date_to_float (  const PDate &  t  )

converts date to float: ex: September 29 1972: 720929; December 25 2002: 1021225 Also converts missing date to missing flat value and vice-versa. Definition at line 162 of file PDate.cc. References PLearn::PDate::day, PLearn::PDate::isMissing(), MISSING_VALUE, PLearn::PDate::month, and PLearn::PDate::year. Referenced by PLearn::VMatLanguage::run(), PLearn::RowIterator::toDouble(), and PLearn::FieldValue::toDouble().

double PLearn::datetime_to_double (  const PDateTime &  t  )

converts date/time to double: for example: September 29 1972: 720929; December 25 2002: 1021225. Hours/minutes/seconds are represented as FRACTIONS of days. Also converts missing date to missing double value and vice-versa. Definition at line 137 of file PDateTime.cc. References PLearn::PDateTime::day, hhmmss_to_double(), PLearn::PDateTime::hour, PLearn::PDateTime::isMissing(), PLearn::PDateTime::min, MISSING_VALUE, PLearn::PDateTime::month, PLearn::PDateTime::sec, and PLearn::PDateTime::year. Referenced by PLearn::PDateTime::operator<().

DECLARE_OBJECT_PP (  VMat  , VMatrix  )

DECLARE_OBJECT_PP (  Var  , Variable  )

DECLARE_OBJECT_PP (  Func  , Function  )

DECLARE_OBJECT_PP (  Ker  , Kernel  )

DECLARE_OBJECT_PTR (  TangentLearner   )

DECLARE_OBJECT_PTR (  SpectralClustering   )

DECLARE_OBJECT_PTR (  PCA   )

DECLARE_OBJECT_PTR (  KPCATangentLearner   )

PLearn::DECLARE_OBJECT_PTR (  Isomap   )

DECLARE_OBJECT_PTR (  GaussianContinuum   )

DECLARE_OBJECT_PTR (  EntropyContrast   )

DECLARE_OBJECT_PTR (  TestMethod   )

DECLARE_OBJECT_PTR (  SequentialValidation   )

Declares a few other classes and functions related to this class.

DECLARE_OBJECT_PTR (  SequentialModelSelector   )

Declares a few other classes and functions related to this class.

DECLARE_OBJECT_PTR (  SequentialLearner   )

Declares a few other classes and functions related to this class.

DECLARE_OBJECT_PTR (  EmbeddedSequentialLearner   )

Declares a few other classes and functions related to this class.

DECLARE_OBJECT_PTR (  PLS   )

DECLARE_OBJECT_PTR (  LinearRegressor   )

DECLARE_OBJECT_PTR (  ConstantRegressor   )

PLearn::DECLARE_OBJECT_PTR (  PTester   )

DECLARE_OBJECT_PTR (  Grapher   )

DECLARE_OBJECT_PTR (  GenerateDecisionPlot   )

DECLARE_OBJECT_PTR (  Experiment   )

DECLARE_OBJECT_PTR (  Dictionary   )

DECLARE_OBJECT_PTR (  TestingLearner   )

DECLARE_OBJECT_PTR (  StatefulLearner   )

DECLARE_OBJECT_PTR (  StackedLearner   )

DECLARE_OBJECT_PTR (  SelectInputSubsetLearner   )

DECLARE_OBJECT_PTR (  NNet   )

DECLARE_OBJECT_PTR (  NeuralNet   )

DECLARE_OBJECT_PTR (  NeighborhoodSmoothnessNNet   )

DECLARE_OBJECT_PTR (  EmbeddedLearner   )

DECLARE_OBJECT_PTR (  AddCostToLearner   )

DECLARE_OBJECT_PTR (  UniformDistribution   )

DECLARE_OBJECT_PTR (  UnconditionalDistribution   )

DECLARE_OBJECT_PTR (  SpiralDistribution   )

DECLARE_OBJECT_PTR (  ManifoldParzen2   )

DECLARE_OBJECT_PTR (  LocallyWeightedDistribution   )

DECLARE_OBJECT_PTR (  HistogramDistribution   )

DECLARE_OBJECT_PTR (  GaussMix   )

DECLARE_OBJECT_PTR (  GaussianProcessRegressor   )

DECLARE_OBJECT_PTR (  GaussianDistribution   )

DECLARE_OBJECT_PTR (  EmpiricalDistribution   )

DECLARE_OBJECT_PTR (  Distribution   )

DECLARE_OBJECT_PTR (  ConditionalGaussianDistribution   )

DECLARE_OBJECT_PTR (  ConditionalDistribution   )

DECLARE_OBJECT_PTR (  ConditionalDensityNet   )

DECLARE_OBJECT_PTR (  MultiInstanceNNet   )

DECLARE_OBJECT_PTR (  ClassifierFromDensity   )

DECLARE_OBJECT_PTR (  AdaBoost   )

DECLARE_OBJECT_PTR (  YMDDatedVMatrix   )

DECLARE_OBJECT_PTR (  VVMatrix   )

PLearn::DECLARE_OBJECT_PTR (  VMatrix   )

DECLARE_OBJECT_PTR (  PreprocessingVMatrix   )

DECLARE_OBJECT_PTR (  VMatLanguage   )

DECLARE_OBJECT_PTR (  VecExtendedVMatrix   )

DECLARE_OBJECT_PTR (  UpsideDownVMatrix   )

DECLARE_OBJECT_PTR (  UniformVMatrix   )

DECLARE_OBJECT_PTR (  UniformizeVMatrix   )

DECLARE_OBJECT_PTR (  TransposeVMatrix   )

DECLARE_OBJECT_PTR (  TrainValidTestSplitter   )

DECLARE_OBJECT_PTR (  TrainTestSplitter   )

PLearn::DECLARE_OBJECT_PTR (  TrainTestBagsSplitter   )

DECLARE_OBJECT_PTR (  ToBagSplitter   )

DECLARE_OBJECT_PTR (  TestInTrainSplitter   )

DECLARE_OBJECT_PTR (  TemporalHorizonVMatrix   )

DECLARE_OBJECT_PTR (  SubVMatrix   )

DECLARE_OBJECT_PTR (  SubInputVMatrix   )

DECLARE_OBJECT_PTR (  StrTableVMatrix   )

DECLARE_OBJECT_PTR (  Splitter   )

DECLARE_OBJECT_PTR (  SparseVMatrix   )

PLearn::DECLARE_OBJECT_PTR (  SourceVMatrix   )

DECLARE_OBJECT_PTR (  SortRowsVMatrix   )

DECLARE_OBJECT_PTR (  ShiftAndRescaleVMatrix   )

DECLARE_OBJECT_PTR (  SequentialSplitter   )

DECLARE_OBJECT_PTR (  SelectRowsVMatrix   )

DECLARE_OBJECT_PTR (  SelectRowsFileIndexVMatrix   )

DECLARE_OBJECT_PTR (  SelectColumnsVMatrix   )

DECLARE_OBJECT_PTR (  RowsSubVMatrix   )

DECLARE_OBJECT_PTR (  RowBufferedVMatrix   )

DECLARE_OBJECT_PTR (  RepeatSplitter   )

DECLARE_OBJECT_PTR (  RemoveRowsVMatrix   )

DECLARE_OBJECT_PTR (  RemoveDuplicateVMatrix   )

DECLARE_OBJECT_PTR (  RemapLastColumnVMatrix   )

DECLARE_OBJECT_PTR (  RegularGridVMatrix   )

DECLARE_OBJECT_PTR (  RangeVMatrix   )

DECLARE_OBJECT_PTR (  ProcessingVMatrix   )

DECLARE_OBJECT_PTR (  PrecomputedVMatrix   )

PLearn::DECLARE_OBJECT_PTR (  PLearnerOutputVMatrix   )

DECLARE_OBJECT_PTR (  PairsVMatrix   )

DECLARE_OBJECT_PTR (  OneHotVMatrix   )

DECLARE_OBJECT_PTR (  MultiInstanceVMatrix   )

PLearn::DECLARE_OBJECT_PTR (  MovingAverageVMatrix   )

Declares a few other classes and functions related to this class.

DECLARE_OBJECT_PTR (  MemoryVMatrix   )

DECLARE_OBJECT_PTR (  LocalNeighborsDifferencesVMatrix   )

PLearn::DECLARE_OBJECT_PTR (  LearnerProcessedVMatrix   )

DECLARE_OBJECT_PTR (  KNNVMatrix   )

DECLARE_OBJECT_PTR (  KFoldSplitter   )

DECLARE_OBJECT_PTR (  JulianizeVMatrix   )

DECLARE_OBJECT_PTR (  JoinVMatrix   )

DECLARE_OBJECT_PTR (  InterleaveVMatrix   )

DECLARE_OBJECT_PTR (  IndexedVMatrix   )

DECLARE_OBJECT_PTR (  GramVMatrix   )

DECLARE_OBJECT_PTR (  GetInputVMatrix   )

DECLARE_OBJECT_PTR (  GeneralizedOneHotVMatrix   )

DECLARE_OBJECT_PTR (  FractionSplitter   )

DECLARE_OBJECT_PTR (  ForwardVMatrix   )

DECLARE_OBJECT_PTR (  FinancePreprocVMatrix   )

DECLARE_OBJECT_PTR (  FilterSplitter   )

DECLARE_OBJECT_PTR (  FilteredVMatrix   )

DECLARE_OBJECT_PTR (  FileVMatrix   )

DECLARE_OBJECT_PTR (  ExtendedVMatrix   )

DECLARE_OBJECT_PTR (  ExplicitSplitter   )

DECLARE_OBJECT_PTR (  DiskVMatrix   )

DECLARE_OBJECT_PTR (  DBSplitter   )

DECLARE_OBJECT_PTR (  DatedVMatrix   )

DECLARE_OBJECT_PTR (  DatedJoinVMatrix   )

DECLARE_OBJECT_PTR (  CumVMatrix   )

DECLARE_OBJECT_PTR (  CrossReferenceVMatrix   )

DECLARE_OBJECT_PTR (  ConcatRowsVMatrix   )

DECLARE_OBJECT_PTR (  ConcatRowsSubVMatrix   )

DECLARE_OBJECT_PTR (  ConcatColumnsVMatrix   )

DECLARE_OBJECT_PTR (  CompressedVMatrix   )

DECLARE_OBJECT_PTR (  CenteredVMatrix   )

DECLARE_OBJECT_PTR (  ByteMemoryVMatrix   )

DECLARE_OBJECT_PTR (  BootstrapVMatrix   )

DECLARE_OBJECT_PTR (  BootstrapSplitter   )

DECLARE_OBJECT_PTR (  BatchVMatrix   )

DECLARE_OBJECT_PTR (  AutoVMatrix   )

DECLARE_OBJECT_PTR (  AsciiVMatrix   )

DECLARE_OBJECT_PTR (  WeightedSumSquareVariable   )

DECLARE_OBJECT_PTR (  VecElementVariable   )

DECLARE_OBJECT_PTR (  VarRowVariable   )

DECLARE_OBJECT_PTR (  VarRowsVariable   )

DECLARE_OBJECT_PTR (  Variable   )

DECLARE_OBJECT_PTR (  VarElementVariable   )

DECLARE_OBJECT_PTR (  VarColumnsVariable   )

DECLARE_OBJECT_PTR (  VarArrayElementVariable   )

DECLARE_OBJECT_PTR (  UnfoldedSumOfVariable   )

DECLARE_OBJECT_PTR (  UnfoldedFuncVariable   )

DECLARE_OBJECT_PTR (  UnequalConstantVariable   )

DECLARE_OBJECT_PTR (  UnaryHardSlopeVariable   )

DECLARE_OBJECT_PTR (  TransposeProductVariable   )

DECLARE_OBJECT_PTR (  TimesVariable   )

DECLARE_OBJECT_PTR (  TimesScalarVariable   )

DECLARE_OBJECT_PTR (  TimesRowVariable   )

DECLARE_OBJECT_PTR (  TimesConstantVariable   )

DECLARE_OBJECT_PTR (  TimesColumnVariable   )

DECLARE_OBJECT_PTR (  TanhVariable   )

DECLARE_OBJECT_PTR (  SumVariable   )

DECLARE_OBJECT_PTR (  SumSquareVariable   )

DECLARE_OBJECT_PTR (  SumOverBagsVariable   )

DECLARE_OBJECT_PTR (  SumOfVariable   )

DECLARE_OBJECT_PTR (  SumAbsVariable   )

DECLARE_OBJECT_PTR (  SubsampleVariable   )

DECLARE_OBJECT_PTR (  SubMatVariable   )

DECLARE_OBJECT_PTR (  SubMatTransposeVariable   )

DECLARE_OBJECT_PTR (  SquareVariable   )

DECLARE_OBJECT_PTR (  SquareRootVariable   )

DECLARE_OBJECT_PTR (  SourceVariable   )

DECLARE_OBJECT_PTR (  SoftSlopeVariable   )

DECLARE_OBJECT_PTR (  SoftSlopeIntegralVariable   )

DECLARE_OBJECT_PTR (  SoftplusVariable   )

DECLARE_OBJECT_PTR (  SoftmaxVariable   )

DECLARE_OBJECT_PTR (  SoftmaxLossVariable   )

DECLARE_OBJECT_PTR (  SignVariable   )

DECLARE_OBJECT_PTR (  SigmoidVariable   )

DECLARE_OBJECT_PTR (  SemiSupervisedProbClassCostVariable   )

DECLARE_OBJECT_PTR (  RowSumVariable   )

DECLARE_OBJECT_PTR (  RowAtPositionVariable   )

DECLARE_OBJECT_PTR (  RightPseudoInverseVariable   )

DECLARE_OBJECT_PTR (  ReshapeVariable   )

DECLARE_OBJECT_PTR (  ProjectionErrorVariable   )

DECLARE_OBJECT_PTR (  ProductVariable   )

DECLARE_OBJECT_PTR (  ProductTransposeVariable   )

PLearn::DECLARE_OBJECT_PTR (  PowVariable   )

DECLARE_OBJECT_PTR (  PlusVariable   )

DECLARE_OBJECT_PTR (  PlusScalarVariable   )

DECLARE_OBJECT_PTR (  PlusRowVariable   )

DECLARE_OBJECT_PTR (  PlusConstantVariable   )

DECLARE_OBJECT_PTR (  PlusColumnVariable   )

DECLARE_OBJECT_PTR (  PLogPVariable   )

PLearn::DECLARE_OBJECT_PTR (  PDistributionVariable   )

DECLARE_OBJECT_PTR (  OneHotVariable   )

DECLARE_OBJECT_PTR (  OneHotSquaredLoss   )

DECLARE_OBJECT_PTR (  NllSemisphericalGaussianVariable   )

DECLARE_OBJECT_PTR (  NegCrossEntropySigmoidVariable   )

DECLARE_OBJECT_PTR (  NegateElementsVariable   )

DECLARE_OBJECT_PTR (  MulticlassLossVariable   )

DECLARE_OBJECT_PTR (  MinVariable   )

DECLARE_OBJECT_PTR (  MinusVariable   )

DECLARE_OBJECT_PTR (  MinusTransposedColumnVariable   )

DECLARE_OBJECT_PTR (  MinusRowVariable   )

DECLARE_OBJECT_PTR (  MinusColumnVariable   )

DECLARE_OBJECT_PTR (  MiniBatchClassificationLossVariable   )

DECLARE_OBJECT_PTR (  MaxVariable   )

DECLARE_OBJECT_PTR (  Max2Variable   )

DECLARE_OBJECT_PTR (  MatRowVariable   )

DECLARE_OBJECT_PTR (  MatrixSumOfVariable   )

DECLARE_OBJECT_PTR (  MatrixSoftmaxVariable   )

DECLARE_OBJECT_PTR (  MatrixSoftmaxLossVariable   )

DECLARE_OBJECT_PTR (  MatrixOneHotSquaredLoss   )

DECLARE_OBJECT_PTR (  MatrixInverseVariable   )

DECLARE_OBJECT_PTR (  MatrixElementsVariable   )

DECLARE_OBJECT_PTR (  MatrixAffineTransformVariable   )

DECLARE_OBJECT_PTR (  MatrixAffineTransformFeedbackVariable   )

DECLARE_OBJECT_PTR (  MarginPerceptronCostVariable   )

DECLARE_OBJECT_PTR (  LogVariable   )

DECLARE_OBJECT_PTR (  LogSoftmaxVariable   )

DECLARE_OBJECT_PTR (  LogAddVariable   )

DECLARE_OBJECT_PTR (  LiftOutputVariable   )

DECLARE_OBJECT_PTR (  LeftPseudoInverseVariable   )

DECLARE_OBJECT_PTR (  IsSmallerVariable   )

DECLARE_OBJECT_PTR (  IsMissingVariable   )

DECLARE_OBJECT_PTR (  IsLargerVariable   )

DECLARE_OBJECT_PTR (  IsAboveThresholdVariable   )

DECLARE_OBJECT_PTR (  InvertElementsVariable   )

DECLARE_OBJECT_PTR (  InterValuesVariable   )

DECLARE_OBJECT_PTR (  IndexAtPositionVariable   )

DECLARE_OBJECT_PTR (  IfThenElseVariable   )

DECLARE_OBJECT_PTR (  HardSlopeVariable   )

DECLARE_OBJECT_PTR (  Function   )

DECLARE_OBJECT_PTR (  ExtendedVariable   )

DECLARE_OBJECT_PTR (  ExpVariable   )

DECLARE_OBJECT_PTR (  ErfVariable   )

DECLARE_OBJECT_PTR (  EqualVariable   )

DECLARE_OBJECT_PTR (  EqualScalarVariable   )

DECLARE_OBJECT_PTR (  EqualConstantVariable   )

DECLARE_OBJECT_PTR (  ElementAtPositionVariable   )

DECLARE_OBJECT_PTR (  DuplicateScalarVariable   )

DECLARE_OBJECT_PTR (  DuplicateRowVariable   )

DECLARE_OBJECT_PTR (  DuplicateColumnVariable   )

DECLARE_OBJECT_PTR (  DotProductVariable   )

DECLARE_OBJECT_PTR (  DivVariable   )

DECLARE_OBJECT_PTR (  DilogarithmVariable   )

DECLARE_OBJECT_PTR (  DiagonalizedFactorsProductVariable   )

DECLARE_OBJECT_PTR (  DeterminantVariable   )

DECLARE_OBJECT_PTR (  CutBelowThresholdVariable   )

DECLARE_OBJECT_PTR (  CutAboveThresholdVariable   )

DECLARE_OBJECT_PTR (  CrossEntropyVariable   )

DECLARE_OBJECT_PTR (  ConvolveVariable   )

DECLARE_OBJECT_PTR (  ConcatRowsVariable   )

DECLARE_OBJECT_PTR (  ConcatOfVariable   )

DECLARE_OBJECT_PTR (  ConcatColumnsVariable   )

DECLARE_OBJECT_PTR (  ColumnIndexVariable   )

DECLARE_OBJECT_PTR (  ClassificationLossVariable   )

DECLARE_OBJECT_PTR (  BinaryClassificationLossVariable   )

DECLARE_OBJECT_PTR (  ArgminVariable   )

DECLARE_OBJECT_PTR (  ArgmaxVariable   )

DECLARE_OBJECT_PTR (  AffineTransformWeightPenalty   )

DECLARE_OBJECT_PTR (  AffineTransformVariable   )

DECLARE_OBJECT_PTR (  AbsVariable   )

DECLARE_OBJECT_PTR (  Optimizer   )

DECLARE_OBJECT_PTR (  HTryCombinations   )

DECLARE_OBJECT_PTR (  HCoordinateDescent   )

DECLARE_OBJECT_PTR (  HTryAll   )

DECLARE_OBJECT_PTR (  HSetVal   )

DECLARE_OBJECT_PTR (  HyperOptimizer   )

DECLARE_OBJECT_PTR (  GradientOptimizer   )

DECLARE_OBJECT_PTR (  ShellScript   )

DECLARE_OBJECT_PTR (  RunObject   )

DECLARE_OBJECT_PTR (  ObjectGenerator   )

DECLARE_OBJECT_PTR (  NearestNeighborPredictionCost   )

DECLARE_OBJECT_PTR (  VecStatsCollector   )

DECLARE_OBJECT_PTR (  QuantilesStatsIterator   )

DECLARE_OBJECT_PTR (  LiftStatsIterator   )

DECLARE_OBJECT_PTR (  MaxStatsIterator   )

DECLARE_OBJECT_PTR (  MinStatsIterator   )

DECLARE_OBJECT_PTR (  SharpeRatioStatsIterator   )

DECLARE_OBJECT_PTR (  StderrStatsIterator   )

DECLARE_OBJECT_PTR (  StddevStatsIterator   )

DECLARE_OBJECT_PTR (  ExpMeanStatsIterator   )

DECLARE_OBJECT_PTR (  MeanStatsIterator   )

DECLARE_OBJECT_PTR (  StatsIterator   )

DECLARE_OBJECT_PTR (  StatsCollector   )

DECLARE_OBJECT_PTR (  Smoother   )

DECLARE_OBJECT_PTR (  ScaledConditionalCDFSmoother   )

DECLARE_OBJECT_PTR (  ManualBinner   )

DECLARE_OBJECT_PTR (  LimitedGaussianSmoother   )

DECLARE_OBJECT_PTR (  LiftStatsCollector   )

DECLARE_OBJECT_PTR (  ConditionalStatsCollector   )

DECLARE_OBJECT_PTR (  ConditionalCDFSmoother   )

DECLARE_OBJECT_PTR (  Binner   )

DECLARE_OBJECT_PTR (  WeightedCostFunction   )

DECLARE_OBJECT_PTR (  SquaredErrorCostFunction   )

DECLARE_OBJECT_PTR (  SourceKernel   )

DECLARE_OBJECT_PTR (  SigmoidPrimitiveKernel   )

DECLARE_OBJECT_PTR (  SigmoidalKernel   )

DECLARE_OBJECT_PTR (  SelectedOutputCostFunction   )

DECLARE_OBJECT_PTR (  ScaledLaplacianKernel   )

DECLARE_OBJECT_PTR (  ScaledGeneralizedDistanceRBFKernel   )

DECLARE_OBJECT_PTR (  ScaledGaussianKernel   )

DECLARE_OBJECT_PTR (  ReconstructionWeightsKernel   )

DECLARE_OBJECT_PTR (  QuadraticUtilityCostFunction   )

DECLARE_OBJECT_PTR (  PricingTransactionPairProfitFunction   )

DECLARE_OBJECT_PTR (  PrecomputedKernel   )

DECLARE_OBJECT_PTR (  PowDistanceKernel   )

DECLARE_OBJECT_PTR (  PolynomialKernel   )

DECLARE_OBJECT_PTR (  NormalizedDotProductKernel   )

DECLARE_OBJECT_PTR (  NegOutputCostFunction   )

DECLARE_OBJECT_PTR (  NegLogProbCostFunction   )

DECLARE_OBJECT_PTR (  NegKernel   )

DECLARE_OBJECT_PTR (  MulticlassErrorCostFunction   )

DECLARE_OBJECT_PTR (  LogOfGaussianDensityKernel   )

PLearn::DECLARE_OBJECT_PTR (  LLEKernel   )

DECLARE_OBJECT_PTR (  LiftBinaryCostFunction   )

DECLARE_OBJECT_PTR (  LaplacianKernel   )

PLearn::DECLARE_OBJECT_PTR (  Kernel   )

DECLARE_OBJECT_PTR (  GeodesicDistanceKernel   )

DECLARE_OBJECT_PTR (  GeneralizedDistanceRBFKernel   )

DECLARE_OBJECT_PTR (  GaussianKernel   )

DECLARE_OBJECT_PTR (  GaussianDensityKernel   )

DECLARE_OBJECT_PTR (  DotProductKernel   )

DECLARE_OBJECT_PTR (  DivisiveNormalizationKernel   )

DECLARE_OBJECT_PTR (  DistanceKernel   )

DECLARE_OBJECT_PTR (  DifferenceKernel   )

DECLARE_OBJECT_PTR (  ConvexBasisKernel   )

DECLARE_OBJECT_PTR (  CompactVMatrixPolynomialKernel   )

PLearn::DECLARE_OBJECT_PTR (  CompactVMatrixGaussianKernel   )

DECLARE_OBJECT_PTR (  ClassMarginCostFunction   )

DECLARE_OBJECT_PTR (  ClassErrorCostFunction   )

DECLARE_OBJECT_PTR (  ClassDistanceProportionCostFunction   )

DECLARE_OBJECT_PTR (  AdditiveNormalizationKernel   )

DECLARE_OBJECT_PTR (  FilePStreamBuf   )

DECLARE_OBJECT_PTR (  UCISpecification   )

DECLARE_TYPE_TRAITS (  VarArray   )

DECLARE_TYPE_TRAITS (  StatsItArray   )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  bool  , 0x12  , 0x12  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  double  , 0x10  , 0x11  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  float  , 0x0E  , 0x0F  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  long  , 0x07  , 0x08  )

PLearn::DECLARE_TYPE_TRAITS_FOR_BASETYPE (  unsigned  int, 0x0B  , 0x0C  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  int  , 0x07  , 0x08  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  unsigned  short, 0x05  , 0x06  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  short  , 0x03  , 0x04  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  unsigned  char, 0x02  , 0x02  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  signed  char, 0x01  , 0x01  )

DECLARE_TYPE_TRAITS_FOR_BASETYPE (  char  , 0x01  , 0x01  )

template<class ObjectType, class OptionType> void declareOption (  OptionList &  ol, const string &  optionname, OptionType *ObjectType::*  member_ptr, OptionBase::flag_t  flags, const string &  description, const string &  defaultval = "" )  [inline]

Definition at line 121 of file Option.h.

template<class ObjectType, class OptionType> void declareOption (  OptionList &  ol, const string &  optionname, OptionType ObjectType::*  member_ptr, OptionBase::flag_t  flags, const string &  description, const string &  defaultval = "" )  [inline]

For flags, you should specify one of OptionBase::buildoption, OptionBase::learntoption or OptionBase::tuningoption If the option is not to be serialized, you can additionally specify OptionBase::nosave. The "type" printed in the help is given by TypeTraits<OptionType>::name(). The "default value" printed in optionHelp() will be a serialization of the value of the field in a default constructed instance, (which should be ok in most cases), unless you explicitly specify it as the last argument here (It is recomended that you *don't* specify it explicitly, unless you really must). Parameters: ol  the list to which this option should be appended optionname  the name of this option member_ptr  &YourClass::your_field description  see the flags in OptionBase a description of the option defaultval  the default value for this option, as set by the default constructor Definition at line 111 of file Option.h. References PLearn::OptionBase::flag_t. Referenced by PLearn::YMDDatedVMatrix::declareOptions(), PLearn::WeightedCostFunction::declareOptions(), PLearn::VVMatrix::declareOptions(), PLearn::VVec::declareOptions(), PLearn::VMatrixFromDistribution::declareOptions(), PLearn::VMatrix::declareOptions(), PLearn::PreprocessingVMatrix::declareOptions(), PLearn::VMatLanguage::declareOptions(), PLearn::VecStatsCollector::declareOptions(), PLearn::VecExtendedVMatrix::declareOptions(), PLearn::VecElementVariable::declareOptions(), PLearn::Variable::declareOptions(), PLearn::UniformVMatrix::declareOptions(), PLearn::UniformizeVMatrix::declareOptions(), PLearn::UniformDistribution::declareOptions(), PLearn::UnfoldedSumOfVariable::declareOptions(), PLearn::UnfoldedFuncVariable::declareOptions(), PLearn::UnequalConstantVariable::declareOptions(), PLearn::UnaryVariable::declareOptions(), PLearn::UnaryHardSlopeVariable::declareOptions(), PLearn::UCISpecification::declareOptions(), PLearn::TrainValidTestSplitter::declareOptions(), PLearn::TrainTestSplitter::declareOptions(), PLearn::TrainTestBagsSplitter::declareOptions(), PLearn::Train::declareOptions(), PLearn::ToBagSplitter::declareOptions(), PLearn::TextSenseSequenceVMatrix::declareOptions(), PLearn::TestMethod::declareOptions(), PLearn::TestInTrainSplitter::declareOptions(), PLearn::TestingLearner::declareOptions(), PLearn::TemporalHorizonVMatrix::declareOptions(), PLearn::TangentLearner::declareOptions(), PLearn::SumOverBagsVariable::declareOptions(), PLearn::SumOfVariable::declareOptions(), PLearn::SubVMatrix::declareOptions(), PLearn::SubsampleVariable::declareOptions(), PLearn::SubMatVariable::declareOptions(), PLearn::SubMatTransposeVariable::declareOptions(), PLearn::SubInputVMatrix::declareOptions(), PLearn::QuantilesStatsIterator::declareOptions(), PLearn::LiftStatsIterator::declareOptions(), PLearn::SharpeRatioStatsIterator::declareOptions(), PLearn::StderrStatsIterator::declareOptions(), PLearn::StddevStatsIterator::declareOptions(), PLearn::ExpMeanStatsIterator::declareOptions(), PLearn::MeanStatsIterator::declareOptions(), PLearn::StatsIterator::declareOptions(), PLearn::StatsCollector::declareOptions(), PLearn::StackedLearner::declareOptions(), PLearn::SquaredErrorCostFunction::declareOptions(), PLearn::SpiralDistribution::declareOptions(), PLearn::SpectralClustering::declareOptions(), PLearn::SourceVMatrixSplitter::declareOptions(), PLearn::SourceVMatrix::declareOptions(), PLearn::SourceKernel::declareOptions(), PLearn::SortRowsVMatrix::declareOptions(), PLearn::SoftSlopeVariable::declareOptions(), PLearn::SoftSlopeIntegralVariable::declareOptions(), PLearn::SigmoidPrimitiveKernel::declareOptions(), PLearn::SigmoidalKernel::declareOptions(), PLearn::ShiftAndRescaleVMatrix::declareOptions(), PLearn::ShellScript::declareOptions(), PLearn::SetOption::declareOptions(), PLearn::SequentialValidation::declareOptions(), PLearn::SequentialSplitter::declareOptions(), PLearn::SequentialModelSelector::declareOptions(), PLearn::SequentialLearner::declareOptions(), PLearn::SemiSupervisedProbClassCostVariable::declareOptions(), PLearn::SelectRowsVMatrix::declareOptions(), PLearn::SelectRowsFileIndexVMatrix::declareOptions(), PLearn::SelectInputSubsetLearner::declareOptions(), PLearn::SelectedOutputCostFunction::declareOptions(), PLearn::SelectColumnsVMatrix::declareOptions(), PLearn::ScaledGeneralizedDistanceRBFKernel::declareOptions(), PLearn::ScaledGaussianKernel::declareOptions(), PLearn::ScaledConditionalCDFSmoother::declareOptions(), PLearn::RunObject::declareOptions(), PLearn::RowsSubVMatrix::declareOptions(), PLearn::RowAtPositionVariable::declareOptions(), PLearn::ReshapeVariable::declareOptions(), PLearn::RepeatSplitter::declareOptions(), PLearn::RemoveRowsVMatrix::declareOptions(), PLearn::RemoveDuplicateVMatrix::declareOptions(), PLearn::RemapLastColumnVMatrix::declareOptions(), PLearn::RegularGridVMatrix::declareOptions(), PLearn::ReconstructionWeightsKernel::declareOptions(), PLearn::RealMapping::declareOptions(), PLearn::RangeVMatrix::declareOptions(), PLearn::QuadraticUtilityCostFunction::declareOptions(), PLearn::ProcessingVMatrix::declareOptions(), PLearn::PricingTransactionPairProfitFunction::declareOptions(), PLearn::PrecomputedVMatrix::declareOptions(), PLearn::PrecomputedKernel::declareOptions(), PLearn::PowVariable::declareOptions(), PLearn::PowDistanceKernel::declareOptions(), PLearn::PolynomialKernel::declareOptions(), PLearn::PlusConstantVariable::declareOptions(), PLearn::PLS::declareOptions(), PLearn::PLearnerOutputVMatrix::declareOptions(), PLearn::PLearner::declareOptions(), PLearn::PTester::declareOptions(), PLearn::PDistributionVariable::declareOptions(), PLearn::PDistribution::declareOptions(), PLearn::PConditionalDistribution::declareOptions(), PLearn::PCA::declareOptions(), PLearn::PairsVMatrix::declareOptions(), PLearn::Optimizer::declareOptions(), PLearn::OneHotVMatrix::declareOptions(), PLearn::OneHotVariable::declareOptions(), PLearn::OneHotSquaredLoss::declareOptions(), PLearn::ObjectGenerator::declareOptions(), PLearn::NormalizedDotProductKernel::declareOptions(), PLearn::NNet::declareOptions(), PLearn::NeuralNet::declareOptions(), PLearn::NeighborhoodSmoothnessNNet::declareOptions(), PLearn::NegLogProbCostFunction::declareOptions(), PLearn::NegKernel::declareOptions(), PLearn::NearestNeighborPredictionCost::declareOptions(), PLearn::NaryVariable::declareOptions(), PLearn::MultiInstanceVMatrix::declareOptions(), PLearn::MultiInstanceNNet::declareOptions(), PLearn::MovingAverageVMatrix::declareOptions(), PLearn::MovingAverage::declareOptions(), PLearn::MemoryVMatrix::declareOptions(), PLearn::MatRowVariable::declareOptions(), PLearn::MatrixSumOfVariable::declareOptions(), PLearn::MatrixOneHotSquaredLoss::declareOptions(), PLearn::MatrixElementsVariable::declareOptions(), PLearn::MarginPerceptronCostVariable::declareOptions(), PLearn::ManualBinner::declareOptions(), PLearn::LogOfGaussianDensityKernel::declareOptions(), PLearn::LocalNeighborsDifferencesVMatrix::declareOptions(), PLearn::LocallyWeightedDistribution::declareOptions(), PLearn::LLEKernel::declareOptions(), PLearn::LLE::declareOptions(), PLearn::LinearRegressor::declareOptions(), PLearn::LiftStatsCollector::declareOptions(), PLearn::LiftBinaryCostFunction::declareOptions(), PLearn::LearnerProcessedVMatrix::declareOptions(), PLearn::Learner::declareOptions(), PLearn::LaplacianKernel::declareOptions(), PLearn::KPCATangentLearner::declareOptions(), PLearn::KNNVMatrix::declareOptions(), PLearn::KFoldSplitter::declareOptions(), PLearn::KernelVMatrix::declareOptions(), PLearn::KernelProjection::declareOptions(), PLearn::KernelPCA::declareOptions(), PLearn::Kernel::declareOptions(), PLearn::JoinVMatrix::declareOptions(), PLearn::IsomapTangentLearner::declareOptions(), PLearn::Isomap::declareOptions(), PLearn::IsMissingVariable::declareOptions(), PLearn::IsAboveThresholdVariable::declareOptions(), PLearn::InterleaveVMatrix::declareOptions(), PLearn::IndexedVMatrix::declareOptions(), PLearn::IndexAtPositionVariable::declareOptions(), PLearn::HTryCombinations::declareOptions(), PLearn::HCoordinateDescent::declareOptions(), PLearn::HTryAll::declareOptions(), PLearn::HSetVal::declareOptions(), PLearn::HyperOptimizer::declareOptions(), PLearn::HistogramDistribution::declareOptions(), PLearn::GraphicalBiText::declareOptions(), PLearn::Grapher::declareOptions(), PLearn::GramVMatrix::declareOptions(), PLearn::GradientOptimizer::declareOptions(), PLearn::GeodesicDistanceKernel::declareOptions(), PLearn::GenerateDecisionPlot::declareOptions(), PLearn::GeneralizedOneHotVMatrix::declareOptions(), PLearn::GeneralizedDistanceRBFKernel::declareOptions(), PLearn::GaussMix::declareOptions(), PLearn::GaussianProcessRegressor::declareOptions(), PLearn::GaussianKernel::declareOptions(), PLearn::GaussianDistribution::declareOptions(), PLearn::GaussianDensityKernel::declareOptions(), PLearn::GaussianContinuum::declareOptions(), PLearn::Function::declareOptions(), PLearn::FractionSplitter::declareOptions(), PLearn::ForwardVMatrix::declareOptions(), PLearn::FinancePreprocVMatrix::declareOptions(), PLearn::FilterSplitter::declareOptions(), PLearn::FilteredVMatrix::declareOptions(), PLearn::FileVMatrix::declareOptions(), PLearn::FilePStreamBuf::declareOptions(), PLearn::ExtendedVMatrix::declareOptions(), PLearn::ExtendedVariable::declareOptions(), PLearn::ExplicitSplitter::declareOptions(), PLearn::Experiment::declareOptions(), PLearn::EqualConstantVariable::declareOptions(), PLearn::EntropyContrast::declareOptions(), PLearn::EmbeddedSequentialLearner::declareOptions(), PLearn::EmbeddedLearner::declareOptions(), PLearn::ElementAtPositionVariable::declareOptions(), PLearn::DuplicateScalarVariable::declareOptions(), PLearn::DuplicateRowVariable::declareOptions(), PLearn::DuplicateColumnVariable::declareOptions(), PLearn::DivisiveNormalizationKernel::declareOptions(), PLearn::Distribution::declareOptions(), PLearn::DistanceKernel::declareOptions(), PLearn::DiskVMatrix::declareOptions(), PLearn::Dictionary::declareOptions(), PLearn::DBSplitter::declareOptions(), PLearn::DatedJoinVMatrix::declareOptions(), PLearn::CutBelowThresholdVariable::declareOptions(), PLearn::CutAboveThresholdVariable::declareOptions(), PLearn::CumVMatrix::declareOptions(), PLearn::CrossReferenceVMatrix::declareOptions(), PLearn::ConvexBasisKernel::declareOptions(), PLearn::ConstantRegressor::declareOptions(), PLearn::ConjGradientOptimizer::declareOptions(), PLearn::ConditionalStatsCollector::declareOptions(), PLearn::ConditionalGaussianDistribution::declareOptions(), PLearn::ConditionalDensityNet::declareOptions(), PLearn::ConditionalCDFSmoother::declareOptions(), PLearn::ConcatRowsVMatrix::declareOptions(), PLearn::ConcatRowsSubVMatrix::declareOptions(), PLearn::ConcatOfVariable::declareOptions(), PLearn::ConcatColumnsVMatrix::declareOptions(), PLearn::CompactVMatrixPolynomialKernel::declareOptions(), PLearn::CompactVMatrixGaussianKernel::declareOptions(), PLearn::ClassMarginCostFunction::declareOptions(), PLearn::ClassifierFromDensity::declareOptions(), PLearn::ClassErrorCostFunction::declareOptions(), PLearn::CenteredVMatrix::declareOptions(), PLearn::BootstrapVMatrix::declareOptions(), PLearn::BootstrapSplitter::declareOptions(), PLearn::BinaryVariable::declareOptions(), PLearn::BatchVMatrix::declareOptions(), PLearn::AutoVMatrix::declareOptions(), PLearn::AsciiVMatrix::declareOptions(), PLearn::AffineTransformWeightPenalty::declareOptions(), PLearn::AdditiveNormalizationKernel::declareOptions(), PLearn::AddCostToLearner::declareOptions(), PLearn::AdaptGradientOptimizer::declareOptions(), and PLearn::AdaBoost::declareOptions().

template<class T> TVec<T> deepCopy (  const TVec< T > &  source, CopiesMap &  copies )  [inline]

Definition at line 88 of file TVec_impl.h. References PLearn::TVec< T >::deepCopy().

template<class T> TVec<T> deepCopy (  const TVec< T > &  source  )  [inline]

< create empty map Definition at line 81 of file TVec_impl.h. References CopiesMap, and deepCopy().

template<class T> TMat<T> deepCopy (  const TMat< T >  source, CopiesMap  copies )  [inline]

Definition at line 599 of file TMat_impl.h. References PLearn::TMat< T >::deepCopy().

template<class T> TMat<T> deepCopy (  const TMat< T >  source  )  [inline]

< create empty map Definition at line 592 of file TMat_impl.h. References CopiesMap, and deepCopy().

template<class T> T* deepCopy (  PP< T >  source  )  [inline]

This function simply calls the previous one with an initially empty map. makes a copie of the PP with all it's fields, respecting the dependance shceme between elements and without allowing double copies of equal elements. < create empty map Definition at line 224 of file PP.h. References CopiesMap, and deepCopy().

template<class T> T* deepCopy (  PP< T >  source, CopiesMap &  copies )

A simple template function. Definition at line 215 of file PP.h. References deepCopy().

template<class T> T* deepCopy (  const T *  source  )  [inline]

This function simply calls the previous one with an initially empty map. < create empty map Definition at line 84 of file CopiesMap.h. References CopiesMap, and deepCopy().

template<class T> T* deepCopy (  const T *  source, CopiesMap &  copies )

A simple template function that calls the method. Definition at line 79 of file CopiesMap.h. Referenced by PLearn::ClassifierFromDensity::build_(), and deepCopy().

template<> void PLearn::deepCopyField (  VMat &  field, CopiesMap &  copies )

Definition at line 115 of file VMat.cc.

template<> void deepCopyField (  VarArray &  field, CopiesMap &  copies )  [inline]

Definition at line 235 of file VarArray.h. References PLearn::VarArray::makeDeepCopyFromShallowCopy().

template<> void deepCopyField (  Var &  field, CopiesMap &  copies )  [inline]

Specialized in order to display a warning message. Definition at line 74 of file Var.h. References PLWARNING.

template<> void PLearn::deepCopyField (  Func &  field, CopiesMap &  copies )

Definition at line 638 of file Func.cc.

template<class T> void deepCopyField (  TVec< T > &  field, CopiesMap &  copies )  [inline]

Definition at line 94 of file TVec_impl.h. References PLearn::TVec< T >::makeDeepCopyFromShallowCopy().

template<class T> void deepCopyField (  TMat< T > &  field, CopiesMap &  copies )  [inline]

Definition at line 603 of file TMat_impl.h. References PLearn::TMat< T >::makeDeepCopyFromShallowCopy().

template<> void deepCopyField (  StatsItArray &  field, CopiesMap &  copies )  [inline]

Definition at line 402 of file StatsIterator.h. References PLearn::Array< StatsIt >::makeDeepCopyFromShallowCopy().

template<> void deepCopyField (  StatsCollector &  field, CopiesMap &  copies )  [inline]

Apparently needed to specialize this method, otherwise it was the generic deepCopyField from CopiesMap.h that was called when deep copying a TVec<StatsCollector>. Definition at line 216 of file StatsCollector.h. References PLearn::Object::makeDeepCopyFromShallowCopy().

template<> void deepCopyField (  Mat &  field, CopiesMap &  copies )  [inline]

Definition at line 103 of file Mat.h. References PLearn::TMat< T >::makeDeepCopyFromShallowCopy().

template<> void deepCopyField (  Vec &  field, CopiesMap &  copies )  [inline]

Definition at line 72 of file Mat.h. References PLearn::TVec< T >::makeDeepCopyFromShallowCopy().

template<> void deepCopyField (  Ker &  field, CopiesMap &  copies )  [inline]

Definition at line 224 of file Kernel.h.

template<class T> void deepCopyField (  PP< T > &  field, CopiesMap &  copies )  [inline]

Any pointer or smart pointer: call deepCopy(). Definition at line 198 of file PP.h. References PLWARNING.

template<class T> void deepCopyField (  T *&  field, CopiesMap &  copies )  [inline]

Definition at line 71 of file CopiesMap.h.

template<class T> void deepCopyField (  T &  , CopiesMap &  )  [inline]

Types that do not require deep copy. Any type not handled below: do nothing. Support for generic deep copying Deep copying is defined for objects in the following manner: + copy constructors should always do a shallow copy. + a public method OBJTYPE* deepCopy(map<const void*, void*>& copies) const should be defined to allow deepCopying + the deepCopy method should be virtual for classes that are designed to be subclassed Take a close look at the Object class in Object.h to see how this is done. no op Definition at line 61 of file CopiesMap.h. References PLWARNING.

template<class T> void deepCopyField (  Array< T > &  field, CopiesMap &  copies )  [inline]

Definition at line 86 of file Array_impl.h. References PLearn::Array< T >::makeDeepCopyFromShallowCopy(). Referenced by PLearn::Storage< pair< real, real > >::deepCopy(), PLearn::WeightedCostFunction::makeDeepCopyFromShallowCopy(), PLearn::VVMatrix::makeDeepCopyFromShallowCopy(), PLearn::VMatrix::makeDeepCopyFromShallowCopy(), PLearn::VecStatsCollector::makeDeepCopyFromShallowCopy(), PLearn::VecElementVariable::makeDeepCopyFromShallowCopy(), PLearn::Variable::makeDeepCopyFromShallowCopy(), PLearn::UnfoldedSumOfVariable::makeDeepCopyFromShallowCopy(), PLearn::UnfoldedFuncVariable::makeDeepCopyFromShallowCopy(), PLearn::TVec< T >::makeDeepCopyFromShallowCopy(), PLearn::TMat< T >::makeDeepCopyFromShallowCopy(), PLearn::TextSenseSequenceVMatrix::makeDeepCopyFromShallowCopy(), PLearn::TemporalHorizonVMatrix::makeDeepCopyFromShallowCopy(), PLearn::TangentLearner::makeDeepCopyFromShallowCopy(), PLearn::SumOverBagsVariable::makeDeepCopyFromShallowCopy(), PLearn::SumOfVariable::makeDeepCopyFromShallowCopy(), PLearn::SubVMatrix::makeDeepCopyFromShallowCopy(), PLearn::QuantilesStatsIterator::makeDeepCopyFromShallowCopy(), PLearn::LiftStatsIterator::makeDeepCopyFromShallowCopy(), PLearn::SharpeRatioStatsIterator::makeDeepCopyFromShallowCopy(), PLearn::StderrStatsIterator::makeDeepCopyFromShallowCopy(), PLearn::StddevStatsIterator::makeDeepCopyFromShallowCopy(), PLearn::StatsIterator::makeDeepCopyFromShallowCopy(), PLearn::StackedLearner::makeDeepCopyFromShallowCopy(), PLearn::Splitter::makeDeepCopyFromShallowCopy(), PLearn::SourceVMatrix::makeDeepCopyFromShallowCopy(), PLearn::SourceVariable::makeDeepCopyFromShallowCopy(), PLearn::SourceKernel::makeDeepCopyFromShallowCopy(), PLearn::SortRowsVMatrix::makeDeepCopyFromShallowCopy(), PLearn::ShellScript::makeDeepCopyFromShallowCopy(), PLearn::SequentialModelSelector::makeDeepCopyFromShallowCopy(), PLearn::SequentialLearner::makeDeepCopyFromShallowCopy(), PLearn::SelectRowsVMatrix::makeDeepCopyFromShallowCopy(), PLearn::SelectInputSubsetLearner::makeDeepCopyFromShallowCopy(), PLearn::SelectedOutputCostFunction::makeDeepCopyFromShallowCopy(), PLearn::SelectColumnsVMatrix::makeDeepCopyFromShallowCopy(), PLearn::ScaledLaplacianKernel::makeDeepCopyFromShallowCopy(), PLearn::ScaledGeneralizedDistanceRBFKernel::makeDeepCopyFromShallowCopy(), PLearn::ScaledGaussianKernel::makeDeepCopyFromShallowCopy(), PLearn::RowBufferedVMatrix::makeDeepCopyFromShallowCopy(), PLearn::RepeatSplitter::makeDeepCopyFromShallowCopy(), PLearn::RegularGridVMatrix::makeDeepCopyFromShallowCopy(), PLearn::PTester::makeDeepCopyFromShallowCopy(), PLearn::PrecomputedVMatrix::makeDeepCopyFromShallowCopy(), PLearn::PrecomputedKernel::makeDeepCopyFromShallowCopy(), PLearn::PLS::makeDeepCopyFromShallowCopy(), PLearn::PLearnerOutputVMatrix::makeDeepCopyFromShallowCopy(), PLearn::PLearner::makeDeepCopyFromShallowCopy(), PLearn::PDistributionVariable::makeDeepCopyFromShallowCopy(), PLearn::PDistribution::makeDeepCopyFromShallowCopy(), PLearn::PCA::makeDeepCopyFromShallowCopy(), PLearn::Optimizer::makeDeepCopyFromShallowCopy(), PLearn::NNet::makeDeepCopyFromShallowCopy(), PLearn::NeuralNet::makeDeepCopyFromShallowCopy(), PLearn::NeighborhoodSmoothnessNNet::makeDeepCopyFromShallowCopy(), PLearn::NegKernel::makeDeepCopyFromShallowCopy(), PLearn::NaryVariable::makeDeepCopyFromShallowCopy(), PLearn::MultiInstanceVMatrix::makeDeepCopyFromShallowCopy(), PLearn::MultiInstanceNNet::makeDeepCopyFromShallowCopy(), PLearn::MovingAverageVMatrix::makeDeepCopyFromShallowCopy(), PLearn::MemoryVMatrix::makeDeepCopyFromShallowCopy(), PLearn::MatRowVariable::makeDeepCopyFromShallowCopy(), PLearn::MatrixSumOfVariable::makeDeepCopyFromShallowCopy(), PLearn::MatrixElementsVariable::makeDeepCopyFromShallowCopy(), PLearn::LogSumVariable::makeDeepCopyFromShallowCopy(), PLearn::LocalNeighborsDifferencesVMatrix::makeDeepCopyFromShallowCopy(), PLearn::LinearRegressor::makeDeepCopyFromShallowCopy(), PLearn::LiftStatsCollector::makeDeepCopyFromShallowCopy(), PLearn::LearnerProcessedVMatrix::makeDeepCopyFromShallowCopy(), PLearn::Learner::makeDeepCopyFromShallowCopy(), PLearn::KNNVMatrix::makeDeepCopyFromShallowCopy(), PLearn::Kernel::makeDeepCopyFromShallowCopy(), PLearn::JulianizeVMatrix::makeDeepCopyFromShallowCopy(), PLearn::IndexedVMatrix::makeDeepCopyFromShallowCopy(), PLearn::HistogramDistribution::makeDeepCopyFromShallowCopy(), PLearn::GaussMix::makeDeepCopyFromShallowCopy(), PLearn::GaussianProcessRegressor::makeDeepCopyFromShallowCopy(), PLearn::GaussianKernel::makeDeepCopyFromShallowCopy(), PLearn::GaussianDistribution::makeDeepCopyFromShallowCopy(), PLearn::GaussianContinuum::makeDeepCopyFromShallowCopy(), PLearn::Function::makeDeepCopyFromShallowCopy(), PLearn::ForwardVMatrix::makeDeepCopyFromShallowCopy(), PLearn::FinancePreprocVMatrix::makeDeepCopyFromShallowCopy(), PLearn::FilterSplitter::makeDeepCopyFromShallowCopy(), PLearn::ExplicitSplitter::makeDeepCopyFromShallowCopy(), PLearn::EmpiricalDistribution::makeDeepCopyFromShallowCopy(), PLearn::EmbeddedSequentialLearner::makeDeepCopyFromShallowCopy(), PLearn::EmbeddedLearner::makeDeepCopyFromShallowCopy(), PLearn::DatedJoinVMatrix::makeDeepCopyFromShallowCopy(), PLearn::CumVMatrix::makeDeepCopyFromShallowCopy(), PLearn::ConditionalStatsCollector::makeDeepCopyFromShallowCopy(), PLearn::ConditionalDensityNet::makeDeepCopyFromShallowCopy(), PLearn::ConditionalCDFSmoother::makeDeepCopyFromShallowCopy(), PLearn::ConcatOfVariable::makeDeepCopyFromShallowCopy(), PLearn::CompactVMatrix::makeDeepCopyFromShallowCopy(), PLearn::ClassifierFromDensity::makeDeepCopyFromShallowCopy(), PLearn::BatchVMatrix::makeDeepCopyFromShallowCopy(), PLearn::Array< T >::makeDeepCopyFromShallowCopy(), PLearn::ArgminOfVariable::makeDeepCopyFromShallowCopy(), PLearn::AddCostToLearner::makeDeepCopyFromShallowCopy(), and PLearn::AdaBoost::makeDeepCopyFromShallowCopy().

Var det (  Var  m  )  [inline]

Definition at line 78 of file DeterminantVariable.h.

template<class T> T det (  const TMat< T > &  LU, int  detsign )

Definition at line 5262 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> T det (  const TMat< T > &  A  )

Definition at line 5240 of file TMat_maths_impl.h. References PLearn::TMat< T >::copy(), PLearn::TMat< T >::length(), LU_decomposition(), maxabs(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DeterminantVariable::fprop(), and PLearn::ProductRandomVariable::invertible().

double PLearn::determine_density_integral_from_log_densities_on_grid (  Vec  log_densities, real  deltax, real  deltay )

Definition at line 80 of file learner_utils.cc. References exp(), and logadd().

void PLearn::determine_grid_for_dataset (  VMat  dataset, int  nx, int  ny, real &  x0, real &  y0, real &  deltax, real &  deltay, real  extraspace = .10 )

Definition at line 65 of file learner_utils.cc. References computeRange(), and PLearn::VMat::subMatColumns().

void dgesdd_ (  char *  JOBZ, int *  M, int *  N, double *  A, int *  LDA, double *  S, double *  U, int *  LDU, double *  VT, int *  LDVT, double *  WORK, int *  LWORK, int *  IWORK, int *  INFO )

Referenced by lapack_Xgesdd_().

void dgesv_ (  int *  N, int *  NRHS, double *  A, int *  LDA, int *  IPIV, double *  B, int *  LDB, int *  INFO )

Referenced by lapackSolveLinearSystem().

void dgetrf_ (  int *  M, int *  N, double *  A, int *  LDA, int *  IPIV, int *  INFO )

Referenced by matInvert().

void dgetri_ (  int *  N, double *  A, int *  LDA, int *  IPIV, double *  WORK, int *  LWORK, int *  INFO )

Referenced by matInvert().

template<class T> TVec<T> diag (  const TMat< T > &  mat  )

Definition at line 3656 of file TMat_maths_impl.h. References diag(), and PLearn::TMat< T >::length().

template<class T> void diag (  const TMat< T > &  mat, const TVec< T > &  d )

Definition at line 3648 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TMat< T >::length(). Referenced by PLearn::EntropyContrast::compute_df_dx(), diag(), PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost(), and PLearn::EntropyContrast::train().

Var diagonalized_factors_product (  Var  left_matrix, Var  center_diagonal, Var  right_matrix )  [inline]

Definition at line 87 of file DiagonalizedFactorsProductVariable.h. Referenced by PLearn::TangentLearner::build_(), and PLearn::GaussianContinuum::build_().

template<class T> void diagonalizedFactorsProduct (  TMat< T > &  result, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, bool  accumulate = false )

return the matrix with elements (i,j) = sum_k U_{ik} d_k V_{kj} Definition at line 2421 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::EntropyContrast::compute_df_dx(), PLearn::DiagonalizedFactorsProductVariable::fprop(), and PLearn::EntropyContrast::train().

template<class T> void diagonalizedFactorsProductBprop (  const TMat< T > &  dCdresult, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, TMat< T > &  dCdU, TVec< T > &  dCdd, TMat< T > &  dCdV )

GIVEN that res(i,j) = sum_k U_{ik} d_k V_{kj}, and given dC/dres, U,d and V, accumulate gradients on dC/dU, dC/dd and dC/dV: dC/dU[i,k] += sum_j dC/dres[i,j] d_k V[k,j] dC/dd[k] += sum_{ij} dC/dres[i,j] U[i,k] V[k,j] dC/dV[k,j] += d_k * sum_i U[i,k] dC/dres[i,j]. Definition at line 2455 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::bprop().

template<class T> void diagonalizedFactorsProductTranspose (  TMat< T > &  result, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, bool  accumulate = false )

return the matrix with elements (i,j) = sum_k U_{ik} d_k V_{jk} Definition at line 2492 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::fprop().

template<class T> void diagonalizedFactorsProductTransposeBprop (  const TMat< T > &  dCdresult, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, TMat< T > &  dCdU, TVec< T > &  dCdd, TMat< T > &  dCdV )

Definition at line 2527 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::bprop().

template<class T> void diagonalizedFactorsTransposeProduct (  TMat< T > &  result, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, bool  accumulate = false )

return the matrix with elements (i,j) = sum_k U_{ki} d_k V_{kj} Definition at line 2567 of file TMat_maths_impl.h. References PLearn::TMat< T >::clear(), PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::fprop().

template<class T> void diagonalizedFactorsTransposeProductBprop (  const TMat< T > &  dCdresult, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, TMat< T > &  dCdU, TVec< T > &  dCdd, TMat< T > &  dCdV )

Definition at line 2600 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::bprop().

template<class T> void diagonalizedFactorsTransposeProductTranspose (  TMat< T > &  result, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, bool  accumulate = false )

return the matrix with elements (i,j) = sum_k U_{ki} d_k V_{jk} Definition at line 2641 of file TMat_maths_impl.h. References PLearn::TMat< T >::clear(), PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::fprop().

template<class T> void diagonalizedFactorsTransposeProductTransposeBprop (  const TMat< T > &  dCdresult, const TMat< T > &  U, const TVec< T >  d, const TMat< T >  V, TMat< T > &  dCdU, TVec< T > &  dCdd, TMat< T > &  dCdV )

Definition at line 2673 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), k, PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::DiagonalizedFactorsProductVariable::bprop().

template<class MatT> void diagonalizeSubspace (  MatT &  A, Mat &  X, Vec &  Ax, Mat &  solutions, Vec &  evalues, Mat &  evectors )

Diagonalize the sub-space spanned by the rows of X(mxn) with respect to symmetric matrix A(nxn), m<=n. The eigenpairs will be put in the evalues/evectors arguments (expressed in the basis of X), and the corresponding basis in R^n will be put in the solutions(kxn) matrix. The function proceeds as follows: GramSchmid orthornormalize X, so that X X' = I(mxm) C(mxm) = X A X' solve small eigensystem C = V' S V (V = evectors, S = evalues) solutions = V X Thus in the end we have solutions solutions' = V X X' V' = I if X was orthonormal to start with solutions A solutions' = V X A X' V' = V C V' = S first collect C = X A X' symmetric part then diagonalize C = evectors' * diag(evalues) * evectors the eigen-values should be in increasing order convert the eigenvectors corresponding to the smallest eigenvalues < already 0 Definition at line 641 of file plapack.h. References PLearn::TVec< T >::clear(), PLearn::TMat< T >::copy(), dot(), eigen_SymmMat(), endl(), GramSchmidtOrthogonalization(), PLearn::TMat< T >::length(), multiplyAcc(), norm(), product(), and PLearn::TMat< T >::subMatRows().

template<class T> TMat< T > PLearn::diagonalmatrix (  const TVec< T > &  v  )

Definition at line 578 of file TMat_impl.h. References PLearn::TVec< T >::length().

template<class T> void diagonalOfSquare (  const TMat< T > &  mat, const TVec< T > &  d )

Definition at line 3664 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), and pownorm().

void difference (  Set  a, Set  b, Set  res )  [inline]

Definition at line 93 of file Set.h. References PLearn::Set::begin(), and PLearn::Set::end(). Referenced by PLearn::FieldConvertCommand::FieldConvertCommand(), PLearn::MinusRandomVariable::MinusRandomVariable(), and PLearn::PlusRandomVariable::PlusRandomVariable().

template<class T> void diffSquareMultiplyAcc (  const TMat< T > &  mat, const TMat< T > &  x, const TMat< T > &  y, T  scale )

Definition at line 3565 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::width(), and x.

template<class T> void diffSquareMultiplyAcc (  const TVec< T > &  vec, const TVec< T > &  x, const TVec< T > &  y, T  scale )

TVec[i] += (x[i]-y[i])^2*scale;. Definition at line 2173 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::DiagonalNormalRandomVariable::EMBprop().

template<class T> void diffSquareMultiplyScaledAcc (  const TVec< T > &  vec, const TVec< T > &  x, const TVec< T > &  y, T  fact1, T  fact2 )

TVec[i] = TVec[i]*fact1 + (x[i]-y[i])^2*fact2;. Definition at line 2191 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x.

Var dilogarithm (  Var  v  )  [inline]

Definition at line 75 of file DilogarithmVariable.h.

real PLearn::dilogarithm (  real  x  )

return the dilogarithm function dilogarithm(x) = sum_{i=1}^{} x^i/i^2 = int_{z=x}^0 log(1-z)/z dz It is also useful because -dilogarithm(-exp(x)) is the primitive of the softplus function log(1+exp(x)). Definition at line 203 of file pl_math.cc. References is_missing(), MISSING_VALUE, PLWARNING, positive_dilogarithm(), and x. Referenced by PLearn::DilogarithmVariable::fprop(), and softplus_primitive().

CostFunc directnegative_costfunc (   )  [inline]

Definition at line 67 of file DirectNegativeCostFunction.h.

void PLearn::displayBasicStats (  VMat  vm  )

Definition at line 174 of file vmatmain.cc. References endl(), PLearn::VMat::fieldName(), k, and PLearn::VMat::width(). Referenced by vmatmain().

void PLearn::displayDecisionSurface (  GhostScript &  gs, real  destx, real  desty, real  destwidth, real  destheight, Learner &  learner, Mat  trainset, Vec  svindexes = Vec(), Vec  outlierindexes = Vec(), int  nextsvindex = -1, real  min_x = -1, real  max_x = +1, real  min_y = -1, real  max_y = +1, real  radius = 0.05, int  nx = 200, int  ny = 200 )

This will display a rectangle (0,0,nx,ny) containing a 2D image of the decision surface for the given learner with the points of each class displayed with + and x, and optionally, the points in svindexes circled in black and the points in outlierindexes circled in gray. Definition at line 696 of file DisplayUtils.cc. References PLearn::TMat< T >::column(), compute2dGridOutputs(), PLearn::GhostScript::displayGray(), displayPoints(), PLearn::GhostScript::drawCircle(), PLearn::GhostScript::grestore(), PLearn::GhostScript::gsave(), PLearn::Learner::inputsize(), k, PLearn::TVec< T >::length(), PLearn::GhostScript::mapping(), max(), min(), PLearn::Learner::outputsize(), PLearn::GhostScript::setdash(), PLearn::GhostScript::setlinewidth(), and x.

void PLearn::displayFunction (  Func  f, bool  display_values = false, bool  display_differentiation = false, real  boxwidth = 100, const char *  the_filename = 0, bool  must_wait = true )

Definition at line 614 of file DisplayUtils.cc. References displayVarGraph(). Referenced by PLearn::SumOfVariable::fbprop(), PLearn::ConditionalDensityNet::train(), and PLearn::Function::verifySymbolicGradient().

void PLearn::displayHistogram (  Gnuplot &  gs, Mat  dataColumn, int  n_bins = 0, Vec *  bins = 0, bool  regular_bins = false, bool  normalized = false, string  extra_args = "" )

Display a histogram of the density of the data column. By default set n_bins according to the number of data points: n_bins = min(5+dataColumn.length()/10,1000), but user can override this. By default (n_bins=0) the bins are equally spaced such that each bin receives approximately the same number of points. The user can override that by providing a vector of bin boundaries (min to bins[0], bins[0] to bins[1], ... bins[n_bins-2] to max) then n_bins will be bins.length()+1, or the user can specify regularly spaced bins with the bool argument. If normalized the the relative frequencies rather than actual frequencies are plotted. Definition at line 57 of file DisplayUtils.cc. References PLearn::TVec< T >::data(), histogram(), left(), PLearn::TVec< T >::length(), MIN, PLearn::Gnuplot::plot(), PLWARNING, PLearn::TVec< T >::resize(), sortElements(), and PLearn::TMat< T >::toVecCopy().

void PLearn::displayObjectHelp (  ostream &  out, const string &  classname )

Will display the help message for an object of the given classname. Definition at line 105 of file TypeFactory.cc. References addprefix(), PLearn::TypeMapEntry::constructor, endl(), PLearn::TypeMapEntry::getoptionlist_method, PLearn::TypeFactory::getTypeMap(), PLearn::TypeMapEntry::isa_method, PLearn::TypeMapEntry::multi_line_help, PLearn::TypeMapEntry::one_line_descr, PLERROR, and TypeMap. Referenced by old_plearn_main(), and PLearn::HelpCommand::run().

void PLearn::displayPoints (  GhostScript &  gs, Mat  data, real  radius, bool  color = false )

this draws x and + with the given radius for all the points in data (supposed to have width 3: [x, y, classnum] Definition at line 649 of file DisplayUtils.cc. References color(), PLearn::GhostScript::drawCross(), PLearn::TMat< T >::length(), and PLearn::GhostScript::setcolor(). Referenced by displayDecisionSurface().

void PLearn::displayVarGraph (  const VarArray &  outputs, bool  display_values = false, real  boxwidth = 100, const char *  the_filename = 0, bool  must_wait = true, VarArray  display0_only_these = VarArray() )

VarGraph *. * VarGraph * Definition at line 177 of file DisplayUtils.cc. References PLearn::TmpFilenames::addFilename(), PLearn::VarArray::ancestors(), PLearn::TVec< Var >::append(), center(), PLearn::GhostScript::centerShow(), PLearn::VarArray::clearMark(), PLearn::TVec< Var >::contains(), distance(), PLearn::GhostScript::drawArrow(), PLearn::GhostScript::drawBox(), PLearn::Var::length(), PLearn::VarArray::setMark(), PLearn::TVec< Var >::size(), PLearn::VarArray::sources(), PLearn::VarArray::unmarkAncestors(), PLearn::GhostScript::usefont(), PLearn::Var::width(), and x. Referenced by displayFunction(), PLearn::Function::fbprop(), PLearn::LogVariable::fprop(), PLearn::GradientOptimizer::optimize(), and PLearn::GradientOptimizer::optimizeN().

template<class T> T dist (  const TVec< T > &  vec1, const TVec< T > &  vec2, double  n )

Definition at line 760 of file TMat_maths_impl.h. References mypow(), powdistance(), and sqrt(). Referenced by PLearn::GeodesicDistanceKernel::computeNearestGeodesicNeighbour(), PLearn::Kernel::estimateHistograms(), PLearn::GeodesicDistanceKernel::evaluate(), PLearn::DistanceKernel::evaluate(), findClosestPairsOfDifferentClass(), PLearn::GaussMix::kmeans(), L1distance(), L2distance(), PLearn::PDistributionVariable::PDistributionVariable(), positionOfClosestElement(), PLearn::GeodesicDistanceKernel::setDataForKernelMatrix(), PLearn::PLS::train(), and PLearn::ManifoldParzen2::train().

Var PLearn::distance (  Var  input1, Var  input2, real  n )

Definition at line 103 of file Var_utils.cc. References norm(). Referenced by displayVarGraph().

template<class T> void divide (  T  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1539 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLearn::TVec< T >::resize().

template<class T> void divide (  const TVec< T > &  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1522 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize().

template<class T> void divide (  const TVec< T > &  source1, T  source2, TVec< T > &  destination )  [inline]

Definition at line 1517 of file TMat_maths_impl.h. References multiply(). Referenced by makeRowsSumTo1(), and PLearn::TestDependenciesCommand::TestDependenciesCommand().

int dnaupd_ (  long int *  , const char *  , long int *  , const char *  , long int *  , double *  , double *  , long int *  , double *  , long int *  , long int *  , long int *  , double *  , double *  , long int *  , long int *  , short  , short  )

Referenced by eigenSparseNonSymmMat().

int dneupd_ (  long int *  , const char *  , long int *  , double *  , double *  , double *  , long int *  , double *  , double *  , double *  , const char *  , long int *  , const char *  , long int *  , double *  , double *  , long int *  , double *  , long int *  , long int *  , long int *  , double *  , double *  , long int *  , long int *  , short  , short  , short  )

Referenced by eigenSparseNonSymmMat().

Var dot (  Var  v1, Var  v2 )  [inline]

dot product Definition at line 82 of file DotProductVariable.h.

template<class T> T dot (  const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 1124 of file TMat_maths_impl.h. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::data(), PLearn::TMat< T >::isCompact(), PLERROR, and PLearn::TMat< T >::size().

template<class V, class T, class U> V dot (  const TVec< T > &  vec1, const TVec< U > &  vec2 )

Special dot product that allows TVec's of different types, as long as operator*(T,U) is defined. The return type V must be specified in all circumstances, e.g. : TVec<int> v1; TVec<float> v2; double result = dot<double>(v1,v2); Definition at line 1108 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

template<class T> T dot (  const TVec< T > &  vec1, const TVec< T > &  vec2 )

Definition at line 1087 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::EntropyContrast::compute_extra_grad_wrt_df_dx(), PLearn::ConjGradientOptimizer::computeCostAndDerivative(), PLearn::KernelProjection::computeCostsFromOutputs(), PLearn::ConjGradientOptimizer::computeDerivative(), PLearn::GaussMix::computeLogLikelihood(), PLearn::PCA::computeOutput(), PLearn::ConjGradientOptimizer::conjpomdp(), constrainedLinearRegression(), PLearn::ConjGradientOptimizer::daiYuan(), diagonalizeSubspace(), PLearn::VMatrix::dot(), PLearn::RowBufferedVMatrix::dot(), PLearn::CompactVMatrix::dot(), PLearn::CompactVMatrix::dotProduct(), PLearn::SigmoidPrimitiveKernel::evaluate(), PLearn::SigmoidalKernel::evaluate(), PLearn::PolynomialKernel::evaluate(), PLearn::NormalizedDotProductKernel::evaluate(), PLearn::DotProductKernel::evaluate(), PLearn::CompactVMatrixPolynomialKernel::evaluate(), PLearn::GaussianProcessRegressor::expectation(), extract_directory(), PLearn::ConjGradientOptimizer::findDirection(), PLearn::ProjectionErrorVariable::fprop(), PLearn::ConjGradientOptimizer::gSearch(), PLearn::ConjGradientOptimizer::hestenesStiefel(), PLearn::GaussianProcessRegressor::inverseCovTimesVec(), linearRegression(), locateDatasetAliasesDir(), logOfCompactGaussian(), logOfNormal(), mahalanobis_distance(), makeExplicitPath(), PLearn::ConjGradientOptimizer::optimize(), PLearn::ConjGradientOptimizer::optimizeN(), PLearn::ConjGradientOptimizer::polakRibiere(), productTranspose(), projectOnOrthogonalSubspace(), PLearn::GaussianProcessRegressor::QFormInverse(), PLearn::TimesScalarVariable::symbolicBprop(), PLearn::PowVariableVariable::symbolicBprop(), PLearn::PLS::train(), PLearn::PCA::train(), PLearn::LinearRegressor::train(), PLearn::Function::verifyGradient(), and weightedLinearRegression().

real dot_product (  real  s, real *  x, real *  y, int  n )  [inline]

Definition at line 368 of file TMat_maths_specialisation.h. References x. Referenced by PLearn::CompactVMatrix::dot(), PLearn::CompactVMatrixPolynomialKernel::evaluate(), and PLearn::PolynomialKernel::evaluateFromDot().

PDateTime PLearn::double_to_datetime (  double  f  )

Definition at line 146 of file PDateTime.cc. References PLearn::PDateTime::day, double_to_hhmmss(), PLearn::PDateTime::hour, is_missing(), PLearn::PDateTime::min, PLearn::PDateTime::month, PLearn::PDateTime::sec, and PLearn::PDateTime::year.

void PLearn::double_to_hhmmss (  double  fraction, int &  hh, int &  mm, int &  ss )

convert a day fraction (< 1) to hours/minutes/seconds Definition at line 173 of file PDateTime.cc. Referenced by double_to_datetime(), and PLearn::PDateTime::PDateTime().

int dsaupd_ (  long int *  , const char *  , long int *  , const char *  , long int *  , double *  , double *  , long int *  , double *  , long int *  , long int *  , long int *  , double *  , double *  , long int *  , long int *  , short  , short  )

Referenced by eigenSparseSymmMat().

int dseupd_ (  long int *  , const char *  , long int *  , double *  , double *  , long int *  , double *  , const char *  , long int *  , const char *  , long int *  , double *  , double *  , long int *  , double *  , long int *  , long int *  , long int *  , double *  , double *  , long int *  , long int *  , short  , short  , short  )

Referenced by eigenSparseSymmMat().

void dsyev_ (  char *  JOBZ, char *  UPLO, int *  N, double *  A, int *  LDA, double *  W, double *  WORK, int *  LWORK, int *  INFO )

Referenced by eigen_SymmMat().

void dsyevr_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, double *  A, int *  LDA, double *  VL, double *  VU, int *  IL, int *  IU, double *  ABSTOL, int *  M, double *  W, double *  Z, int *  LDZ, int *  ISUPPZ, double *  WORK, int *  LWORK, int *  IWORK, int *  LIWORK, int *  INFO )

Referenced by lapack_Xsyevr_().

void dsyevx_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, double *  A, int *  LDA, double *  VL, double *  VU, int *  IL, int *  IU, double *  ABSTOL, int *  M, double *  W, double *  Z, int *  LDZ, double *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )

Referenced by lapack_Xsyevx_().

void dsygvx_ (  int *  ITYPE, char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, double *  A, int *  LDA, double *  B, int *  LDB, double *  VL, double *  VU, int *  IL, int *  IU, double *  ABSTOL, int *  M, double *  W, double *  Z, int *  LDZ, double *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )

Referenced by lapack_Xsygvx_().

Var duplicateColumn (  Var  v, int  the_width )  [inline]

Definition at line 81 of file DuplicateColumnVariable.h. References PLERROR.

Var duplicateRow (  Var  v, int  the_length )  [inline]

Definition at line 80 of file DuplicateRowVariable.h. References PLERROR.

Var duplicateScalar (  Var  v, int  the_length, int  the_width )  [inline]

Definition at line 81 of file DuplicateScalarVariable.h. References PLERROR.

void PLearn::DX_create_dataset_outputs_file (  const string &  filename, PP< PLearner >  learner, VMat  dataset )

Will write a file containing a field with the dataset positions "dset" field will be input -> target, outputs. Definition at line 278 of file GenerateDecisionPlot.cc. References endl(), PLearn::VMat::getExample(), PLearn::VMat::length(), and PLearn::ProgressBar::update(). Referenced by PLearn::GenerateDecisionPlot::run().

void PLearn::DX_create_grid_outputs_file (  const string &  filename, PP< PLearner >  learner, VMat  dataset, int  nx, int  ny, bool  include_datapoint_grid = false, real  xmin = MISSING_VALUE, real  xmax = MISSING_VALUE, real  ymin = MISSING_VALUE, real  ymax = MISSING_VALUE, real  extraspace = .10 )

The "outputs" field will contain sample-grid inputs -> outputs Where the sample grid is made of a regular grid of nx.ny points (in the range [xmin, xmax] x [ymin, ymax]) xmin, xmax, ymin and ymax may be left to MISSING_VALUE, in which case an automatic range will be determined from the range of the points in the given dataset extended by extraspace (ex: .10 == 10%). This regular grid is possibly complemented (if include_datapoint_grid) with an irregular grid made of the x and y coordinates of the dataset that fall within the [xmin, xmax] x [ymin, ymax] range. Definition at line 343 of file GenerateDecisionPlot.cc. References PLearn::ProgressBar::close(), computeRange(), endl(), exp(), PLearn::VMat::getExample(), is_missing(), PLearn::VMat::length(), logadd(), PLearn::VMat::subMatColumns(), tostring(), PLearn::ProgressBar::update(), and x. Referenced by PLearn::GenerateDecisionPlot::run().

void PLearn::DX_save_2D_data (  const string &  filename, const string &  basename, Mat  data )

considers data to have 2d input (first 2 columns of data) Definition at line 207 of file learner_utils.cc. References DX_write_2D_data(), and PLERROR.

void PLearn::DX_save_2D_data_for_grid (  const string &  filename, const string &  basename, int  nx, int  ny, real  x0, real  y0, real  deltax, real  deltay, Mat  data )

data must have nx*ny rows and must corresponds to values associated with the 2D positions of the grid (typically learner outputs on that grid) Definition at line 215 of file learner_utils.cc. References DX_write_2D_data_for_grid(), and PLERROR.

void PLearn::DX_write_2D_data (  ostream &  out, const string &  basename, Mat  data )

considers data to have 2d input (first 2 columns of data) Definition at line 117 of file learner_utils.cc. References PLearn::TMat< T >::length(), and PLearn::TMat< T >::width(). Referenced by DX_save_2D_data().

void PLearn::DX_write_2D_data_for_grid (  ostream &  out, const string &  basename, int  nx, int  ny, real  x0, real  y0, real  deltax, real  deltay, Mat  data )

data must have nx*ny rows and must corresponds to values associated with the 2D positions of the grid (typically learner outputs on that grid) Definition at line 158 of file learner_utils.cc. References PLearn::TMat< T >::length(), and PLearn::TMat< T >::width(). Referenced by DX_save_2D_data_for_grid().

void PLearn::DX_write_2D_fields (  ostream &  out, const string &  basename, Vec  X, Vec  Y, TVec< Mat >  fields )

Definition at line 99 of file GenerateDecisionPlot.cc. References k, PLearn::TVec< T >::length(), and tostring().

void PLearn::DX_write_2D_fields (  ostream &  out, const string &  basename, TVec< Mat >  fields, real  x0, real  y0, real  deltax, real  deltay, TVec< string >  fieldnames = TVec<string>() )

If fieldnames is omitted then the fields will be named basename_0 basename_1 ... Otherwise they are named basename_ followed by the corresponding field name. Definition at line 53 of file GenerateDecisionPlot.cc. References k, PLearn::TVec< T >::length(), and tostring().

int PLearn::eigen_SymmMat (  Mat &  in, Vec &  e_value, Mat &  e_vector, int &  n_evalues_found, bool  compute_all, int  nb_eigen, bool  compute_vectors = true, bool  largest_evalues = true )

This function compute some or all eigenvalues (and optionnaly the corresponding eigenvectors) of a symmetric matrix. The eigenvectors are returned in the ROWS of e_vector. Note1: If compute_all==true, then the field nb_eigen will not be used. Note2: Your input matrix `in' will be over-written Note3: If compute_all=false only some eigen-values (and optionally e-vectors) are computed. This flag allows to select whether those largest in magnitude (the default) or smallest in magnitude are selected. Note4: This function is slightly modified. Now, you do not have to check if your input matrix is symmetric or not. Note5: The vectors and eigenvalues seem to be sorted in increasing order ( Definition at line 49 of file plapack.cc. References PLearn::TMat< T >::data(), dsyev_(), endl(), PLearn::TMat< T >::isSymmetric(), lapack_Xsyevx_(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, PLWARNING, PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), ssyev_(), and PLearn::TMat< T >::width(). Referenced by affineNormalization(), diagonalizeSubspace(), eigen_SymmMat_decreasing(), and multivariate_normal().

int PLearn::eigen_SymmMat_decreasing (  Mat &  in, Vec &  e_value, Mat &  e_vector, int &  n_evalues_found, bool  compute_all, int  nb_eigen, bool  compute_vectors = true, bool  largest_evalues = true )

same as the previous call, but eigenvalues/vectors are sorted by largest firat (in decreasing order) Definition at line 207 of file plapack.cc. References eigen_SymmMat(), PLWARNING, PLearn::TVec< T >::swap(), and PLearn::TMat< T >::swapUpsideDown().

template<class MatT> int eigenSparseNonSymmMat (  MatT &  A, Vec  e_values, Mat  e_vectors, long int &  n_evalues, int  max_n_iter = 300, bool  compute_vectors = true, bool  largest_evalues = true, bool  according_to_magnitude = true, bool  both_ends = false )

Same arguments as eigenSparseSymmMat except that A is not symmetric. We ignore the imaginary part if there is one. See ARPACK/SRC files for more details. To get the eigen pairs in the same order as in plapack's eigenVecOfSymmMat, do the same thing as above, but you don't have to swap the eigen vectors and eigen values. < half of the e-values from each end of the spectrum < we need some extra space... Definition at line 185 of file parpack.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), dnaupd_(), dneupd_(), PLearn::TMat< T >::length(), MIN, PLERROR, PLWARNING, product(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), select(), snaupd_(), sneupd_(), PLearn::TVec< T >::subVec(), PLearn::TMat< T >::width(), and x.

template<class MatT> int eigenSparseSymmMat (  MatT &  A, Vec  e_values, Mat  e_vectors, long int &  n_evalues, int  max_n_iter = 300, bool  compute_vectors = true, bool  largest_evalues = true, bool  according_to_magnitude = true, bool  both_ends = false )

Compute some eigenvalues, and optionally eigenvectors, of a symmetric, possibly sparse, generalized matrix A. The only operation that will be performed on A (repetitively) is the matrix-vector product, i.e. A.product(Vec x, Vec y), yielding y = A x. This uses the ARPACK library. Returns 0 if all went well. Otherwise, see the INFO values set by [ds][eu]pd in the ARPACK/SRC files. It is possible that only a subset of the eigenvalues are found (as given by n_evalues upon return, and the new size of e_vectors/e_values). Note also that e_vectors might be internally and temporarily re-allocated to a larger size, with at most 1.5 times more rows. If you want the eigen values and eigen vectors to be returned in the same order as in plapack's eigenVecOfSymmMat, "according_to_magnitude" must be set to false and you must swap the eigen values and the eigen vectors. i.e. do something like: ............................................................. Mat evectors(nb_principal_components,train_set.length()); Vec evalues(nb_principal_components); int status; long int n_ev=nb_principal_components; status = eigenSparseSymmMat(A, evalues, evectors, n_ev, 300, true, true, false); if (status<0 || status>1) PLERROR("MyClass: eigenSparseSymmMat return error code number %d (see ARPACK dsaupd INFO variable)", status); if (status==1 || n_ev != nb_principal_components) PLERROR("MyClass: eigenSparseSymmMat computed only %d e-vectors rather than the required %d", n_ev, nb_principal_components); evalues.swap(); evectors.swapUpsideDown(); ............................................................. < half of the e-values from each end of the spectrum < we need some extra space... Definition at line 88 of file parpack.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), dsaupd_(), dseupd_(), PLearn::TMat< T >::length(), MIN, PLERROR, PLWARNING, product(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), select(), ssaupd_(), sseupd_(), PLearn::TVec< T >::subVec(), PLearn::TMat< T >::width(), and x.

template<class num_t> void eigenVecOfSymmMat (  TMat< num_t > &  m, int  k, TVec< num_t > &  eigen_values, TMat< num_t > &  eigen_vectors )

Computes up to k largest eigen_values and corresponding eigen_vectors of symmetric matrix m. Parameters eigen_values and eigen_vectors are resized accordingly and filled by the call. The eigenvalues are returned in decreasing order (largest first). The corresponding eigenvectors are in the *ROWS* of eigen_vectors WARNING: m is destroyed during the operation. Definition at line 322 of file plapack.h. References k, lapackEIGEN(), PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), PLearn::TVec< T >::swap(), PLearn::TMat< T >::swapUpsideDown(), and PLearn::TMat< T >::width(). Referenced by PLearn::GaussMix::computeMeansAndCovariances(), computePrincipalComponents(), PLearn::PCA::train(), PLearn::KernelProjection::train(), PLearn::GaussianProcessRegressor::train(), PLearn::GaussianDistribution::train(), and PLearn::GaussMix::updateFromConditionalSorting().

bool elementOf (  const char *  s, const char  t )

Definition at line 113 of file TypesNumeriques.cc. Referenced by compactRepresentationTranslate().

template<class T> void PLearn::elementsEqualTo (  const TVec< T > &  source, const T &  value, const TVec< T > &  destination )

put in destination 1's when (*this)[i]==value, 0 otherwise Definition at line 187 of file TMat_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

Var PLearn::ElogP (  ConditionalExpression  conditional_expression, RVInstanceArray &  parameters_to_learn, bool  clearMarksUponReturn = true )

This is like logP but it represents the expected log-probability of obs=conditional_expression.LHS.v given the RHS, where the expectation is over the "hidden" random variables of EM in mixtures, as a function of the values of the parameters_to_learn. Definition at line 642 of file RandomVar.cc. References logP(). Referenced by EM(), and PLearn::MixtureRandomVariable::logP().

real EM (  ConditionalExpression  conditional_expression, RVArray  parameters_to_learn, VMat  distr, int  n_samples, int  max_n_iterations, real  relative_improvement_threshold, bool  compute_final_train_NLL )

Definition at line 497 of file RandomVar.cc. References ElogP(), endl(), PLearn::RVInstanceArray::instances(), PLearn::ConditionalExpression::LHS, logP(), meanOf(), PLERROR, propagationPath(), PLearn::ConditionalExpression::RHS, PLearn::TVec< RandomVar >::size(), PLearn::RVInstance::v, PLearn::RVInstance::V, and PLearn::RVArray::values().

real PLearn::EM (  ConditionalExpression  conditional_expression, RVArray  parameters_to_learn, VMat  distr, int  n_samples, int  max_n_iterations = 1, real  relative_improvement_threshold = 0.001, bool  accept_worsening_likelihood = false, bool  compute_final_train_NLL = true )

NOTE NOTE NOTE: THE ORDER OF THE VALUES IN THE DISTRIBUTION MUST BE: (1) conditioning variables (RHS), (2) output variables Definition at line 460 of file RandomVar.cc. References PLearn::RVInstanceArray::instances(), PLearn::ConditionalExpression::LHS, logP(), propagationPath(), PLearn::ConditionalExpression::RHS, PLearn::RVInstance::v, PLearn::RVInstance::V, and PLearn::RVArray::values().

void endianswap (  double *  ptr, int  n = 1 )  [inline]

Definition at line 82 of file byte_order.h. References endianswap8().

void endianswap (  float *  ptr, int  n = 1 )  [inline]

Definition at line 81 of file byte_order.h. References endianswap4().

void endianswap (  unsigned long *  ptr, int  n = 1 )  [inline]

Definition at line 80 of file byte_order.h. References endianswap4().

void endianswap (  long *  ptr, int  n = 1 )  [inline]

Definition at line 79 of file byte_order.h. References endianswap4().

void endianswap (  unsigned int *  ptr, int  n = 1 )  [inline]

Definition at line 78 of file byte_order.h. References endianswap4().

void endianswap (  int *  ptr, int  n = 1 )  [inline]

Definition at line 77 of file byte_order.h. References endianswap4().

void endianswap (  unsigned short *  ptr, int  n = 1 )  [inline]

Definition at line 76 of file byte_order.h. References endianswap2().

void endianswap (  short *  ptr, int  n = 1 )  [inline]

Definition at line 75 of file byte_order.h. References endianswap2().

void endianswap (  unsigned char *  ptr, int  n = 1 )  [inline]

Definition at line 72 of file byte_order.h.

void endianswap (  signed char *  ptr, int  n = 1 )  [inline]

Definition at line 71 of file byte_order.h.

void endianswap (  char *  ptr, int  n = 1 )  [inline]

Definition at line 70 of file byte_order.h. Referenced by PLearn::IntVecFile::append(), PLearn::DiskVMatrix::appendRow(), binread_(), PLearn::DiskVMatrix::build_(), PLearn::DiskVMatrix::getNewRow(), PLearn::IntVecFile::getVec(), new_read_compressed(), PLearn::PStream::operator>>(), PLearn::TMat< pair< real, real > >::read(), readSequence(), reverse_double(), reverse_float(), reverse_int(), reverse_short(), reverse_uint(), and reverse_ushort().

void PLearn::endianswap2 (  void *  ptr, int  n )

swaps endians for n 2-byte elements (such as short) Definition at line 43 of file byte_order.cc. Referenced by endianswap().

void PLearn::endianswap4 (  void *  ptr, int  n )

swaps endians for n 4-byte elements (such as int or float) Definition at line 54 of file byte_order.cc. Referenced by endianswap().

void PLearn::endianswap8 (  void *  ptr, int  n )

swaps endians for n 8-byte elements (such as double) Definition at line 66 of file byte_order.cc. Referenced by endianswap().

PStream & PLearn::endl (  PStream &  out  )

Definition at line 58 of file PStream.cc. References PLearn::PStream::endl(). Referenced by PLearn::CompactVMatrix::append(), PLearn::AsciiVMatrix::appendRow(), PLearn::AsciiVMatrix::AsciiVMatrix(), PLearn::PLearnCommandRegistry::badcommand(), PLearn::GraphicalBiText::build_(), PLearn::GramVMatrix::build_(), PLearn::DatedJoinVMatrix::build_(), PLearn::AddCostToLearner::build_(), PLearn::GhostScript::centerShow(), PLearn::GraphicalBiText::check_consitency(), PLearn::CompactVMatrix::CompactVMatrix(), PLearn::VMatLanguage::compileStream(), compressedTransposeProductAcc(), PLearn::GraphicalBiText::compute_likelihood(), PLearn::GraphicalBiText::compute_nodemap(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), computeConditionalStats(), PLearn::Learner::computeCosts(), PLearn::KernelProjection::computeCostsFromOutputs(), PLearn::LiftStatsCollector::computeLift(), PLearn::SDBWithStats::computeStats(), PLearn::ConjGradientOptimizer::ConjGradientOptimizer(), PLearn::SDBVMFieldICBCTargets::convertField(), PLearn::GhostScript::copypage(), PLearn::CountEventsSemaphore::CountEventsSemaphore(), PLearn::VVMatrix::createPreproVMat(), cross_valid(), diagonalizeSubspace(), PLearn::Function::differentiate(), displayBasicStats(), PLearn::GhostScript::displayBlack(), PLearn::GhostScript::displayGray(), displayObjectHelp(), PLearn::ShellProgressBar::done(), PLearn::GhostScript::drawBox(), PLearn::GhostScript::drawCircle(), PLearn::GhostScript::drawLine(), DX_create_dataset_outputs_file(), DX_create_grid_outputs_file(), eigen_SymmMat(), EM(), PLearn::RandomVariable::EM(), PLearn::MixtureRandomVariable::EMBprop(), PLearn::RandomVariable::epoch(), PLearn::GhostScript::erasepage(), errormsg(), PLearn::GaussianKernel::evaluate_i_j(), exitmsg(), PLearn::Gnuplot::export_ps(), PLearn::RowMapSparseMatrix< real >::exportToMatlabReadableFormat(), PLearn::WordNetOntology::extractAncestors(), PLearn::WordNetOntology::extractDescendants(), extractFiles(), PLearn::WordNetOntology::extractTaggedWordFrequencies(), PLearn::Gnuplot::featureplot(), PLearn::GhostScript::fillBox(), PLearn::GhostScript::fillCircle(), PLearn::LiftStatsCollector::finalize(), PLearn::ConjGradientOptimizer::findDirection(), PLearn::SimpleDB< KeyType, QueryResult >::findEqualLinear(), PLearn::ConjGradientOptimizer::fletcherSearchMain(), PLearn::ProjectionErrorVariable::fprop(), PLearn::LogVariable::fprop(), PLearn::ProbabilitySparseMatrix::fullPrint(), PLearn::VVMatrix::generateVMatIndex(), PLearn::GaussianContinuum::get_image_matrix(), PLearn::Dictionary::getId(), PLearn::GhostScript::GhostScript(), PLearn::Gnuplot::Gnuplot(), halfShuffleRows(), PLearn::PLearnCommandRegistry::help(), PLearn::HelpCommand::helpAboutScript(), PLearn::HelpCommand::helpCommands(), PLearn::HelpCommand::helpDatasets(), PLearn::HelpCommand::helpOverview(), PLearn::HelpCommand::helpScripts(), PLearn::Gnuplot::histoplot(), PLearn::SimpleDB< KeyType, QueryResult >::indexColumn(), PLearn::GraphicalBiText::init(), interactiveDisplayCDF(), lapackSVD(), PLearn::Popen::launch(), PLearn::MatlabInterface::launch(), PLearn::IPopen::launch(), PLearn::MatlabInterface::launchAndWaitFor(), PLearn::ConjGradientOptimizer::lineSearch(), PLearn::RGBImage::loadJPEG(), PLearn::SimpleDB< KeyType, QueryResult >::loadSchema(), loadToVMat(), PLearn::VMatrix::lockMetaDataDir(), main(), matInvert(), matlabSave(), PLearn::VarMeasurer::measure(), PLearn::Learner::measure(), PLearn::Gnuplot::multiplot(), PLearn::ConjGradientOptimizer::newtonSearch(), old_plearn_main(), PLearn::Learner::openTestResultsStreams(), PLearn::Learner::openTrainObjectiveStream(), operator<<(), PLearn::ScaledGradientOptimizer::optimize(), PLearn::GradientOptimizer::optimize(), PLearn::ConjGradientOptimizer::optimize(), PLearn::ConjGradientOptimizer::optimizeN(), PLearn::AdaptGradientOptimizer::optimizeN(), PLearn::Learner::outputResultLineToFile(), plearn_main(), PLearn::Gnuplot::plot(), PLearn::Gnuplot::plot3d(), PLearn::Grapher::plot_1D_regression(), PLearn::Grapher::plot_2D_classification(), PLearn::Gnuplot::plotcdf(), PLearn::Gnuplot::plotClasses(), PLearn::Gnuplot::plotdensity(), plotVMats(), prettyprint_test_results(), PLearn::WordNetOntology::print(), PLearn::VMatrix::print(), PLearn::Variable::print(), PLearn::StatsCollector::print(), PLearn::RealMapping::print(), print(), PLearn::Object::print(), PLearn::GraphicalBiText::print(), PLearn::Array< char * >::print(), PLearn::PLearnCommandRegistry::print_command_summary(), print_diff(), PLearn::GraphicalBiText::print_sensemap(), printDistanceStatistics(), PLearn::VMatrix::printFieldInfo(), printFieldNames(), PLearn::VMatrix::printFields(), PLearn::UnfoldedSumOfVariable::printInfo(), PLearn::UnfoldedFuncVariable::printInfo(), PLearn::UnaryVariable::printInfo(), PLearn::SumOverBagsVariable::printInfo(), PLearn::SumOfVariable::printInfo(), PLearn::SourceVariable::printInfo(), PLearn::NaryVariable::printInfo(), PLearn::MatrixSumOfVariable::printInfo(), PLearn::BinaryVariable::printInfo(), PLearn::TVec< pair< real, real > >::println(), PLearn::VarArray::printNames(), PLearn::GraphicalBiText::printNode(), PLearn::WordNetOntology::printNodes(), PLearn::WordNetOntology::printStats(), PLearn::ConjGradientOptimizer::printStep(), PLearn::WordNetOntology::printSynset(), PLearn::WordNetOntology::printSynsetAncestors(), PLearn::WordNetOntology::printWordAncestors(), PLearn::WordNetOntology::printWordOntology(), randomShuffleRows(), readAndMacroProcess(), reduceInputSize(), PLearn::WordNetOntology::reduceWordPolysemy_preserveSenseOverlapping(), PLearn::Profiler::report(), PLearn::Hash< KeyType, DataType >::resize(), PLearn::ResourceSemaphore::ResourceSemaphore(), PLearn::Train::run(), PLearn::TestDependencyCommand::run(), PLearn::TestDependenciesCommand::run(), PLearn::SequentialValidation::run(), PLearn::NearestNeighborPredictionCost::run(), PLearn::KolmogorovSmirnovCommand::run(), PLearn::JulianDateCommand::run(), PLearn::Grapher::run(), PLearn::GhostScript::run(), PLearn::GenerateDecisionPlot::run(), PLearn::AutoRunCommand::run(), PLearn::WordNetOntology::save(), PLearn::VMatrix::saveAMAT(), saveAscii(), PLearn::VMatrix::saveFieldInfos(), PLearn::RowMapSparseMatrix< real >::saveNonZeroElements(), PLearn::RGBImage::savePPM(), PLearn::WordNetOntology::savePredominentSyntacticClasses(), PLearn::SimpleDB< KeyType, QueryResult >::saveSchema(), PLearn::VMatrix::saveStats(), PLearn::SDBWithStats::saveStats(), PLearn::VMatrix::saveStringMappings(), PLearn::WordNetOntology::saveVocInWordnet(), PLearn::GraphicalBiText::sensetag_valid_bitext(), PLearn::GraphicalBiText::senseTagBitext(), PLearn::GraphicalBiText::set_nodemap(), PLearn::Gnuplot::seteps(), PLearn::VMatrix::setSFIFFilename(), PLearn::Gnuplot::setxrange(), PLearn::Gnuplot::setyrange(), PLearn::GhostScript::show(), PLearn::GhostScript::showpage(), PLearn::ScaledConditionalCDFSmoother::smooth(), PLearn::ProbVector::smoothNormalize(), SpearmanRankCorrelation(), split(), PLearn::Learner::stop_if_wanted(), PLearn::SequentialModelSelector::test(), PLearn::MovingAverage::test(), PLearn::GraphicalBiText::test_WSD(), PLearn::TangentLearner::train(), PLearn::SequentialModelSelector::train(), PLearn::PLS::train(), PLearn::NNet::train(), PLearn::NeighborhoodSmoothnessNNet::train(), PLearn::MultiInstanceNNet::train(), PLearn::MovingAverage::train(), PLearn::KernelProjection::train(), PLearn::GaussianContinuum::train(), PLearn::EntropyContrast::train(), PLearn::ConditionalDensityNet::train(), PLearn::ClassifierFromDensity::train(), PLearn::AdaBoost::train(), train_and_test(), PLearn::RowMapSparseMatrix< real >::transposeProduct(), PLearn::TextProgressBarPlugin::update(), PLearn::GraphicalBiText::update_WSD_model(), usage(), use(), PLearn::GhostScript::usefont(), PLearn::Function::verifyGradient(), PLearn::Function::verifyHessian(), PLearn::Function::verifyrfprop(), PLearn::Function::verifySymbolicGradient(), viewVMat(), vmatmain(), warningmsg(), PLearn::CountEventsSemaphore::~CountEventsSemaphore(), PLearn::GhostScript::~GhostScript(), PLearn::Gnuplot::~Gnuplot(), PLearn::ResourceSemaphore::~ResourceSemaphore(), and PLearn::SharedMemory< T >::~SharedMemory().

Var PLearn::entropy (  Var  v, bool  normalize = true )

Definition at line 91 of file Var_utils.cc. References abs(), log(), normalize(), plogp(), and sum().

const char * PLearn::eNumericTypeNames (  int  a  )

converts a code in corresponding string Definition at line 55 of file TypesNumeriques.cc. References NT_CARDINAL, NT_CODE, NT_CURRENCY, NT_NOT_NUMERIC, NT_ORDINAL, NT_PERCENT, NT_PREFIXED, NT_RANGE, NT_SUFFIXED, NT_TIME, and NT_UNKNOWN_NUMERIC_TYPE.

template<class T> void equals (  const TMat< T > &  src, T  v, TMat< T > &  dest )

Definition at line 5277 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void equals (  const TVec< T > &  src, T  v, TVec< T > &  dest )

Definition at line 1695 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

Var erf (  Var  v  )  [inline]

Definition at line 71 of file ErfVariable.h.

void PLearn::errormsg (  const char *  msg,   ... )

Definition at line 64 of file plerror.cc. References endl(), ERROR_MSG_SIZE, and error_stream.

int establish_connection (  const int  argc, const char *  argv[] )  [inline]

Definition at line 145 of file IPopen.h. References establish_connection(), and PLERROR.

int establish_connection (  const char *  hostname, int  port_no )  [inline]

Definition at line 143 of file IPopen.h. References establish_connection(), and hostname().

int PLearn::establish_connection (  int  n_hosts, const char *  hostnames[], int  port_no )

Definition at line 99 of file IPopen.cc. References PLERROR. Referenced by establish_connection().

template<class T> T estimatedCumProb (  T  x, TVec< T >  bins )

Definition at line 1812 of file TMat_maths_impl.h. References binary_search(), k, PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::UniformizeVMatrix::getNewRow().

vector< string > PLearn::execute (  const string &  command  )

Returns the full output of the command as a vector of strings, containing the lines of the answer (with any newline character removed). The command must not be waiting for input on its standard input or this call will never return. Definition at line 171 of file Popen.cc. References PLearn::PStream::getline(), and PLearn::Popen::in. Referenced by PLearn::ShellProgressBar::getWcAsciiFileLineCount().

void PLearn::exitmsg (  const char *  msg,   ... )

Definition at line 109 of file plerror.cc. References endl(), ERROR_MSG_SIZE, and error_stream. Referenced by cross_valid(), getMultipleModelAliases(), old_plearn_main(), train_and_test(), and use().

Var exp (  Var  v  )  [inline]

Definition at line 73 of file ExpVariable.h.

RandomVar PLearn::exp (  RandomVar  x  )

exponential function applied element-by-element Definition at line 448 of file RandomVar.cc. References x.

template<class T> TVec<T> exp (  TVec< T >  vec  )

Definition at line 1639 of file TMat_maths_impl.h. References apply(), and safeexp().

template<class T> void exp (  const TVec< T > &  x, TVec< T > &  y )

computes y <- exp(x) Definition at line 107 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::resize(), and x. Referenced by bnldev(), PLearn::NllSemisphericalGaussianVariable::bprop(), PLearn::ErfVariable::bprop(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), PLearn::ClassifierFromDensity::computeOutput(), PLearn::GaussMix::computePosteriors(), PLearn::PDistribution::density(), PLearn::Distribution::density(), PLearn::ConditionalGaussianDistribution::density(), determine_density_integral_from_log_densities_on_grid(), DX_create_grid_outputs_file(), PLearn::MixtureRandomVariable::ElogP(), PLearn::MixtureRandomVariable::EMBprop(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::LogRandomVariable::EMBprop(), PLearn::SigmoidPrimitiveKernel::evaluate(), PLearn::ScaledLaplacianKernel::evaluate(), PLearn::ScaledGeneralizedDistanceRBFKernel::evaluate(), PLearn::ScaledGaussianKernel::evaluate(), PLearn::LaplacianKernel::evaluate(), PLearn::GeneralizedDistanceRBFKernel::evaluate(), PLearn::GaussianDensityKernel::evaluate(), PLearn::ConvexBasisKernel::evaluate(), PLearn::CompactVMatrixGaussianKernel::evaluate(), PLearn::GaussianKernel::evaluateFromSquaredNormOfDifference(), PLearn::ExpMeanStatsIterator::finish(), gamdev(), gauss_01_density(), gauss_density_var(), geometric_mean(), PLearn::GaussianContinuum::get_image_matrix(), incomplete_beta(), PLearn::ConditionalDensityNet::initialize_mu(), inverse_softplus(), PLearn::LogRandomVariable::invertible(), KS_test(), logadd(), PLearn::DiagonalNormalRandomVariable::logP(), logsub(), PLearn::RandomVariable::P(), pl_gcf(), pl_gser(), poidev(), PLearn::VMatLanguage::run(), safeexp(), PLearn::GaussMix::setInput(), PLearn::DiagonalNormalRandomVariable::setValueFromParentsValue(), PLearn::ExpRandomVariable::setValueFromParentsValue(), softmax(), softplus(), softplus_primitive(), PLearn::SoftmaxLossVariable::symbolicBprop(), PLearn::LogAddVariable::symbolicBprop(), PLearn::ErfVariable::symbolicBprop(), PLearn::GaussianContinuum::train(), and PLearn::AdaBoost::train().

StatsIt exp_mean_stats (   )  [inline]

Definition at line 419 of file StatsIterator.h.

real PLearn::expdev (   )

returns an exponential distributed random number Definition at line 318 of file random.cc. References log(), and uniform_sample().

template<class T> void exponentialMovingAverageUpdate (  const TVec< T > &  vec, const TVec< T > &  x, T  alpha )

TVec[i] = (1-alpha)*TVec[i]+x[i]*alpha;. Definition at line 2056 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::ScaledGradientOptimizer::optimize().

template<class T> void exponentialMovingSquareUpdate (  const TVec< T > &  vec, const TVec< T > &  x, T  alpha )

TVec[i] = (1-alpha)*TVec[i]+x[i]^2*alpha;. Definition at line 2092 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::ScaledGradientOptimizer::optimize().

template<class T> void exponentialMovingVarianceUpdate (  const TVec< T > &  vec, const TVec< T > &  x, const TVec< T > &  mu, T  alpha )

TVec[i] = (1-alpha)*TVec[i]+(x[i]-mu[i])^2*alpha;. Definition at line 2071 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x.

Var extend (  Var  v, real  extension_value = 1.0, int  n_extend = 1 )  [inline]

simple extension of a vector (same semantic as old extend, when we only had vectors) Definition at line 104 of file ExtendedVariable.h. References PLERROR.

Var extend (  Var  v, int  top_extent, int  bottom_extent, int  left_extent, int  right_extent, real  fill_value = 0.0 )  [inline]

general extension of a matrix in any direction Definition at line 100 of file ExtendedVariable.h.

RandomVar PLearn::extend (  RandomVar  v, real  extension_value = 1.0, int  n_extend = 1 )

Definition at line 453 of file RandomVar.cc. Referenced by PLearn::ExtendedRandomVariable::setValueFromParentsValue(), PLearn::SubMatVariable::symbolicBprop(), and PLearn::SubMatTransposeVariable::symbolicBprop().

template<class T> void externalProduct (  const TMat< T > &  mat, const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 2979 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::VecStatsCollector::getCorrelation().

template<class T> void externalProductAcc (  const TMat< T > &  mat, const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 2999 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), PLearn::TMat< T >::isCompact(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, val, and PLearn::TMat< T >::width(). Referenced by PLearn::VMatrix::accumulateXtX(), PLearn::VMatrix::accumulateXtY(), PLearn::AffineTransformVariable::bprop(), computeMeanAndCovar(), and linearRegression().

template<class T> void externalProductScaleAcc (  const TMat< T > &  mat, const TVec< T > &  v1, const TVec< T > &  v2, T  gamma, T  alpha )

Definition at line 3059 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void externalProductScaleAcc (  const TMat< T > &  mat, const TVec< T > &  v1, const TVec< T > &  v2, T  gamma )

Definition at line 3039 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProjectionErrorVariable::bprop(), computeCovar(), computeInputMeanAndCovar(), computeMeanAndCovar(), computeWeightedMeanAndCovar(), PLearn::VecStatsCollector::update(), and weightedLinearRegression().

string PLearn::extract_directory (  const string &  filepath  )

Returns everything before the last '/' including the '/' (if there's no '/' it returns "./"). Definition at line 146 of file stringutils.cc. References dot(), and slash. Referenced by abspath(), PLearn::AutoSDBVMatrix::AutoSDBVMatrix(), PLearn::VVMatrix::createPreproVMat(), PLearn::VVMatrix::extractSourceMatrix(), force_mkdir_for_file(), getDataSet(), PLearn::VVMatrix::getDateOfVMat(), locateDatasetAliasesDir(), makeFileNameValid(), readAndMacroProcess(), and readFileAndMacroProcess().

string PLearn::extract_extension (  const string &  filepath  )

Returns everything after the last '.' of the filename (i.e. excluding the directory paths if any is present), including the '.' (if there's no '.' in the filename it returns "") Definition at line 158 of file stringutils.cc. References extract_filename(). Referenced by getDataSet(), getDataSetDate(), loadMat(), makeFileNameValid(), readFileAndMacroProcess(), PLearn::ReadAndWriteCommand::run(), PLearn::HelpCommand::run(), and vmatmain().

string PLearn::extract_filename (  const string &  filepath  )

** File path manipulation functions ** Returns everything after the last '/' (if there's no '/' returns filepath) Definition at line 137 of file stringutils.cc. References slash. Referenced by abspath(), PLearn::AutoSDBVMatrix::AutoSDBVMatrix(), extract_extension(), extract_filename_without_extension(), getDataSet(), matlabR11eigs(), readAndMacroProcess(), and readFileAndMacroProcess().

string PLearn::extract_filename_without_extension (  const string &  filepath  )

Returns everything before the last '.' of the filename, excluding the '.' (if there's no '.' in the filename it returns the whole filename). Definition at line 168 of file stringutils.cc. References extract_filename(). Referenced by makeFileNameValid().

real FABS (  real  x  )  [inline]

Definition at line 250 of file pl_math.h. References x. Referenced by PLearn::LiftStatsIterator::update(), PLearn::LiftStatsCollector::update(), and PLearn::Function::verifyGradient().

template<class T> TVec<T> fastsigmoid (  const TVec< T > &  src  )  [inline]

Definition at line 1014 of file TMat_maths_impl.h. References compute_fastsigmoid(), and PLearn::TVec< T >::length().

real fastsigmoid (  const real &  x  )  [inline]

Definition at line 193 of file pl_math.h. References fasttanh(), and x. Referenced by compute_fastsigmoid().

template<class T> TVec<T> fasttanh (  const TVec< T > &  src  )  [inline]

Definition at line 977 of file TMat_maths_impl.h. References compute_fasttanh(), and PLearn::TVec< T >::length().

real fasttanh (  const real &  x  )  [inline]

Definition at line 166 of file pl_math.h. References DOUBLE_TO_INT, MAXTANHX, tanhtable, TANHTABLESIZE, and x. Referenced by compute_fasttanh(), and fastsigmoid().

bool PLearn::file_exists (  const string &  filename  )

Definition at line 114 of file general.cc. Referenced by PLearn::VVMatrix::extractSourceMatrix(), filter(), fullyRebalance2Classes(), grep(), PLearn::SDBWithStats::hasStats(), PLearn::HelpCommand::helpAboutScript(), input2dSet(), PLearn::IntVecFile::open(), PLearn::FilteredVMatrix::openIndex(), plearn_main(), PLearn::VMat::precompute(), rebalanceNClasses(), PLearn::RunCommand::run(), PLearn::SourceVMatrix::setMetaDataDir(), and PLearn::Learner::stop_if_wanted().

int PLearn::file_size (  const string &  filename  )

Simple file info. Definition at line 71 of file general.cc. Referenced by PLearn::SimpleDB< KeyType, QueryResult >::computeSize(), and filter().

long PLearn::filesize (  const string &  filename  )

Returns the length of a file, measured in bytes. Definition at line 323 of file fileutils.cc. References PLERROR. Referenced by PLearn::IntVecFile::getVersionAndSize(), loadFileAsString(), MemoryMap(), and PLearn::Storage< pair< real, real > >::Storage().

template<class T> void fill_one_hot (  const TVec< T > &  vec, int  hotpos, T  coldvalue, T  hotvalue )

Definition at line 1308 of file TMat_maths_impl.h. References PLearn::TVec< T >::fill(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::OneHotVMatrix::getNewRow(), PLearn::GeneralizedOneHotVMatrix::getNewRow(), and one_hot().

void PLearn::fill_random_discrete (  const Vec &  dest, const Vec &  set )

sample each element from the given set Definition at line 570 of file random.cc. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::end(), PLearn::TVec< T >::length(), and uniform_multinomial_sample(). Referenced by PLearn::NNet::initializeParams().

void PLearn::fill_random_normal (  const Mat &  dest, real  mean = 0, real  sdev = 1 )

Definition at line 613 of file random.cc. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::end(), gaussian_mu_sigma(), and mean().

void PLearn::fill_random_normal (  const Vec &  dest, const Vec &  mean, const Vec &  stdev )

sample each element from multivariate Normal(mean,diag(sdev^2)) distribution Definition at line 589 of file random.cc. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::end(), gaussian_mu_sigma(), PLearn::TVec< T >::length(), mean(), and PLERROR.

void PLearn::fill_random_normal (  const Vec &  dest, real  mean = 0, real  stdev = 1 )

sample each element from Normal(mean,sdev^2) distribution Definition at line 580 of file random.cc. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::end(), gaussian_mu_sigma(), and mean(). Referenced by PLearn::GaussianDistribution::generate(), PLearn::GaussMix::generateFromGaussian(), PLearn::NeuralNet::initializeParams(), PLearn::NeighborhoodSmoothnessNNet::initializeParams(), PLearn::MultiInstanceNNet::initializeParams(), PLearn::ConditionalDensityNet::initializeParams(), and PLearn::PCA::train().

void PLearn::fill_random_uniform (  const Mat &  dest, real  minval = 0, real  maxval = 1 )

Definition at line 604 of file random.cc. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::end(), and uniform_sample().

void PLearn::fill_random_uniform (  const Vec &  dest, real  minval = 0, real  maxval = 1 )

sample each element from uniform distribution U[minval,maxval] Definition at line 560 of file random.cc. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::end(), and uniform_sample(). Referenced by affineMatrixInitialize(), PLearn::EntropyContrast::initialize_NNcontinuous(), PLearn::TangentLearner::initializeParams(), PLearn::NNet::initializeParams(), PLearn::GaussianContinuum::initializeParams(), and PLearn::Function::verifyGradient().

template<class T> void fillItSymmetric (  const TMat< T > &  mat  )

Fill the bottom left part of a matrix with its top right part, so that it becomes symmetric. Definition at line 2805 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and PLearn::TMat< T >::mod().

VMat PLearn::filter (  VMat  d, const string &  indexfile )

returns a VMat that contains only the lines that do not have any MISSING_VALUE The indexes of the rows of the original matrix are recorded in the indexfile BEWARE: If the indexfile already exists, it is *not* recomputed, but used as is. Definition at line 758 of file VMat_maths.cc. References PLearn::IntVecFile::append(), file_exists(), file_size(), PLearn::TVec< T >::hasMissing(), PLearn::VMat::length(), PLearn::VMat::rows(), and PLearn::VMat::width(). Referenced by PLearn::EmbeddedSequentialLearner::train().

bool PLearn::find (  const vector< string > &  command_line, const string &  option )

also useful to find "turn-on" options in a command line, i.e. of the form option this function just returns true if the option is found in the command_line. Note: this may be useful with command_line = stringvector(argc,argv); Definition at line 586 of file stringutils.cc. Referenced by PLearn::Hash< KeyType, DataType >::del(), PLearn::Hash< KeyType, DataType >::element(), PLearn::Hash< KeyType, DataType >::hashAddress(), PLearn::VMatrix::removeStringMapping(), and PLearn::SimpleDB< KeyType, QueryResult >::truncateFromRow().

Mat PLearn::findClosestPairsOfDifferentClass (  int  k, VMat  data, Ker  dist )

Definition at line 687 of file Kernel.cc. References argmax(), dist(), k, sortRows(), and PLearn::TVec< real >::subVec().

int findit (  const vector< string > &  v, string  element )

Definition at line 525 of file stringutils.cc.

int findpos (  const vector< string > &  v, string  element )

return index of element in v, or -1 if not found

PDate float_to_date (  double  d  )  [inline]

Definition at line 170 of file PDate.h. References float_to_date().

PDate PLearn::float_to_date (  float  f  )

Definition at line 170 of file PDate.cc. References PLearn::PDate::day, is_missing(), PLearn::PDate::month, and PLearn::PDate::year. Referenced by PLearn::DatedJoinVMatrix::build_(), PLearn::SDBVMFieldMonths::convertField(), PLearn::SDBVMFieldDay::convertField(), PLearn::SDBVMFieldDate::convertField(), float_to_date(), and PLearn::VMatLanguage::run().

PStream & PLearn::flush (  PStream &  out  )

Definition at line 52 of file PStream.cc. References PLearn::PStream::flush(). Referenced by PLearn::Learner::computeLeaveOneOutCosts(), PLearn::VVec::print(), PLearn::KernelProjection::train(), vmatmain(), and PLearn::pl_fdstream::~pl_fdstream().

bool PLearn::force_mkdir (  const string &  dirname  )

Forces directory creation if it doesn't already exist. (also creates any missing directory along its path) Return value indicates success (true) or failure (false). If the directory already exists, true is returned. Definition at line 252 of file fileutils.cc. References isdir(), mode, remove_trailing_slash(), and slash. Referenced by PLearn::AutoSDBVMatrix::AutoSDBVMatrix(), PLearn::VVMatrix::build_(), PLearn::SequentialValidation::build_(), PLearn::PLearner::build_(), PLearn::PTester::build_(), PLearn::GaussianProcessRegressor::build_(), PLearn::DiskVMatrix::build_(), PLearn::VVMatrix::createPreproVMat(), force_mkdir_for_file(), PLearn::VMatrix::loadStringMapping(), PLearn::VMatrix::lockMetaDataDir(), matlabSave(), PLearn::SequentialValidation::run(), PLearn::Experiment::run(), PLearn::PLearner::setExperimentDirectory(), PLearn::PTester::setExperimentDirectory(), PLearn::Learner::setExperimentDirectory(), and PLearn::VMatrix::setMetaDataDir().

void PLearn::force_mkdir_for_file (  const string &  filepath  )

Extracts the directory part of the filepath and calls force_mkdir Calls PLERROR in case of failure. Definition at line 286 of file fileutils.cc. References extract_directory(), force_mkdir(), and PLERROR. Referenced by PLearn::FileVMatrix::build_(), save(), saveStringInFile(), PLearn::VMatrix::saveStringMappings(), and PLearn::VMatrix::setSFIFFilename().

bool PLearn::force_rmdir (  const string &  dirname  )

Forces removal of directory and all its content Return value indicates success (true) or failure (false) If the directory does not exist, false is returned. Definition at line 296 of file fileutils.cc. References isdir(), and lsdir_fullpath(). Referenced by PLearn::VVMatrix::createPreproVMat(), PLearn::VMatrix::saveDMAT(), and PLearn::PrecomputedVMatrix::usePrecomputed().

double fread_double (  FILE *  f, bool  is_file_bigendian = true )  [inline]

Definition at line 101 of file pl_io_deprecated.h. References fread_double().

void PLearn::fread_double (  FILE *  f, float *  ptr, int  n, bool  is_file_bigendian = true )

reads disk doubles into float array Definition at line 268 of file pl_io_deprecated.cc. References fread_double().

void PLearn::fread_double (  FILE *  f, double *  ptr, int  n, bool  is_file_bigendian = true )

Definition at line 255 of file pl_io_deprecated.cc. References reverse_double(). Referenced by fread_double(), PLearn::FileVMatrix::getNewRow(), loadPMat(), and loadPVec().

float fread_float (  FILE *  f, bool  is_file_bigendian = true )  [inline]

Definition at line 99 of file pl_io_deprecated.h. References fread_float().

void PLearn::fread_float (  FILE *  f, double *  ptr, int  n, bool  is_file_bigendian = true )

reads disk floats into double array Definition at line 246 of file pl_io_deprecated.cc. References fread_float().

void PLearn::fread_float (  FILE *  f, float *  ptr, int  n, bool  is_file_bigendian = true )

Definition at line 233 of file pl_io_deprecated.cc. References reverse_float(). Referenced by fread_float(), PLearn::FileVMatrix::getNewRow(), loadADMat(), loadADVec(), loadPMat(), loadPVec(), loadSNMat(), and loadSNVec().

int fread_int (  FILE *  f, bool  is_file_bigendian = true )  [inline]

The following calls read a single value from the file, assuming it is in the specified representation (either little or big endian) If necessary the representation is translated to the endianness used on the current architecture. Definition at line 97 of file pl_io_deprecated.h. References fread_int().

void PLearn::fread_int (  FILE *  f, int *  ptr, int  n, bool  is_file_bigendian = true )

Reads binary data from a file assuming it is in the specified representation (either little or big endian) If necessary the representation is translated to the endianness on the current architecture. Definition at line 220 of file pl_io_deprecated.cc. References reverse_int(). Referenced by fread_int(), PLearn::IntVecFile::get(), loadADMat(), loadADVec(), loadSNMat(), and loadSNVec().

void PLearn::fread_short (  FILE *  f, unsigned short *  ptr, int  n, bool  is_file_bigendian = true )

Definition at line 277 of file pl_io_deprecated.cc. References reverse_ushort().

void PLearn::fullyRebalance2Classes (  VMat  inputs, const string &  filename, bool  save_indices = true )

Rebalance a 2-class VMat such as to keep all the examples of the dominant class. Definition at line 1153 of file VMat_maths.cc. References PLearn::TmpFilenames::addFilename(), file_exists(), fname, PLearn::VMat::lastColumn(), PLearn::VMat::length(), MAX, PLearn::IntVecFile::put(), PLearn::TVec< T >::resize(), PLearn::VMat::rows(), PLearn::VMat::save(), PLearn::VMat::toMat(), and PLearn::TMat< T >::toVecCopy().

void fwrite_double (  FILE *  f, double  value, bool  is_file_bigendian = true )  [inline]

Definition at line 110 of file pl_io_deprecated.h. References fwrite_double().

void PLearn::fwrite_double (  FILE *  f, const float *  ptr, int  n, bool  is_file_bigendian = true )

writes float array to double file Definition at line 209 of file pl_io_deprecated.cc. References fwrite_double().

void PLearn::fwrite_double (  FILE *  f, const double *  ptr, int  n, bool  is_file_bigendian = true )

Definition at line 185 of file pl_io_deprecated.cc. References reverse_double(). Referenced by PLearn::FileVMatrix::appendRow(), fwrite_double(), PLearn::FileVMatrix::put(), and PLearn::FileVMatrix::putSubRow().

void fwrite_float (  FILE *  f, float  value, bool  is_file_bigendian = true )  [inline]

Definition at line 108 of file pl_io_deprecated.h. References fwrite_float().

void PLearn::fwrite_float (  FILE *  f, const double *  ptr, int  n, bool  is_file_bigendian = true )

writes double array to float file Definition at line 176 of file pl_io_deprecated.cc. References fwrite_float().

void PLearn::fwrite_float (  FILE *  f, const float *  ptr, int  n, bool  is_file_bigendian = true )

Definition at line 152 of file pl_io_deprecated.cc. References reverse_float(). Referenced by PLearn::FileVMatrix::appendRow(), fwrite_float(), PLearn::FileVMatrix::put(), PLearn::FileVMatrix::putSubRow(), saveSNMat(), and saveSNVec().

void fwrite_int (  FILE *  f, int  value, bool  is_file_bigendian = true )  [inline]

The following calls write a single value to the file in the specified representation, regardeless of the endianness on the current architecture. Definition at line 106 of file pl_io_deprecated.h. References fwrite_int().

void PLearn::fwrite_int (  FILE *  f, const int *  ptr, int  n, bool  is_file_bigendian = true )

Writes binary data to the file in the specified representation (little or big endian) regardeless of the endianness used on the current architecture. Definition at line 128 of file pl_io_deprecated.cc. References reverse_int(). Referenced by fwrite_int(), PLearn::IntVecFile::put(), saveSNMat(), and saveSNVec().

real PLearn::gamdev (  int  ia  )

returns a gamma distributed random number Definition at line 393 of file random.cc. References exp(), log(), PLERROR, sqrt(), uniform_sample(), and x.

real PLearn::gauss_01_cum (  real  x  )

For X ~ Normal(0,1), cumulative probability function P(X<x). Definition at line 158 of file pl_erf.cc. References pl_erf(), and x. Referenced by gauss_01_quantile(), gauss_cum(), normal_cdf(), p_value(), and testNoCorrelationAsymptotically().

real PLearn::gauss_01_density (  real  x  )

for X ~ Normal(0,1), return density of X at x Definition at line 191 of file pl_erf.cc. References exp(), Sqrt2Pi, and x. Referenced by gauss_density_stddev().

real PLearn::gauss_01_log_density (  real  x  )

Definition at line 196 of file pl_erf.cc. References Log2Pi, and x.

real PLearn::gauss_01_quantile (  real  q  )

For X ~ Normal(0,1), inverse of cumulative probability function P(X<x) i.e. approximately gauss_01_quantile(gauss_01_cum(x)) ~=~ x (the inverse is computed with a binary search, the bisection method) Definition at line 165 of file pl_erf.cc. References gauss_01_cum().

real gauss_cum (  real  x, real  mu, real  sigma )  [inline]

Definition at line 74 of file pl_erf.h. References gauss_01_cum(), and x. Referenced by PLearn::LimitedGaussianSmoother::smooth().

real gauss_density_stddev (  real  x, real  mu, real  sigma )  [inline]

Definition at line 91 of file pl_erf.h. References gauss_01_density(), and x.

real PLearn::gauss_density_var (  real  x, real  mu, real  var )

Definition at line 208 of file pl_erf.cc. References exp(), Sqrt2Pi, var(), and x.

real PLearn::gauss_log_density_stddev (  real  x, real  mu, real  sigma )

Definition at line 213 of file pl_erf.cc. References log(), Log2Pi, and x. Referenced by PLearn::GaussMix::computeLogLikelihood().

real PLearn::gauss_log_density_var (  real  x, real  mu, real  var )

Definition at line 201 of file pl_erf.cc. References log(), Log2Pi, var(), and x. Referenced by PLearn::GaussianProcessRegressor::computeCostsFromOutputs().

real PLearn::gaussian_01 (   )

returns a random number gaussian with mean 0 and standard deviation 1 Definition at line 328 of file random.cc. References gset, iset, log(), sqrt(), the_seed, and uniform_sample(). Referenced by gaussian_mu_sigma(), PLearn::EntropyContrast::gen_normal_0_1(), multivariate_normal(), and normal_sample().

real PLearn::gaussian_mixture_mu_sigma (  Vec &  w, const Vec &  mu, const Vec &  sigma )

returns a random number with mixture of gaussians, "w" is the mixture (must be positive numbers summing to 1), "mu" and "sigma" are the vectors of means and standard deviations for each gaussian Definition at line 369 of file random.cc. References PLearn::TVec< T >::data(), gaussian_mu_sigma(), and PLearn::TVec< T >::length().

real PLearn::gaussian_mu_sigma (  real  mu, real  sigma )

returns a random number gaussian with mean "mu" and standard dev "sigma" Definition at line 356 of file random.cc. References gaussian_01(). Referenced by fill_random_normal(), PLearn::DiagonalNormalSampleVariable::fprop(), gaussian_mixture_mu_sigma(), PLearn::SpiralDistribution::generate(), PLearn::GaussMix::generateFromGaussian(), and PLearn::AdaBoost::train().

template<class num_t> void generalizedEigenVecOfSymmMat (  TMat< num_t > &  m1, TMat< num_t > &  m2, int  itype, int  k, TVec< num_t > &  eigen_values, TMat< num_t > &  eigen_vectors )

Computes up to k largest eigen_values and corresponding eigen_vectors of a real generalized symmetric-definite eigenproblem, of the form m1*x=(lambda)*m2*x (itype = 1), m1*m2*x=(lambda)*x (itype = 2) or m2*m1*x=(lambda)*x (itype = 3) m1 and m2 are assumed to be symmetric and m2 is also positive definite. Parameters eigen_values and eigen_vectors are resized accordingly and filled by the call. The eigenvalues are returned in decreasing order (largest first). The corresponding eigenvectors are in the *ROWS* of eigen_vectors WARNING: m1 and m2 are destroyed during the operation. Definition at line 350 of file plapack.h. References k, lapackGeneralizedEIGEN(), PLearn::TMat< T >::length(), PLERROR, PLearn::TVec< T >::resize(), PLearn::TMat< T >::resize(), PLearn::TVec< T >::swap(), PLearn::TMat< T >::swapUpsideDown(), and PLearn::TMat< T >::width().

template<class T> T geometric_mean (  const TMat< T > &  mat  )

Definition at line 3823 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), exp(), PLearn::TMat< T >::length(), log(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> T geometric_mean (  const TVec< T > &  vec  )

Definition at line 363 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), exp(), PLearn::TVec< T >::length(), and PLERROR.

string PLearn::get_option (  const vector< string > &  command_line, const string &  option, const string &  default_value )

The command_line is made of pairs of the form option value Look for an option in the command_line, and return the corresponding value if it is found, or return default_value otherwise. Note: this may be useful with command_line = stringvector(argc,argv); Definition at line 577 of file stringutils.cc.

long PLearn::get_seed (   )

returns the current seed used by the random number generator Definition at line 211 of file random.cc. References seed(), and the_seed.

char getAfterSkipBlanks (  istream &  in  )  [inline]

gets the first char after removal of blanks Definition at line 164 of file fileutils.h.

char getAfterSkipBlanksAndComments (  istream &  in  )  [inline]

gets the first char after removal of blanks and comments Definition at line 167 of file fileutils.h. References skipBlanksAndComments(). Referenced by readAndMacroProcess().

string PLearn::getcwd (   )

returns the absolute path to the current working directory as a string Definition at line 81 of file fileutils.cc. Referenced by abspath(), getDataSet(), getDataSetDate(), and readAndMacroProcess().

VMat PLearn::getDataSet (  const string &  datasetstring, const string &  alias = "" )

datasetstring can be one of the following: the name of a preprogrammed dataset (possibly with parameter specification) the path of the basename of an .sdb the path of a file in one of the recognized matrix data formats the path of a directory containing a dataset the name of an alias in the dataset.aliases file of the current directory or one of its parents alias is a short name that can be used as part of a filename containing results related to the dataset ( it's set using the VMat's setAlias method, and code that wishes to use it can acces it by calling getAlias) Definition at line 109 of file getDataSet.cc. References c_str(), extract_directory(), extract_extension(), extract_filename(), getcwd(), getDatasetAliases(), isfile(), PLearn::PP< VMatrix >::isNull(), loadAsciiAsVMat(), loadAsciiSingleBinaryDescriptor(), loadClassificationDataset(), newObject(), pathexists(), PLERROR, readFileAndMacroProcess(), remove_extension(), removeblanks(), slash, split(), and vconcat(). Referenced by PLearn::AutoVMatrix::build_(), PLearn::LearnerCommand::compute_outputs(), PLearn::VVMatrix::createPreproVMat(), cross_valid(), plotVMats(), PLearn::VVMatrix::processJoinSection(), PLearn::TestDependencyCommand::run(), PLearn::TestDependenciesCommand::run(), PLearn::KolmogorovSmirnovCommand::run(), PLearn::LearnerCommand::test(), PLearn::LearnerCommand::train(), train_and_test(), use(), viewVMat(), and vmatmain().

map< string, string > PLearn::getDatasetAliases (  const string &  dir_or_file_path = "."  )

Looks for 'dataset.aliases' file in specified directory and its parent directories; loads it and returns the corresponding map. Returns an empty map if file was not found. Definition at line 258 of file getDataSet.cc. References PLearn::PStream::get(), isfile(), locateDatasetAliasesDir(), PLERROR, read(), removeblanks(), and ws(). Referenced by cross_valid(), getDataSet(), getDataSetDate(), train_and_test(), and use().

time_t PLearn::getDataSetDate (  const string &  datasetstring, const string &  alias = "" )

Definition at line 75 of file getDataSet.cc. References extract_extension(), getcwd(), getDatasetAliases(), isfile(), mtime(), pathexists(), PLERROR, and slash. Referenced by PLearn::VVMatrix::getDateOfVMat(), PLearn::VMatLanguage::preprocess(), and vmatmain().

string PLearn::getDataSetHelp (   )

returns a help describing the datasetstring parameter of getDataSet Definition at line 64 of file getDataSet.cc. References loadClassificationDatasetHelp(). Referenced by PLearn::HelpCommand::helpDatasets(), old_plearn_main(), and vmatmain().

time_t PLearn::getDateOfCode (  const string &  codefile  )

Definition at line 56 of file VMatLanguage.cc. References mtime(), and PLERROR. Referenced by PLearn::VVMatrix::getDateOfVMat().

string getHost (   )

Definition at line 675 of file VMatrix.cc. Referenced by PLearn::VMatrix::lockMetaDataDir().

bool getList (  char *  str, int  curj, const VMat &  vm, Vec &  outList, char *  strReason )

Definition at line 294 of file vmatmain.cc. References PLearn::TVec< T >::clear(), pl_isnumber(), PLearn::TVec< T >::push_back(), split(), toint(), and PLearn::VMat::width(). Referenced by viewVMat().

map< string, string > PLearn::getModelAliases (  const string &  filename  )

reads a modelalias -> object_representation map from a model.aliases file Definition at line 72 of file old_plearn_main.cc. References PLERROR, remove_comments(), removeblanks(), smartReadUntilNext(), and ws(). Referenced by cross_valid(), getMultipleModelAliases(), and train_and_test().

vector< string > PLearn::getMultipleModelAliases (  const string &  model  )

Definition at line 260 of file old_plearn_main.cc. References exitmsg(), getModelAliases(), and isfile(). Referenced by old_plearn_main().

void PLearn::getNextNonBlankLine (  istream &  in, string &  line )

returns the next non blank line (#-style comments are considered blank) Definition at line 451 of file fileutils.cc. Referenced by PLearn::AsciiVMatrix::build_(), and parseSizeFromRemainingLines().

vector< string > PLearn::getNonBlankLines (  const string &  in  )

Returns a vector of string containing only non-empty lines, as you guessed it. Definition at line 594 of file stringutils.cc. References isBlank(), and split(). Referenced by PLearn::VVMatrix::buildFilteredVMatFromVPL(), and PLearn::VVMatrix::extractSourceMatrix().

int getPid (   )

Definition at line 680 of file VMatrix.cc. Referenced by PLearn::VMatrix::lockMetaDataDir().

template<class T> TVec<T> getQuantiles (  const TVec< T > &  vec, int  q )

returns a vector of length q+1 that contains the q quantiles of the sorted vector v and the last value corresponds to the last value of the vector vec. Definition at line 1928 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

int PLearn::getSystemTotalMemory (   )

Definition at line 9 of file procinfo.cc. References PLERROR. Referenced by PLearn::TestDependenciesCommand::run().

string getUser (   )

Definition at line 685 of file VMatrix.cc. Referenced by PLearn::VMatrix::lockMetaDataDir().

template<class T> int GramSchmidtOrthogonalization (  TMat< T >  A, T  tolerance = 1e-6 )

Orthonormalize in-place the rows of the given matrix, using successive projections on the orthogonal subspace of the previously found basis. The resulting matrix has the following properties: its rows spans the same space as A its rows are orthogonal (dot product = 0) its rows are of norm 1 However, it may happen that the original rows of A were not linearly independent. In that case the, algorithm returns the number of rows that were successfully obtained (and the user should probably then do A = A.subMatRows(0,result) to obtain the basis). The tolerance argument is the minimum value of the norm of a row when projected orthogonal to the previous ones for this row to contribute to the basis. Definition at line 5654 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), norm(), and projectOnOrthogonalSubspace(). Referenced by diagonalizeSubspace(), and PLearn::PCA::train().

VMat PLearn::grep (  VMat  d, int  col, Vec  values, const string &  indexfile, bool  exclude = false )

Same as above, except that the indexes of the rows are stored on disk rather than in memory (a SelectRowsFileIndexVMatrix is returned rather than a SelectRowsVMatrix) BEWARE: If the indexfile already exists, it is *not* recomputed, but used as is. Definition at line 743 of file VMat_maths.cc. References PLearn::IntVecFile::append(), PLearn::TVec< T >::contains(), file_exists(), PLearn::VMat::length(), and PLearn::VMat::rows().

VMat PLearn::grep (  VMat  d, int  col, Vec  values, bool  exclude = false )

If exclude==false (the default) returns a VMat containing only the rows whose column col has a value that belongs to the given set of authorized values If exclude==true returns a VMat with all the other rows (corresponds to grep -v) [MISSING_VALUE is a possible value and is handled correctly] Definition at line 689 of file VMat_maths.cc. References PLearn::TVec< T >::contains(), PLearn::TVec< T >::copy(), PLearn::VMat::length(), PLearn::VMat::rows(), and PLearn::TVec< T >::subVec().

template<class T> TMat<T> grep (  TMat< T >  data, int  col, T  value, bool  exclude = false )  [inline]

Same as above, but with a single value argument. Definition at line 5738 of file TMat_maths_impl.h. References grep().

template<class T> TMat<T> grep (  TMat< T >  data, int  col, TVec< T >  values, bool  exclude = false )

Definition at line 5563 of file TMat_maths_impl.h. References PLearn::TMat< T >::compact(), PLearn::TVec< T >::contains(), PLearn::TMat< T >::length(), PLearn::TMat< T >::resize(), and PLearn::TMat< T >::width(). Referenced by grep().

void PLearn::halfShuffleRows (  SDB &  sdb  )

not quite a random shuffle (see implementation) but more efficient use of disk cache Definition at line 937 of file SimpleDB.cc. References endl(), PLearn::SimpleDB< KeyType, QueryResult >::getInRow(), PLearn::SimpleDB< KeyType, QueryResult >::getSchema(), k, PLearn::SimpleDB< KeyType, QueryResult >::length(), SDB, and PLearn::SimpleDB< KeyType, QueryResult >::setRow().

Var hard_slope (  Var  x, Var  left, Var  right )  [inline]

Definition at line 75 of file HardSlopeVariable.h. References left(), right(), and x.

real hard_slope (  real  x, real  left = 0, real  right = 1 )  [inline]

Definition at line 338 of file pl_math.h. References left(), right(), and x. Referenced by PLearn::UnaryHardSlopeVariable::fprop(), PLearn::HardSlopeVariable::fprop(), soft_slope(), and tabulated_soft_slope().

real PLearn::hard_slope_integral (  real  left = 0, real  right = 1, real  a = 0, real  b = 1 )

Definition at line 220 of file pl_math.cc. Referenced by soft_slope_integral(), and tabulated_soft_slope_integral().

template<class T> T harmonic_mean (  const TVec< T > &  vec, bool  ignore_missing = false )

Definition at line 325 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), MISSING_VALUE, and PLERROR.

size_t PLearn::hashbytes (  const char *  byte_start, size_t  byte_length )

**************** Hash tables support ************************* basic hashing function that can be used in defining the hashing functions for objects or any type. This one mixes the bits in the byte_length bytes starting at byte_start, and returns an integer between 0 and MAXINT Definition at line 78 of file pl_hash_fun.cc. References PL_HASH_NOMBRES_MAGIQUES. Referenced by hashval().

template<class T, unsigned SizeBits, class Allocator> unsigned int hashval (  const SmallVector< T, SizeBits, Allocator > &  v  )  [inline]

hash function for hash tables Definition at line 170 of file SmallVector.h. References hashbytes().

template<class T> size_t hashval (  const T &  x  )  [inline]

default which will work in many cases but not all Definition at line 75 of file pl_hash_fun.h. References hashbytes(), and x.

size_t hashval (  const char *  strng  )  [inline]

hashing function which must be redefined for classes that can be used as keys: unsigned int hash(const T& object); or unsigned int hash(const T object); This function returns ANY unsigned int (i.e. between 0 and MAXINT) (it is hash(x)table_size that will be used to choose an address in the hash table). It is defined here for some built-in types: Definition at line 70 of file pl_hash_fun.h. References hashbytes(), and strlen().

VMat hconcat (  Array< VMat >  ds  )  [inline]

Definition at line 97 of file ConcatColumnsVMatrix.h.

VMat hconcat (  VMat  d1, VMat  d2 )  [inline]

Definition at line 94 of file ConcatColumnsVMatrix.h.

Var hconcat (  const VarArray &  varray  )  [inline]

Definition at line 78 of file ConcatColumnsVariable.h.

RandomVar PLearn::hconcat (  const RVArray &  a  )

Definition at line 456 of file RandomVar.cc.

template<class T> TMat<T> hconcat (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

Definition at line 214 of file Array_impl.h. References hconcat().

template<class T> TMat<T> hconcat (  const Array< TMat< T > > &  ar  )

Definition at line 190 of file Array_impl.h. References PLERROR, and PLearn::TMat< T >::subMatColumns(). Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::VVMatrix::createPreproVMat(), hconcat(), loadUSPS(), PLearn::LocallyWeightedDistribution::log_density(), PLearn::SequentialModelSelector::matlabSave(), removeColumn(), PLearn::ConcatColumnsRandomVariable::setValueFromParentsValue(), PLearn::TangentLearner::train(), PLearn::GaussianContinuum::train(), and PLearn::ClassifierFromDensity::train().

double PLearn::hhmmss_to_double (  int  hh, int  mm, int  ss )

converts an hours/minutes/seconds to a day fraction Definition at line 166 of file PDateTime.cc. Referenced by datetime_to_double(), and PLearn::PDateTime::toJulianDay().

template<class T> TVec<T> histogram (  const TVec< T > &  vec, T  minval, T  maxval, int  nbins )

Definition at line 466 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and val. Referenced by displayHistogram(), and PLearn::Gnuplot::histoplot().

string PLearn::hostname (   )

Definition at line 139 of file general.cc. References PLERROR. Referenced by establish_connection(), and PLearn::IPServer::machine_name().

real hyperplaneDistance (  const Vec &  x, const Mat &  points, real  weight_decay = 0. )  [inline]

Distance between point x and closest point on hyperplane that passes through all points. Definition at line 619 of file plapack.h. References closestPointOnHyperplane(), L2distance(), and x.

int PLearn::ICBCpartition (  const Vec &  claims, real  threshold )

Given a vector of claims (representing the claim values for different KOLs) and a threshold value, the following function returns the class (integer between 0 and 3) of the claims vector. There are 4 classes: class 0: "neg" claim vector (i.e. at least one claim in the vector < 0 and the others = 0) class 1: "zero" claim vector (i.e. all claims in the vector are 0) class 2: "smallpos" claim vector (i.e. at least one claim in the vector > 0 and all claims < threshold) class 3: "largepos" claim vector (i.e. at least one claim in the vector >= threshold) Definition at line 1210 of file SDBVMat.cc. References PLearn::TVec< T >::length(). Referenced by PLearn::SDBVMFieldICBCClassification::convertField().

Var ifThenElse (  Var  If, Var  Then, Var  Else )  [inline]

IT WOULD BE NICE IF WE COULD REDEFINE (:?). Definition at line 86 of file IfThenElseVariable.h.

template<class T> void ifThenElse (  const TVec< T > &  if_vec, const TVec< T > &  then_vec, const TVec< T > &  else_vec, TVec< T > &  dest )

Definition at line 1771 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::NeuralNet::build_(), PLearn::ConditionalDensityNet::build_(), d_hard_slope(), PLearn::PowVariableVariable::symbolicBprop(), PLearn::IfThenElseVariable::symbolicBprop(), and PLearn::AbsVariable::symbolicBprop().

double PLearn::incbcf (  double  a, double  b, double  x )

Definition at line 813 of file random.cc. References big, biginv, MACHEP, and x.

double PLearn::incbd (  double  a, double  b, double  x )

Definition at line 903 of file random.cc. References big, biginv, MACHEP, and x.

real PLearn::incomplete_beta (  real  z, real  x, real  y )

Note that z must be in [0,1] Definition at line 146 of file random.cc. References exp(), incomplete_beta_continued_fraction(), log(), log_beta(), PLERROR, and x. Referenced by student_t_cdf().

real incomplete_beta_continued_fraction (  real  z, real  x, real  y )

Definition at line 94 of file random.cc. References k, PLWARNING, and x. Referenced by incomplete_beta().

map< real, TVec< int > > PLearn::indicesOfOccurencesInColumn (  VMat  m, int  col )

returns a map mapping all different values appearing in column col to a vector of the corresponding row indices in the VMat (this proceeds in 2 passes, first calling countOccurencesInColumn to allocate the exact memory) Definition at line 726 of file VMat_maths.cc. References countOccurencesInColumn(), and PLearn::VMat::length(). Referenced by PLearn::ClassifierFromDensity::train().

Mat PLearn::input2dSet (  const string &  filename = "data2d.amat"  )

This will input a 2d binary classification problem (launches a java applet). Definition at line 59 of file databases.cc. References file_exists(), JAVA, loadAscii(), and shuffleRows(). Referenced by loadClassificationDataset().

void PLearn::interactiveDisplayCDF (  const Array< VMat > &  vmats  )

Definition at line 96 of file vmatmain.cc. References PLearn::StatsCollector::cdf(), endl(), k, PLearn::TVec< T >::length(), pgetline(), PLearn::Gnuplot::plot(), PLearn::TVec< T >::size(), split(), and toint(). Referenced by vmatmain().

void intersection (  Set  a, Set  b, Set  res )  [inline]

Definition at line 100 of file Set.h. References PLearn::Set::begin(), and PLearn::Set::end().

Var interValues (  Var  values  )  [inline]

if values = [x1,x2,...,x10], the resulting variable is [(x1+x2)/2,(x2+x3)/2, ... (x9+x10)/2] Definition at line 80 of file InterValuesVariable.h.

template<class T> void inverse (  const TMat< T > &  m, TMat< T > &  inv )

Definition at line 4709 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::resize(), and PLearn::TMat< T >::width().

template<class T> TMat<T> inverse (  TMat< T > &  m  )

Definition at line 4699 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(). Referenced by PLearn::MatrixInverseVariable::fprop(), leftPseudoInverse(), matInvert(), and rightPseudoInverse().

template<class T> TVec<T> inverse_sigmoid (  const TVec< T > &  src  )  [inline]

Definition at line 1032 of file TMat_maths_impl.h. References compute_inverse_sigmoid(), and PLearn::TVec< T >::length().

real inverse_sigmoid (  real  x  )  [inline]

numerically stable version of inverse_sigmoid(x) = log(x/(1-x)) Definition at line 278 of file pl_math.h. References FEQUAL, log(), PLERROR, and x. Referenced by compute_inverse_sigmoid(), PLearn::ConditionalDensityNet::initializeParams(), and PLearn::ConditionalDensityNet::train().

real inverse_softplus (  real  y  )  [inline]

inverse of softplus function Definition at line 327 of file pl_math.h. References exp(), log(), and MISSING_VALUE. Referenced by PLearn::ConditionalDensityNet::initializeParams(), and PLearn::ConditionalDensityNet::train().

template<class T> TVec<T> inverted (  const TVec< T > &  vec  )

Definition at line 1053 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

Var invertElements (  Var  v  )  [inline]

Definition at line 72 of file InvertElementsVariable.h.

template<class T> void invertElements (  const TMat< T > &  m  )

x'_ij = 1.0/x_ij; Definition at line 4642 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void invertElements (  const TVec< T > &  vec  )

Definition at line 1045 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length(). Referenced by PLearn::ShiftAndRescaleVMatrix::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), d_hard_slope(), normalize(), operator/(), PLearn::DivVariable::symbolicBprop(), and PLearn::PLS::train().

real ipow (  real  x, int  p )  [inline]

Definition at line 261 of file pl_math.h. References x. Referenced by PLearn::CompactVMatrixPolynomialKernel::evaluate(), and PLearn::PolynomialKernel::evaluateFromDot().

bool is_integer (  real  x  )  [inline]

Definition at line 248 of file pl_math.h. References x. Referenced by computeConditionalMeans(), and PLearn::ClassErrorCostFunction::evaluate().

bool is_missing (  float  x  )  [inline]

Missing value for double and float are represented by NaN. Definition at line 246 of file pl_math.h. References x.

bool is_missing (  double  x  )  [inline]

Missing value for double and float are represented by NaN. Definition at line 243 of file pl_math.h. References x.

template<class T> bool is_missing (  const T &  x  )  [inline]

Tells if the passed value means "missing" for its data-type. The default version of returns false (not a "missing value") Definition at line 240 of file pl_math.h. Referenced by argmax(), argmin(), PLearn::RealMapping::binnumber(), PLearn::FinancePreprocVMatrix::build_(), computeBasicStats(), PLearn::SDBVMFieldICBCTargets::convertField(), PLearn::SDBVMFieldDate::convertField(), PLearn::SDBVMField::convertMissing(), countOccurencesInColumn(), dilogarithm(), double_to_datetime(), DX_create_grid_outputs_file(), PLearn::ConditionalStatsCollector::findrange(), float_to_date(), PLearn::DilogarithmVariable::fprop(), PLearn::ConcatRowsVMatrix::fullyCheckMappings(), PLearn::ConcatRowsVMatrix::get(), PLearn::MovingAverageVMatrix::getNewRow(), PLearn::JoinVMatrix::getNewRow(), PLearn::FinancePreprocVMatrix::getNewRow(), PLearn::CumVMatrix::getNewRow(), PLearn::ConcatRowsVMatrix::getSubRow(), PLearn::VMatrix::getValString(), PLearn::SDBVMOutputCoder::handleOtherAndMissing(), harmonic_mean(), PLearn::TVec< pair< real, real > >::hasMissing(), PLearn::TMat< pair< real, real > >::hasMissing(), PLearn::RealMapping::map(), matlabSave(), mean(), new_get_compr_data_type(), new_write_raw_data_as(), PLearn::VMatLanguage::preprocess(), PLearn::RealMapping::print(), print_diff(), remove_missing(), PLearn::VMatLanguage::run(), PLearn::SDBVMOutputCoder::setOutput(), PLearn::StrTableVMatrix::StrTableVMatrix(), sum(), PLearn::VMFieldStat::update(), PLearn::StatsCollector::update(), PLearn::LiftStatsCollector::update(), PLearn::ConditionalStatsCollector::update(), viewVMat(), weighted_mean(), and PLearn::PStream::writeAsciiNum().

real is_positive (  real  x  )  [inline]

"hard" version of the sigmoid, i.e. the indicator function that is 1 if its argument is STRICTLY positive, and 0 otherwise Definition at line 275 of file pl_math.h. References x.

Var isAboveThreshold (  Var  v, real  threshold = 0, real  truevalue = 1, real  falsevalue = 0, bool  strict = false )  [inline]

Definition at line 80 of file IsAboveThresholdVariable.h. Referenced by PLearn::ConditionalDensityNet::build_().

bool PLearn::isAlpha (  char  c  )

Definition at line 2832 of file WordNetOntology.cc. References isDigit(), and isLetter().

bool PLearn::isBlank (  const string &  s  )

returns true if s is a blank line (containing only space, tab, until end of line or a # comment-character is reached Definition at line 303 of file stringutils.cc. Referenced by getNonBlankLines(), PLearn::VMatLanguage::preprocess(), and PLearn::VVMatrix::processJoinSection().

Var PLearn::isdifferent (  Var  v1, Var  v2 )

Definition at line 198 of file Var_operators.cc. References isequal().

bool PLearn::isDigit (  char  c  )

Definition at line 2827 of file WordNetOntology.cc. Referenced by isAlpha(), and trimWord().

bool PLearn::isdir (  const string &  path  )

returns true if the given path is an existing directory (or a symbolic link pointing to a directory) Definition at line 139 of file fileutils.cc. Referenced by abspath(), PLearn::DiskVMatrix::build_(), PLearn::VVMatrix::createPreproVMat(), force_mkdir(), force_rmdir(), goAndCreateDir(), makedir(), PLearn::VMatrix::resolveFieldInfoLink(), train_and_test(), and PLearn::PrecomputedVMatrix::usePrecomputed().

Var PLearn::isequal (  Var  v1, Var  v2 )

First case: v1 and v2 are two vectors of length() l resulting Var is 1 if for all i=0 to l-1, v1->value[i] == v2->value[i], 0 otherwise Second case: one of v1 or v2 is a scalar variable (length() 1) and the other is a vector of length() l resulting Var is a vector of length() l, doing an element-wise comparison Definition at line 98 of file EqualVariable.cc. Referenced by isdifferent(), PLearn::FunctionalRandomVariable::logP(), operator!=(), and operator==().

bool PLearn::isfile (  const string &  path  )

returns true if the given path is an existing regular file (or a symbolic link pointing to a file) Definition at line 153 of file fileutils.cc. Referenced by PLearn::AsciiVMatrix::AsciiVMatrix(), PLearn::FileVMatrix::build_(), PLearn::DiskVMatrix::build_(), PLearn::AsciiVMatrix::build_(), PLearn::VVMatrix::buildFilteredVMatFromVPL(), PLearn::VVMatrix::createPreproVMat(), cross_valid(), PLearn::FileVMatrix::FileVMatrix(), PLearn::VVMatrix::generateVMatIndex(), getDataSet(), getDatasetAliases(), getDataSetDate(), PLearn::VMatrix::getFieldInfos(), PLearn::AutoSDBVMatrix::getMappings(), getMultipleModelAliases(), PLearn::VVMatrix::getPrecomputedDataName(), PLearn::VMatrix::getRanges(), PLearn::VMatrix::getSFIFFilename(), PLearn::VMatrix::getStats(), PLearn::GhostScript::GhostScript(), PLearn::VVMatrix::isPrecomputedAndUpToDate(), PLearn::VMatrix::loadStringMapping(), locateDatasetAliasesDir(), old_plearn_main(), PLearn::IntVecFile::open(), PLearn::VMatrix::resolveFieldInfoLink(), train_and_test(), and PLearn::PrecomputedVMatrix::usePrecomputed().

template<class T> void isLargerThan (  const TVec< T > &  first, const TVec< T > &  second, TVec< T > &  dest )

Definition at line 1711 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

template<class T> void isLargerThanOrEqualTo (  const TVec< T > &  first, const TVec< T > &  second, TVec< T > &  dest )

Definition at line 1726 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

bool PLearn::isLegalPunct (  char  c  )

Definition at line 2837 of file WordNetOntology.cc. Referenced by trimWord().

bool PLearn::isLetter (  char  c  )

Definition at line 2822 of file WordNetOntology.cc. Referenced by isAlpha(), and trimWord().

bool PLearn::isMapKeysAreInt (  map< real, int > &  m  )

check that all keys of the map are int values Definition at line 126 of file general.cc. Referenced by PLearn::CompactVMatrix::CompactVMatrix().

Var isMissing (  Var  x  )  [inline]

Definition at line 79 of file IsMissingVariable.h. References x. Referenced by PLearn::NeuralNet::build_().

bool PLearn::isParagraphBlank (  const string &  s  )

returns true if s is a blank paragraph (containing only space, tab, until end of **string**) Definition at line 318 of file stringutils.cc.

template<class T> void isSmallerThan (  const TVec< T > &  first, const TVec< T > &  second, TVec< T > &  dest )

Definition at line 1741 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

template<class T> void isSmallerThanOrEqualTo (  const TVec< T > &  first, const TVec< T > &  second, TVec< T > &  dest )

Definition at line 1756 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

string join (  const TVec< string > &  s, const string &  separator )  [inline]

Definition at line 832 of file TMat_impl.h. References PLearn::TVec< T >::size().

string PLearn::join (  const vector< string > &  s, const string &  separator = " " )

makes a single string from a vector of strings Definition at line 499 of file stringutils.cc.

string join (  const Array< string > &  s, const string &  separator )  [inline]

Definition at line 132 of file Array_impl.h. References PLearn::TVec< T >::size(). Referenced by PLearn::Learner::measure(), PLearn::Learner::openTestResultsStreams(), PLearn::Learner::openTrainObjectiveStream(), PLearn::PTester::perform(), PLearn::VMatLanguage::preprocess(), and PLearn::SequentialValidation::run().

real PLearn::KS_test (  Vec &  v1, Vec &  v2, int  conv = 10 )

Returns result of Kolmogorov-Smirnov test between 2 samples The call sorts v1 and v2. Definition at line 247 of file stats_utils.cc. References KS_test().

void PLearn::KS_test (  Vec &  v1, Vec &  v2, int  conv, real &  D, real &  p_value )

Kolmogorov-Smirnov test. Computes D (the max abs dfference between the 2 cdfs) and p_value P(random variable D > observed D|no difference in true prob) A reasonable value for D is 10. The call sorts v1 and v2. Definition at line 238 of file stats_utils.cc. References KS_test(), PLearn::TVec< T >::length(), max_cdf_diff(), and p_value().

real PLearn::KS_test (  real  D, real  N, int  conv = 10 )

Return the probability that the Kolmogorov-Smirnov statistic D takes the observed value or greater, given the null hypothesis that the distributions that are compared are really identical. N is the effective number of samples used for comparing the distributions. The argument conv gives the precision with which this probability is computed. A value above 10 does not bring much improvement. Note that the statistic D can be obtained as follows: Comparing two empirical distributions from data sets D_1 and D_2: Let F_1(x) the empirical cumulative distribution of D_1 of size N_1, and let F_2(x) the empirical cumulative distribution of D_2 of size N_2. Then D = max_x | F_1(x) - F_2(x) | and the effective N is N_1 N_2 / (N_1 + N_2). Comparing a theoretical distribution F and a data set D of size N with empirical cumulative distribution F_N: D = max_x | F(x) - F_N(x) | This function returns the following P(D > observed d | same distributions) estimated by 2 sum_{k=1}^{infty} (-1)^{k-1} exp(-2k^2 a^2) where a = sqrt(D*(sqrt(N)+0.12+0.11/sqrt(N))) Ref: Stephens, M.A. (1970), Journal of the Royal Statistical Society B, vol. 32, pp. 115-122. Definition at line 221 of file stats_utils.cc. References exp(), k, sqrt(), and x. Referenced by KS_test(), and PLearn::KolmogorovSmirnovCommand::run().

template<class T> T kthOrderedElement (  const TVec< T > &  vec, int  k )  [inline]

returns the value of the kth ordered element of v k can take values 0 to vec.length()-1 Definition at line 1875 of file TMat_maths_impl.h. References k, and positionOfkthOrderedElement(). Referenced by median().

template<class T> T L1distance (  const TVec< T > &  vec1, const TVec< T > &  vec2 )  [inline]

Definition at line 775 of file TMat_maths_impl.h. References dist().

template<class T> T L2distance (  const TVec< T > &  vec1, const TVec< T > &  vec2 )  [inline]

Definition at line 771 of file TMat_maths_impl.h. References dist(). Referenced by hyperplaneDistance(), and printDistanceStatistics().

void lapack_Xgesdd_ (  char *  JOBZ, int *  M, int *  N, float *  A, int *  LDA, float *  S, float *  U, int *  LDU, float *  VT, int *  LDVT, float *  WORK, int *  LWORK, int *  IWORK, int *  INFO )  [inline]

Definition at line 64 of file plapack.h. References sgesdd_().

void lapack_Xgesdd_ (  char *  JOBZ, int *  M, int *  N, double *  A, int *  LDA, double *  S, double *  U, int *  LDU, double *  VT, int *  LDVT, double *  WORK, int *  LWORK, int *  IWORK, int *  INFO )  [inline]

Definition at line 61 of file plapack.h. References dgesdd_(). Referenced by lapackSVD().

void lapack_Xsyevr_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, double *  A, int *  LDA, double *  VL, double *  VU, int *  IL, int *  IU, double *  ABSTOL, int *  M, double *  W, double *  Z, int *  LDZ, int *  ISUPPZ, double *  WORK, int *  LWORK, int *  IWORK, int *  LIWORK, int *  INFO )  [inline]

Definition at line 70 of file plapack.h. References dsyevr_().

void lapack_Xsyevr_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, float *  A, int *  LDA, float *  VL, float *  VU, int *  IL, int *  IU, float *  ABSTOL, int *  M, float *  W, float *  Z, int *  LDZ, int *  ISUPPZ, float *  WORK, int *  LWORK, int *  IWORK, int *  LIWORK, int *  INFO )  [inline]

Definition at line 67 of file plapack.h. References ssyevr_().

void lapack_Xsyevx_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, float *  A, int *  LDA, float *  VL, float *  VU, int *  IL, int *  IU, float *  ABSTOL, int *  M, float *  W, float *  Z, int *  LDZ, float *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )  [inline]

Definition at line 58 of file plapack.h. References ssyevx_().

void lapack_Xsyevx_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, double *  A, int *  LDA, double *  VL, double *  VU, int *  IL, int *  IU, double *  ABSTOL, int *  M, double *  W, double *  Z, int *  LDZ, double *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )  [inline]

Definition at line 55 of file plapack.h. References dsyevx_(). Referenced by eigen_SymmMat(), and lapackEIGEN().

void lapack_Xsygvx_ (  int *  ITYPE, char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, float *  A, int *  LDA, float *  B, int *  LDB, float *  VL, float *  VU, int *  IL, int *  IU, float *  ABSTOL, int *  M, float *  W, float *  Z, int *  LDZ, float *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )  [inline]

Definition at line 76 of file plapack.h. References ssygvx_().

void lapack_Xsygvx_ (  int *  ITYPE, char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, double *  A, int *  LDA, double *  B, int *  LDB, double *  VL, double *  VU, int *  IL, int *  IU, double *  ABSTOL, int *  M, double *  W, double *  Z, int *  LDZ, double *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )  [inline]

Definition at line 73 of file plapack.h. References dsygvx_(). Referenced by lapackGeneralizedEIGEN().

template<class num_t> void lapackEIGEN (  const TMat< num_t > &  A, TVec< num_t > &  eigenvals, TMat< num_t > &  eigenvecs, char  RANGE = 'A', num_t  low = 0, num_t  high = 0, num_t  ABSTOL = 0 )

Computes the eigenvalues and eigenvectors of a symmetric (NxN) matrix A. Meaning of RANGE: 'A': all eigenvalues will be found. 'V': all eigenvalues in the half-open interval (low,high] will be found. 'I': will find eigenvals with indexes int(low) to int(high) included (smallest eigenval having index 0) ABSTOL is the tolerance (see lapack doc for call dsyevx_ ) If you do not wish to compute eigenvectors, provide a null (empty) 'eigenvecs'. Upon return, eigenvals will contain the M eigenvalues found in increasing order (it will be resized to M). And eigenvecs (unless initially null) will be resized to an MxN matrix containing the corresponding M eigenvectors in its *rows*. Definition at line 101 of file plapack.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::isEmpty(), PLearn::TMat< T >::isNotEmpty(), lapack_Xsyevx_(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), and PLearn::TMat< T >::width(). Referenced by eigenVecOfSymmMat().

template<class num_t> void lapackGeneralizedEIGEN (  const TMat< num_t > &  A, const TMat< num_t > &  B, int  ITYPE, TVec< num_t > &  eigenvals, TMat< num_t > &  eigenvecs, char  RANGE = 'A', num_t  low = 0, num_t  high = 0, num_t  ABSTOL = 0 )

Computes the eigenvalues and eigenvectors of a real generalized symmetric-definite eigenproblem, of the form A*x=(lambda)*B*x, A*Bx=(lambda)*x, or B*A*x=(lambda)*x A and B are assumed to be symmetric and B is also positive definite. Meaning of ITYPE Specifies the problem type to be solved: = 1: A*x = (lambda)*B*x = 2: A*B*x = (lambda)*x = 3: B*A*x = (lambda)*x Meaning of RANGE: 'A': all eigenvalues will be found. 'V': all eigenvalues in the half-open interval (low,high] will be found. 'I': will find eigenvals with indexes int(low) to int(high) included (smallest eigenval having index 0) ABSTOL is the tolerance (see lapack doc for call dsygvx_ ) If you do not wish to compute eigenvectors, provide a null (empty) 'eigenvecs'. Upon return, eigenvals will contain the M eigenvalues found in increasing order (it will be resized to M). And eigenvecs (unless initially null) will be resized to an MxN matrix containing the corresponding M eigenvectors in its *rows*. Definition at line 222 of file plapack.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::isEmpty(), PLearn::TMat< T >::isNotEmpty(), lapack_Xsygvx_(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), and PLearn::TMat< T >::width(). Referenced by generalizedEigenVecOfSymmMat().

int PLearn::lapackSolveLinearSystem (  Mat &  At, Mat &  Bt, TVec< int > &  pivots )

Solves AX = B This is a simple wrapper over the lapack routine. It expects At and Bt (transposes of A and B) as input, as well as storage for resulting pivots vector of ints of same length as A. The call overwrites Bt, putting the transposed solution Xt in there, and At is also overwritten to contain the factors L and U from the factorization A = P*L*U; (the unit diagonal elements of L are not stored). The lapack status is returned: = 0: successful exit < 0: if INFO = -i, the i-th argument had an illegal value > 0: if INFO = i, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution could not be computed. Definition at line 293 of file plapack.cc. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), dgesv_(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, PLearn::TVec< T >::resize(), sgesv_(), and PLearn::TMat< T >::width(). Referenced by solveLinearSystem().

template<class num_t> void lapackSVD (  const TMat< num_t > &  At, TMat< num_t > &  Ut, TVec< num_t > &  S, TMat< num_t > &  V, char  JOBZ = 'A', real  safeguard = 1 )

Performs the SVD decomposition A = U.S.Vt See SVD(...) for more details. CAREFUL: the 'At' matrix argument is changed in the process! This is a straight forward call to the lapack function. As fortran uses column-major matrices, and we use row-major matrices, it's really as if we had to pass the transpose of A (denoted At) and were getting back the transpose of U (Ut) and V. If you want a version without the funny transposes, look at SVD (which simply calls this one with a different order of parameters...) Definition at line 386 of file plapack.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), endl(), PLearn::TMat< T >::isNotEmpty(), lapack_Xgesdd_(), PLearn::TMat< T >::length(), min(), PLearn::TMat< T >::mod(), PLERROR, PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), and PLearn::TMat< T >::width(). Referenced by PLearn::GaussianContinuum::compute_train_and_validation_costs(), PLearn::ProjectionErrorVariable::fprop(), PLearn::NllSemisphericalGaussianVariable::fprop(), PLearn::GaussianContinuum::make_random_walk(), and SVD().

string PLearn::left (  const string &  s, size_t  width, char  padding = ' ' )

aligns the given string in a cell having the given width Definition at line 56 of file stringutils.cc. Referenced by PLearn::SoftSlopeVariable::bprop(), PLearn::SoftSlopeIntegralVariable::bprop(), PLearn::HardSlopeVariable::bprop(), d_hard_slope(), d_soft_slope(), displayHistogram(), PLearn::SoftSlopeVariable::fprop(), PLearn::SoftSlopeIntegralVariable::fprop(), PLearn::HardSlopeVariable::fprop(), hard_slope(), PLearn::HardSlopeVariable::HardSlopeVariable(), operator<(), operator<<(), operator<=(), operator>(), operator>=(), soft_slope(), soft_slope_integral(), soft_slope_limit(), PLearn::SoftSlopeIntegralVariable::SoftSlopeIntegralVariable(), PLearn::SoftSlopeVariable::SoftSlopeVariable(), split_on_first(), tabulated_soft_slope(), tabulated_soft_slope_integral(), and PLearn::UnaryHardSlopeVariable::UnaryHardSlopeVariable().

Var leftPseudoInverse (  Var  v  )  [inline]

Definition at line 76 of file LeftPseudoInverseVariable.h.

template<class T> void leftPseudoInverse (  const TMat< T > &  m, TMat< T > &  inv )

Definition at line 4663 of file TMat_maths_impl.h. References inverse(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> TMat<T> leftPseudoInverse (  TMat< T > &  m  )

Definition at line 4653 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and PLearn::TMat< T >::width(). Referenced by PLearn::LeftPseudoInverseVariable::fprop(), and PLearn::ProductRandomVariable::invertible().

bool lessPair (  pair< int, float > &  p1, pair< int, float > &  p2 )

Definition at line 1034 of file GraphicalBiText.cc.

Var lift_output (  Var  linear_output, Var  target )  [inline]

Definition at line 71 of file LiftOutputVariable.h. Referenced by PLearn::NNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), and PLearn::MultiInstanceNNet::build_().

StatsIt lift_stats (  int  the_index = 0, real  the_fraction = 0.1 )  [inline]

Definition at line 415 of file StatsIterator.h.

Mat PLearn::linearRegression (  VMat  inputs, VMat  outputs, real  weight_decay )

Version that does all the memory allocations of XtX, XtY and theta_t. Returns theta_t. Definition at line 974 of file VMat_maths.cc. References linearRegression(), and PLearn::VMat::width().

real PLearn::linearRegression (  VMat  inputs, VMat  outputs, real  weight_decay, Mat  theta_t, bool  use_precomputed_XtX_XtY, Mat  XtX, Mat  XtY, real &  sum_squared_Y, bool  return_squared_loss = false, int  verbose_computation_every = 0, bool  cholesky = true )

computes the result of the linear regression into theta_t Parameters must have the following sizes: inputs(l,n) outputs(l,m) theta_t(n+1,m) XtX(n+1,n+1) XtY(n+1,m) The n+1 is due to the inclusion of the bias terms in the matrices (first row of theta_t) If use_precomputed_XtX_XtY is false, then they are computed. Otherwise they are used as they are (typically passed precomputed from a previous call made with a possibly different weight_decay). Returns average of squared loss. Definition at line 907 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), dot(), externalProductAcc(), PLearn::TMat< T >::length(), PLearn::VMat::length(), PLERROR, product(), solveLinearSystem(), solveLinearSystemByCholesky(), PLearn::TMat< T >::width(), PLearn::VMat::width(), and x.

template<class T> void linearRegression (  TVec< T >  inputs, TVec< T >  outputs, T  weight_decay, TVec< T >  theta_t )

Definition at line 5465 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and PLERROR.

template<class T> void linearRegression (  TMat< T >  inputs, TMat< T >  outputs, T  weight_decay, TMat< T >  theta_t )

Definition at line 5393 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, solveLinearSystemByCholesky(), and PLearn::TMat< T >::width(). Referenced by linearRegression(), and PLearn::LinearRegressor::train().

template<class T> void linearRegressionNoBias (  TMat< T >  inputs, TMat< T >  outputs, T  weight_decay, TMat< T >  weights )

Definition at line 5359 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLERROR, solveLinearSystemByCholesky(), transposeProduct(), and PLearn::TMat< T >::width().

template<class T> void load (  const string &  filepath, T &  x )  [inline]

Definition at line 1172 of file PStream.h. References PLERROR, and x. Referenced by PLearn::NearestNeighborPredictionCost::build_(), PLearn::CompactVMatrix::CompactVMatrix(), PLearn::LearnerCommand::compute_outputs(), PLearn::AutoSDBVMatrix::getMappings(), PLearn::VMatrix::getRanges(), PLearn::VMatrix::getStats(), PLearn::WordNetOntology::load(), PLearn::Object::load(), main(), PLearn::Learner::measure(), PLearn::RGBImageDB::RGBImageDB(), PLearn::RowMapSparseMatrix< real >::RowMapSparseMatrix(), PLearn::ReadAndWriteCommand::run(), PLearn::SparseVMatrix::SparseVMatrix(), PLearn::LearnerCommand::test(), PLearn::LearnerCommand::train(), PLearn::GaussianContinuum::train(), and PLearn::WordNetOntology::WordNetOntology().

Mat PLearn::loadADMat (  const string &  filename  )

Native AD format. Definition at line 1011 of file MatIO.cc. References PLearn::TMat< T >::data(), fread_float(), fread_int(), and PLERROR.

Vec PLearn::loadADVec (  const string &  filename  )

Definition at line 1031 of file MatIO.cc. References PLearn::TVec< T >::data(), fread_float(), fread_int(), and PLERROR.

template<class T> void PLearn::loadAscii (  const string &  filename, TVec< T > &  vec )

Definition at line 399 of file MatIO.h. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::clear(), PLearn::TVec< T >::end(), MISSING_VALUE, PLERROR, and PLearn::TVec< T >::resize().

template<class T> void PLearn::loadAscii (  const string &  filename, TMat< T > &  mat )

Definition at line 481 of file MatIO.h. References loadAscii().

template<class T> void PLearn::loadAscii (  const string &  filename, TMat< T > &  mat, TVec< string > &  fieldnames, TVec< map< string, real > > *  map_sr = 0 )

WARNING: use only for float, double, and int types. Other type are not guaranteed to work intelligent functions that will load a file in almost all ascii formats that ever existed in this lab. Definition at line 195 of file MatIO.h. References PLearn::TVec< T >::clear(), PLearn::TVec< T >::fill(), k, PLearn::TVec< T >::length(), map_sr, MISSING_VALUE, parseSizeFromRemainingLines(), pl_isnumber(), PLERROR, PLWARNING, removeblanks(), PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), skipBlanksAndComments(), split(), toint(), tostring(), val, and ws(). Referenced by cross_valid(), input2dSet(), loadAscii(), loadAsciiAsVMat(), loadATT800(), loadBreastCancer(), loadBreastCancerWisconsin(), loadCallxx(), loadDiabetes(), loadLetters(), loadMat(), and loadVec().

VMat PLearn::loadAsciiAsVMat (  const string &  filename  )

Load an ASCII file and return the corresponding VMat (this will be a MemoryVMatrix, since the entire file is loaded in memory). Definition at line 124 of file VMat.cc. References loadAscii(), map_sr, mtime(), and PLearn::TVec< T >::size(). Referenced by getDataSet(), and loadUCIAMat().

template<class T> void loadAsciiSingleBinaryDescriptor (  const string &  filename, TMat< T > &  mat )

Load an ASCII matrix whose format is: (entry_name, long_binary_dscriptor) with 'long_binary_dscriptor' being of the form '001100101011', each character being an entry of the matrix. (entry_name is ignored). Header must be: #size: length width Definition at line 341 of file MatIO.h. References PLERROR, removeblanks(), PLearn::TMat< T >::resize(), skipBlanksAndComments(), split(), toint(), and ws(). Referenced by getDataSet().

void PLearn::loadAsciiWithoutSize (  const string &  filename, const Mat &  mat )

Definition at line 875 of file MatIO.cc. References PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

void PLearn::loadAsciiWithoutSize (  const string &  filename, const Vec &  vec )

Reads and writes an ascii file without the size header (assuming that the size(length() and width()) is set). Definition at line 834 of file MatIO.cc. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::MatlabInterface::eigs_r11(), and matlabR11eigs().

int PLearn::loadATT800 (  VMat &  training_set, VMat &  test_set )

Definition at line 303 of file databases.cc. References PLearn::TMat< T >::column(), dbdir_name, PLearn::TMat< T >::length(), loadAscii(), normalizeDataSets(), shuffleRows(), PLearn::TMat< T >::subMatColumns(), PLearn::TMat< T >::subMatRows(), sum(), and PLearn::TMat< T >::width().

int PLearn::loadBreastCancer (  VMat &  training_set, VMat &  validation_set, VMat &  test_set, int  ntrain, int  nvalid, bool  uniq = true )

These calls return the number of classes... Definition at line 198 of file databases.cc. References dbdir_name, PLearn::TMat< T >::length(), loadAscii(), normalizeDataSets(), shuffleRows(), and PLearn::TMat< T >::subMatRows().

VMat PLearn::loadBreastCancerWisconsin (  bool  normalize = true, bool  uniq = true )

Definition at line 182 of file databases.cc. References dbdir_name, loadAscii(), normalize(), normalizeDataSet(), shuffleRows(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width(). Referenced by loadClassificationDataset().

void PLearn::loadCallxx (  int  year, VMat &  d )

Definition at line 667 of file databases.cc. References c_str(), dbdir_name, and loadAscii().

void PLearn::loadClassificationDataset (  const string &  dbname, int &  inputsize, int &  nclasses, VMat &  trainset, VMat &  testset, bool  normalizeinputs, VMat &  allset )

Definition at line 746 of file databases.cc. References computeMeanAndStddev(), input2dSet(), PLearn::TVec< T >::length(), PLearn::TMat< T >::load(), loadBreastCancerWisconsin(), loadLetters(), loadMNIST(), loadUCI(), loadUSPS(), normalize(), PLERROR, remapLastColumn(), split(), PLearn::VMat::subMatRows(), PLearn::TVec< T >::subVec(), toint(), PLearn::TMat< T >::width(), and PLearn::VMat::width(). Referenced by getDataSet().

string loadClassificationDatasetHelp (   )  [inline]

This will return a VMat with a target in the last column in {0,..,nclasses-1} (for binary classification possible values are 0 and 1 (not -1)). Possible dbname are: 2d letters breast usps mnist usps0 ... usps9 nist0 ... usps9 The dbname can optionally be followed by :size in which case only the 'size' first elements of trainset and testset will be kept. Definition at line 127 of file databases.h. Referenced by getDataSetHelp().

void PLearn::loadCorel (  Mat &  training_set, Mat &  validation_set, Mat &  test_set, int  negative_class = 2, int  positive_class = 3 )

Definition at line 615 of file databases.cc. References PLearn::TMat< T >::fill(), PLearn::TMat< T >::length(), loadCorelDatamat(), PLearn::TMat< T >::resize(), shuffleRows(), smoothCorelHisto(), PLearn::TMat< T >::subMat(), and PLearn::TMat< T >::width().

void PLearn::loadCorelDatamat (  int  classnum, Mat &  train, Mat &  valid, Mat &  test )

Definition at line 514 of file databases.cc. References c_str(), PLearn::TMat< T >::data(), dbdir_name, PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::resize(), reverse_float(), and PLearn::TMat< T >::width(). Referenced by loadCorel().

int PLearn::loadDiabetes (  VMat &  training_set, VMat &  validation_set, VMat &  test_set, int  ntrain, int  nvalid )

Definition at line 281 of file databases.cc. References dbdir_name, PLearn::TMat< T >::length(), loadAscii(), normalizeDataSets(), shuffleRows(), and PLearn::TMat< T >::subMatRows().

VMat PLearn::loadDiabetes (  bool  normalize = true  )

Definition at line 267 of file databases.cc. References dbdir_name, loadAscii(), normalize(), normalizeDataSet(), shuffleRows(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().

string PLearn::loadFileAsString (  const string &  filepath  )

Returns the whole content of the file as a string. Definition at line 335 of file fileutils.cc. References filesize(), and PLERROR. Referenced by PLearn::VMatrix::lockMetaDataDir(), and PLearn::VMatrix::resolveFieldInfoLink().

void PLearn::loadGnuplot (  const string &  filename, Mat &  mat )

Format readable by gnuplot. Definition at line 613 of file MatIO.cc. References PLERROR, and PLearn::TMat< T >::resize(). Referenced by loadHousing().

VMat PLearn::loadHousing (  bool  normalize = true  )

Definition at line 235 of file databases.cc. References dbdir_name, loadGnuplot(), normalize(), normalizeDataSet(), and PLearn::TMat< T >::subMatColumns().

VMat PLearn::loadIonosphere (   )

Definition at line 259 of file databases.cc. References dbdir_name, loadUCIMLDB(), and shuffleRows().

void PLearn::loadJPEGrgb (  const string &  jpeg_filename, Mat &  rgbmat, int &  row_size, int  scale = 1 )

read a file in JPEG format (read the RGB components). this will be resized to a (npixels x 3) matrix, where the (R,G,B) pixels are ordered by rows of the original image. To figure the actual image dimensions, the row size is also returned (so the number of columns is length()/row_size). An optional subsampling factor can be given (1,2,4 or 8) The R,G,B components always range from 0 to 255. Definition at line 1484 of file MatIO.cc. References PLearn::TMat< T >::data(), k, PLERROR, and PLearn::TMat< T >::resize().

void loadLetters (  int &  inputsize, int &  nclasses, VMat &  trainset, VMat &  testset )

Definition at line 736 of file databases.cc. References dbdir_name, loadAscii(), PLearn::TMat< T >::subMatRows(), and PLearn::TMat< T >::width().

int PLearn::loadLetters (  VMat &  training_set, VMat &  validation_set, VMat &  test_set, int  n_letters, real  validation_fraction = .2, real  test_fraction = .4, bool  do_shuffle = true )

Definition at line 494 of file databases.cc. References PLearn::VMat::length(), loadLetters(), normalizeDataSets(), and PLearn::VMat::subMatRows().

VMat PLearn::loadLetters (  int  n_letters, bool  do_shuffle )

Definition at line 455 of file databases.cc. References PLearn::TMat< T >::copy(), dbdir_name, k, PLearn::TMat< T >::length(), loadAscii(), Mat, PLERROR, PLearn::TMat< T >::resize(), shuffleRows(), and PLearn::TMat< T >::width().

int PLearn::loadLetters (  VMat &  training_set, VMat &  validation_set, VMat &  test_set, char *  letters = "ABCDEFGHIJKLMNOPQRSTUVWXYZ", real  validation_fraction = .2, real  test_fraction = .4, bool  do_shuffle = true )

Definition at line 406 of file databases.cc. References PLearn::TMat< T >::copy(), dbdir_name, k, PLearn::TMat< T >::length(), loadAscii(), Mat, normalizeDataSets(), PLearn::TMat< T >::resize(), shuffleRows(), strlen(), PLearn::TMat< T >::subMatRows(), and PLearn::TMat< T >::width().

VMat PLearn::loadLetters (  const char *  class0, const char *  class1, bool  normalize = true )

Definition at line 366 of file databases.cc. References dbdir_name, PLearn::TMat< T >::length(), loadAscii(), normalize(), normalizeDataSet(), strlen(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().

VMat PLearn::loadLetters (  bool  normalize = true  )

Definition at line 351 of file databases.cc. References dbdir_name, loadAscii(), normalize(), normalizeDataSet(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width(). Referenced by loadClassificationDataset(), and loadLetters().

void PLearn::loadMat (  const string &  filename, TMat< double > &  mat )

Definition at line 110 of file MatIO.cc. References extract_extension(), loadAscii(), loadPMat(), and PLERROR.

void PLearn::loadMat (  const string &  filename, TMat< float > &  mat )

Tries to guess the format... (quite dumb right now) This is currently what the old constructor with string argument did Definition at line 88 of file MatIO.cc. References extract_extension(), loadAscii(), loadPMat(), and PLERROR.

void loadMNIST (  VMat &  training_set, VMat &  test_set )  [inline]

Definition at line 71 of file NistDB.h. Referenced by loadClassificationDataset().

Object * PLearn::loadObject (  const string &  filename  )

Loads an object from the given file (no macro-preprocessing is performed). Definition at line 340 of file Object.cc. References PLearn::Object::build(), PLERROR, and readObject(). Referenced by readObject(), train_and_test(), and use().

VMat PLearn::loadPimaIndians (  bool  normalize = true  )

Definition at line 223 of file databases.cc. References dbdir_name, loadUCIMLDB(), normalize(), normalizeDataSet(), shuffleRows(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().

void PLearn::loadPMat (  const string &  filename, TMat< double > &  mat )

Definition at line 449 of file MatIO.cc. References DATAFILE_HEADERLENGTH, fread_double(), fread_float(), header, PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::resize(), and PLearn::TMat< T >::width().

void PLearn::loadPMat (  const string &  filename, TMat< float > &  mat )

Definition at line 393 of file MatIO.cc. References DATAFILE_HEADERLENGTH, fread_double(), fread_float(), header, PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::resize(), and PLearn::TMat< T >::width(). Referenced by PLearn::TMat< pair< real, real > >::load(), and loadMat().

void PLearn::loadPVec (  const string &  filename, TVec< double > &  vec )

Definition at line 276 of file MatIO.cc. References PLearn::TVec< T >::data(), DATAFILE_HEADERLENGTH, fread_double(), fread_float(), header, PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize().

void PLearn::loadPVec (  const string &  filename, TVec< float > &  vec )

Definition at line 226 of file MatIO.cc. References PLearn::TVec< T >::data(), DATAFILE_HEADERLENGTH, fread_double(), fread_float(), header, PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize(). Referenced by PLearn::TVec< pair< real, real > >::load(), and loadVec().

Mat PLearn::loadSNMat (  const string &  filename  )

SN Format. Definition at line 917 of file MatIO.cc. References fread_float(), fread_int(), and PLERROR. Referenced by loadUSPS().

Vec PLearn::loadSNVec (  const string &  filename  )

Definition at line 972 of file MatIO.cc. References PLearn::TVec< T >::data(), fread_float(), fread_int(), and PLERROR.

VMat PLearn::loadSonar (   )

Definition at line 251 of file databases.cc. References dbdir_name, loadUCIMLDB(), and shuffleRows().

Mat PLearn::loadSTATLOG (  const string &  filename, char ****  to_symbols = 0, int **  to_n_symbols = 0 )

STATLOG machine-learning-database-format Format used for most of the STATLOG machine-learning-database. The missing value is represented with the '?' character in the source file, and with the MISSING_VALUE in the Mat. If some symbols are detected then integer codes are assigned to them (by sorting them for each symbolic column in lexicographic order). The *to_symbols table has one element per column, each of which is a table of strings. The number of strings (i.e., symbols) for each column is given in the table *to_n_symbols. Definition at line 1298 of file MatIO.cc. References compare_string_pointers(), convert_STATLOG_BUF_LEN, cp(), PLearn::TMat< T >::data(), MISSING_VALUE, PLERROR, strlen(), and PLearn::TMat< T >::width().

VMat PLearn::loadToVMat (  string  file, string  name, int  window, int  n_examples )

Definition at line 463 of file GraphicalBiText.cc. References PLearn::ShellProgressBar::done(), PLearn::ShellProgressBar::draw(), endl(), PLearn::TMat< T >::length(), PLearn::VMat::length(), PLearn::ShellProgressBar::update(), and PLearn::VMat::width(). Referenced by PLearn::GraphicalBiText::init().

void PLearn::loadUCI (  VMat &  trainset, VMat &  testset, VMat &  allset, string  db_spec, string  id, bool &  normalize, const string &  type )

Load the train, test and all datasets for a UCI database. The 'normalize' parameter can be changed: if it is set to true in input, it may be changed to false when the method returns (this is because the data will already be normalized, and no additional normalization is needed). Definition at line 875 of file databases.cc. References dbdir_name, PLearn::VMat::length(), loadUCIAMat(), loadUCISet(), macroLoadObject(), normalize(), PLERROR, PLearn::TMat< T >::subMatColumns(), PLearn::TMat< T >::subMatRows(), PLearn::VMat::toMat(), vconcat(), and PLearn::VMat::width(). Referenced by loadClassificationDataset().

void PLearn::loadUCIAMat (  VMat &  data, string  file, PP< UCISpecification >  uci_spec )

Load a AMAT format UCI dataset in the given VMatrix. Definition at line 945 of file databases.cc. References PLearn::VMat::length(), loadAsciiAsVMat(), PLERROR, PLearn::TVec< T >::resize(), PLearn::TVec< T >::subVec(), and PLearn::VMat::width(). Referenced by loadUCI().

Mat PLearn::loadUCIMLDB (  const string &  filename, char ****  to_symbols = 0, int **  to_n_symbols = 0, TVec< int > *  max_in_col = 0, TVec< string > *  header_columns = 0 )

UCI machine-learning-database format Format used for most of the UCI machine-learning-database. The missing value is represented with the '?' character in the source file, and with the MISSING_VALUE in the Mat. If some symbols are detected then integer codes are assigned to them (by sorting them for each symbolic column in lexicographic order). The *to_symbols table has one element per column, each of which is a table of strings. The number of strings (i.e., symbols) for each column is given in the table *to_n_symbols. Additionally, if provided, the 'max_in_col' vector will contain the (rounded to lowest integer) value of the maxium in each column (this will be -1 if there is no numerical value in the column). Also, if 'header_columns' vector is provided, the first line is considered to be the header and the vector will contain the column names. Definition at line 1057 of file MatIO.cc. References PLearn::TVec< T >::append(), compare_string_pointers(), convert_UCIMLDB_BUF_LEN, cp(), PLearn::TMat< T >::data(), PLearn::TVec< T >::fill(), MISSING_VALUE, pl_isnumber(), PLERROR, PLearn::TVec< T >::resize(), strlen(), and PLearn::TMat< T >::width(). Referenced by loadIonosphere(), loadPimaIndians(), loadSonar(), and loadUCISet().

void PLearn::loadUCISet (  VMat &  data, string  file, PP< UCISpecification >  uci_spec )

Load a specific UCI dataset in the given VMatrix. Definition at line 991 of file databases.cc. References k, PLearn::TMat< T >::length(), loadUCIMLDB(), PLERROR, PLearn::TVec< T >::resize(), PLearn::TVec< T >::size(), PLearn::TVec< T >::subVec(), PLearn::VMat::width(), and PLearn::TMat< T >::width(). Referenced by loadUCI().

VMat PLearn::loadUSPS (  bool  use_smooth = true  )

Definition at line 713 of file databases.cc. References argmax(), dbdir_name, hconcat(), PLearn::TMat< T >::length(), and loadSNMat().

void PLearn::loadUSPS (  VMat &  trainset, VMat &  testset, bool  use_smooth = true )

Definition at line 677 of file databases.cc. References argmax(), dbdir_name, hconcat(), PLearn::TMat< T >::length(), and loadSNMat(). Referenced by loadClassificationDataset().

void PLearn::loadVec (  const string &  filename, TVec< double > &  vec )

Definition at line 145 of file MatIO.cc. References loadAscii(), loadPVec(), and PLERROR.

void PLearn::loadVec (  const string &  filename, TVec< float > &  vec )

Definition at line 133 of file MatIO.cc. References loadAscii(), loadPVec(), and PLERROR.

VMat local_neighbors_differences (  VMat  source, int  n_neighbors, bool  concat = false, bool  append_indexes = false )  [inline]

Definition at line 121 of file LocalNeighborsDifferencesVMatrix.h. References PLearn::LocalNeighborsDifferencesVMatrix::append_indexes, PLearn::LocalNeighborsDifferencesVMatrix::build(), concat(), PLearn::LocalNeighborsDifferencesVMatrix::concat_neighbors, PLearn::LocalNeighborsDifferencesVMatrix::n_neighbors, and PLearn::SourceVMatrix::source. Referenced by PLearn::NearestNeighborPredictionCost::run(), PLearn::TangentLearner::train(), and PLearn::GaussianContinuum::train().

string PLearn::locateDatasetAliasesDir (  const string &  dir_or_file_path = "."  )

Looks for 'dataset.aliases' file in specified directory and its parent directories; Returns the directory containing dataset.aliases (returned string will be terminated by a slash) or an empty string if not found. Definition at line 241 of file getDataSet.cc. References abspath(), dot(), extract_directory(), isfile(), pathexists(), PLERROR, remove_trailing_slash(), and slash. Referenced by getDatasetAliases().

Var log (  Var  v  )  [inline]

Definition at line 72 of file LogVariable.h.

RandomVar PLearn::log (  RandomVar  x  )

natural logarithm function applied element-by-element Definition at line 451 of file RandomVar.cc. References x.

template<class T> TVec<T> log (  const TVec< T > &  src  )  [inline]

Definition at line 876 of file TMat_maths_impl.h. References compute_log(), and PLearn::TVec< T >::length().

real PLearn::log (  real  base, real  a )

Definition at line 96 of file pl_math.cc. Referenced by bnldev(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::GaussMix::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::AsciiVMatrix::build_(), choleskyInvert(), PLearn::GraphicalBiText::compute_likelihood(), compute_log(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), PLearn::GaussMix::computePosteriors(), PLearn::MixtureRandomVariable::ElogP(), PLearn::ExpRandomVariable::EMBprop(), entropy(), PLearn::SigmoidPrimitiveKernel::evaluate(), PLearn::LogOfGaussianDensityKernel::evaluate(), PLearn::GaussianDensityKernel::evaluate(), PLearn::ConvexBasisKernel::evaluate(), expdev(), PLearn::NllSemisphericalGaussianVariable::fprop(), PLearn::NegCrossEntropySigmoidVariable::fprop(), PLearn::CrossEntropyVariable::fprop(), gamdev(), gauss_log_density_stddev(), gauss_log_density_var(), gaussian_01(), geometric_mean(), PLearn::GaussianContinuum::get_image_matrix(), incomplete_beta(), PLearn::GraphicalBiText::init(), inverse_sigmoid(), inverse_softplus(), PLearn::ProductRandomVariable::invertible(), PLearn::ExpRandomVariable::invertible(), PLearn::HistogramDistribution::log_density(), PLearn::ConditionalDensityNet::log_density(), log_gamma(), logOfCompactGaussian(), logOfNormal(), PLearn::MultinomialRandomVariable::logP(), PLearn::MixtureRandomVariable::logP(), PLearn::DiagonalNormalRandomVariable::logP(), logtwo(), neg_log_pi(), normal(), pl_dgammlndz(), pl_gammln(), pl_gcf(), pl_gser(), poidev(), positive_dilogarithm(), PLearn::GaussMix::precomputeStuff(), PLearn::VMatLanguage::run(), safeflog(), PLearn::GaussMix::setInput(), PLearn::LogRandomVariable::setValueFromParentsValue(), sum_of_log(), PLearn::PowVariableVariable::symbolicBprop(), PLearn::PLogPVariable::symbolicBprop(), PLearn::GraphicalBiText::test_WSD(), PLearn::GaussianContinuum::train(), and PLearn::ClassifierFromDensity::train().

real PLearn::log_beta (  real  x, real  y )

returns the natural logarithm of the beta function Definition at line 89 of file random.cc. References log_gamma(), and x. Referenced by incomplete_beta().

real PLearn::log_gamma (  real  x  )

returns the natural logarithm of the gamma function Definition at line 69 of file random.cc. References log(), and x. Referenced by bnldev(), log_beta(), and poidev().

Var log_softmax (  Var  v  )  [inline]

Definition at line 72 of file LogSoftmaxVariable.h.

template<class T> void log_softmax (  const TVec< T > &  x, TVec< T > &  y )

Definition at line 96 of file TMat_maths_impl.h. References logadd(), max(), and x. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), and PLearn::LogSoftmaxVariable::fprop().

Var logadd (  Var  input  )  [inline]

Definition at line 73 of file LogSumVariable.h.

Var logadd (  Var &  input1, Var &  input2 )  [inline]

Definition at line 77 of file LogAddVariable.h.

template<class T> T logadd (  const TVec< T > &  vec  )

Definition at line 1282 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), LOG_INIT, logadd(), and sum().

real PLearn::logadd (  real  log_a, real  log_b )

compute log(exp(log_a)+exp(log_b)) without losing too much precision Definition at line 117 of file pl_math.cc. References exp(), and MINUS_LOG_THRESHOLD. Referenced by PLearn::ClassifierFromDensity::computeOutput(), PLearn::GaussMix::computePosteriors(), determine_density_integral_from_log_densities_on_grid(), DX_create_grid_outputs_file(), PLearn::MixtureRandomVariable::ElogP(), PLearn::LogSumVariable::fprop(), PLearn::LogAddVariable::fprop(), PLearn::GaussMix::log_density(), log_softmax(), logadd(), PLearn::MixtureRandomVariable::logP(), and PLearn::GaussMix::setInput().

real PLearn::logOfCompactGaussian (  const Vec &  x, const Vec &  mu, const Vec &  eigenvalues, const Mat &  eigenvectors, real  gamma = 1e-6, bool  add_gamma_to_eigenval = false )

returns log P(x|gaussian) with a gaussian represented compactly by the first few eigenvalues and eigenvectors of its covariance matrix. For numerical stability, you may consider adding some lambda to the diagonal of C Normal(x; mu,C) = 1/sqrt((2PI)^d * det(C)) exp( -0.5 (x-mu)'.inv(C).(x-mu) ) = exp [ -0.5( d*log(2PI) + log(det(D)) ) -0.5 (x-mu)'.inv(C).(x-mu) ] ____________/ ________/ \/ \/ logcoef q Let z = inv(C).(x-mu) ==> z is the solution of C.z = x-mu And then we have q = (x-mu)'.z So computing q is simply a matter of solving this linear equation in z, and then computing q. Definition at line 167 of file distr_maths.cc. References PLearn::TVec< T >::data(), dot(), PLearn::TVec< T >::length(), log(), PLearn::TVec< T >::resize(), square(), val, and x. Referenced by PLearn::GaussianDistribution::log_density().

real PLearn::logOfNormal (  const Vec &  x, const Vec &  mu, const Mat &  C )

Definition at line 211 of file distr_maths.cc. References choleskyDecomposition(), choleskySolve(), dot(), log(), PLearn::TVec< T >::resize(), substract(), and x. Referenced by logPFittedGaussian().

Var PLearn::logP (  ConditionalExpression  conditional_expression, bool  clearMarksUponReturn = true, RVInstanceArray *  parameters_to_learn = 0 )

Construct a Var that computes logP(RandomVariable == value | RHS ) in terms of the value Var and the Vars in the RHS, where RHS is a list of the form (X1==x1 && X2==x2 && X3==x3) where Xi are RandomVar's and xi are Var's which represent the value that are given to the conditioning variables Xi. Normally the marks used to identify RVs which are deterministically determined from the RHS are cleared upon return (unless specified with the optional 2nd argument). Definition at line 616 of file RandomVar.cc. References PLearn::ConditionalExpression::LHS, PLearn::ConditionalExpression::RHS, PLearn::TVec< RVInstance >::size(), PLearn::RVInstance::v, and PLearn::RVInstance::V. Referenced by PLearn::MixtureRandomVariable::ElogP(), ElogP(), EM(), PLearn::MultinomialRandomVariable::logP(), PLearn::MixtureRandomVariable::logP(), PLearn::DiagonalNormalRandomVariable::logP(), PLearn::FunctionalRandomVariable::logP(), and PLearn::RandomVariable::P().

real PLearn::logPFittedGaussian (  const Vec &  x, const Mat &  X, real  lambda )

Fits a gaussian to the points in X (computing its mean and covariance matrix, and adding lambda to the diagonal of that covariance matrix) Then calls logOfNormal to return log(p(x | the_gaussian)). Definition at line 242 of file distr_maths.cc. References addToDiagonal(), computeMeanAndCovar(), logOfNormal(), and x.

real PLearn::logsub (  real  log_a, real  log_b )

compute log(exp(log_a)-exp(log_b)) without losing too much precision Definition at line 135 of file pl_math.cc. References exp(), FEQUAL, MINUS_LOG_THRESHOLD, and PLERROR.

real PLearn::logtwo (  real  a  )

Definition at line 101 of file pl_math.cc. References log(), and LOG_2.

bool PLearn::looksNumeric (  const char *  s  )

tells wether this string looks like a numeric entity Definition at line 107 of file TypesNumeriques.cc. References containsChar(), and DIGITsymbols. Referenced by PLearn::MatlabInterface::eigs_r11(), main(), matlabR11eigs(), and numericType().

string PLearn::lowerstring (  const string &  s  )

convert a string to all lowercase Definition at line 263 of file stringutils.cc. Referenced by PLearn::SimpleDB< KeyType, QueryResult >::loadSchema(), and main().

vector< string > PLearn::lsdir (  const string &  dirpath  )

Returns a list of all entries in the given directory (omitting entries "." and "..") If the direcotry cannot be opened an error is issued. The returned entries are not full paths. Definition at line 175 of file fileutils.cc. References PLERROR. Referenced by lsdir_fullpath(), and train_and_test().

vector< string > PLearn::lsdir_fullpath (  const string &  dirpath  )

Same as lsdir, except dirpath is prepended to the entries' names. Definition at line 244 of file fileutils.cc. References addprefix(), lsdir(), remove_trailing_slash(), and slash. Referenced by force_rmdir().

template<class T> void LU_decomposition (  TMat< T > &  A, TVec< T > &  Trow, int &  detsign, TVec< T > *  p = 0 )

Definition at line 5173 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), k, PLearn::TMat< T >::length(), maxabs(), PLearn::TMat< T >::mod(), PLERROR, PLearn::TMat< T >::swapRows(), and PLearn::TMat< T >::width(). Referenced by det().

Object * PLearn::macroLoadObject (  const string &  filename  )

same as previous, but no need to pass a variables map Definition at line 361 of file Object.cc. References macroLoadObject().

Object * PLearn::macroLoadObject (  const string &  filename, map< string, string > &  vars )

Same as loadObject but first performs macro-processing on the file vars may be initialised with the values of some variables and upon return it will also contain newly $DEFINED variables. Definition at line 352 of file Object.cc. References PLearn::Object::build(), readFileAndMacroProcess(), and readObject(). Referenced by loadUCI(), and macroLoadObject().

template<class T> T mahalanobis_distance (  const TVec< T > &  input, const TVec< T > &  meanvec, const TMat< T > &  inversecovmat )

Definition at line 4282 of file TMat_maths_impl.h. References dot(), and product().

string PLearn::makeExplicitPath (  const string &  filename  )

returns "./"+filename if filename is relative to current dir Definition at line 573 of file fileutils.cc. References dot(), removeblanks(), slash, and slash_char. Referenced by PLearn::VVMatrix::build_().

string PLearn::makeFileNameValid (  const string &  filename  )

Definition at line 525 of file fileutils.cc. References extract_directory(), extract_extension(), extract_filename_without_extension(), pathexists(), PLWARNING, and tostring(). Referenced by PLearn::VMatrix::getSFIFFilename(), PLearn::VMatrix::isSFIFDirect(), and PLearn::VMatrix::setSFIFFilename().

template<class T> void makeItSymmetric (  const TMat< T > &  mat, T  max_dif )

Definition at line 2820 of file TMat_maths_impl.h. References PLearn::TMat< T >::isSquare(), PLearn::TMat< T >::length(), PLERROR, PLWARNING, and PLearn::TMat< T >::width().

Mat makeMat (  int  length, int  width, const string &  values )  [inline]

convenience construction from string allows to write things such as Mat m = newMat(2,2, "1 2 3 4") Definition at line 58 of file MatIO.h.

template<class T> void makeRowsSumTo1 (  const TMat< T > &  mat  )

Definition at line 3450 of file TMat_maths_impl.h. References divide(), PLearn::TMat< T >::length(), and sum().

Vec makeVec (  int  length, const string &  values )  [inline]

Definition at line 61 of file MatIO.h.

void PLearn::manual_seed (  long  x  )

initialzes the random number generator with the given long "x" Definition at line 185 of file random.cc. References iset, the_seed, and x. Referenced by PLearn::RepeatSplitter::build_(), PLearn::AdaBoost::forget(), PLearn::VVMatrix::generateVMatIndex(), PLearn::TangentLearner::initializeParams(), PLearn::NNet::initializeParams(), PLearn::NeuralNet::initializeParams(), PLearn::NeighborhoodSmoothnessNNet::initializeParams(), PLearn::MultiInstanceNNet::initializeParams(), PLearn::GaussianContinuum::initializeParams(), PLearn::ConditionalDensityNet::initializeParams(), PLearn::UniformDistribution::resetGenerator(), PLearn::SpiralDistribution::resetGenerator(), PLearn::GaussMix::resetGenerator(), PLearn::GaussianDistribution::resetGenerator(), PLearn::ConditionalDensityNet::resetGenerator(), seed(), and PLearn::EntropyContrast::train().

Var margin_perceptron_cost (  Var  output, Var  target, real  margin )  [inline]

Definition at line 83 of file MarginPerceptronCostVariable.h. Referenced by PLearn::NNet::build_().

RandomVar PLearn::marginalize (  const RandomVar &  RV, const RandomVar &  hiddenRV )

integrate the RV over the given hiddenRV and return the resulting new RandomVariable. This may be difficult to do in general... Definition at line 653 of file RandomVar.cc. References PLERROR. Referenced by PLearn::MixtureRandomVariable::ElogP(), PLearn::MultinomialRandomVariable::logP(), PLearn::MixtureRandomVariable::logP(), PLearn::DiagonalNormalRandomVariable::logP(), and PLearn::FunctionalRandomVariable::logP().

template<class T> T matColumnDotVec (  const TMat< T > &  mat, int  j, const TVec< T >  v )

return dot product of j-th column with vector v Definition at line 2731 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::mod(), and PLERROR.

int PLearn::matInvert (  Mat &  in, Mat &  inverse )

This function compute the inverse of a matrix. WARNING: the input matrix 'in' is overwritten in the process. Definition at line 219 of file plapack.cc. References PLearn::TMat< T >::data(), dgetrf_(), dgetri_(), endl(), inverse(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, sgetrf_(), sgetri_(), and PLearn::TMat< T >::width(). Referenced by PLearn::PCA::train().

void matlabR11eigs (  RowMapSparseMatrix< double > &  A, Mat  eigen_vectors, Vec  eigen_values, string  which_eigenvalues = "LM" )

Compute k eigen-values / eigen-vectors of a sparse symmetric matrix using the eigs program of matlab-r11 (see PLearn/Contrib/matlab/eigs_r11.m). The 'which_eigenvalues' argument specifies which eigenvalues are desired: a number the k eigen-values closest to that number "LM" Largest Magnitude (the default) "SM" Smallest Magnitude "LR" Largest Real part "SR" Smallest Real part "BE" Both Ends. Computes k/2 eigenvalues from each end of the spectrum (one more from the high end if k is odd.) where k is the length of the eigen_values vector. If eigen_vectors.length()==0 then only the eigen_values are computed. N.B. in comparison with other methods available in PLearn or elsewhere, this function is particularly useful when dealing with symmetric sparse matrices whose smallest eigen-pairs are sought.

void matlabR11eigs (  RowMapSparseMatrix< real > &  A, Mat  eigen_vectors, Vec  eigen_values, string  which_eigenvalues )

Definition at line 144 of file MatlabInterface.cc. References PLearn::TmpFilenames::addFilename(), PLearn::RowMapSparseMatrix< T >::exportToMatlabReadableFormat(), extract_filename(), header, PLearn::Popen::in, PLearn::MatlabInterface::launchAndWaitFor(), PLearn::RowMapSparseMatrix< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), loadAsciiWithoutSize(), looksNumeric(), PLearn::MatlabInterface::matlab, PLearn::MatlabInterface::matlab_file_header, PLERROR, PLWARNING, remove_extension(), PLearn::TMat< T >::resize(), PLearn::TVec< T >::resize(), toint(), and tostring().

void PLearn::matlabSave (  const string &  dir, const string &  plot_title, const Vec &  xValues, const Mat &  yValues, const Vec &  add_col, const Vec &  bounds, TVec< string >  legend = TVec<string>(), bool  save_plot = true )

This is the *real* matlabSave function. 1) If xValues is empty, the yValues are plotted against the row indices. 2) If xValues is not empty and its length is not equal to the length of yValues, then its length must be one and the value xValues[0] will be the start index for the xValues. Definition at line 710 of file MatIO.cc. References abspath(), add(), append_slash(), endl(), force_mkdir(), is_missing(), PLearn::TVec< T >::isNotEmpty(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), MISSING_VALUE, PLERROR, PLearn::TVec< T >::resize(), tostring(), underscore_to_space(), and PLearn::TMat< T >::width().

void PLearn::matlabSave (  const string &  dir, const string &  plot_title, const Mat &  data, const Vec &  add_col, const Vec &  bounds, TVec< string >  legend = TVec<string>(), bool  save_plot = true )

Simply calls the coming matlabSave function with an empty xValues Vec. See below. Definition at line 695 of file MatIO.cc. References matlabSave().

void PLearn::matlabSave (  const string &  dir, const string &  plot_title, const Vec &  xValues, const Vec &  yValues, const Vec &  add_col, const Vec &  bounds, string  lengend = "", bool  save_plot = true )

Definition at line 681 of file MatIO.cc. References PLearn::TVec< T >::append(), PLearn::TVec< T >::length(), and matlabSave().

void PLearn::matlabSave (  const string &  dir, const string &  plot_title, const Vec &  data, const Vec &  add_col, const Vec &  bounds, string  lengend = "", bool  save_plot = true )

The following two are simply calling the matrix version after transforming the Vec in a one column Mat. See below. Definition at line 667 of file MatIO.cc. References PLearn::TVec< T >::append(), and PLearn::TVec< T >::length(). Referenced by PLearn::SequentialModelSelector::matlabSave(), PLearn::SequentialLearner::matlabSave(), and matlabSave().

Var matrixElements (  Var  expression, const Var &  i, const Var &  j, int  ni, int  nj, const VarArray &  parameters )  [inline]

Definition at line 94 of file MatrixElementsVariable.h.

Var matrixIndex (  Var  mat, Var  index )  [inline]

Definition at line 77 of file ColumnIndexVariable.h. References PLERROR, and PLearn::Var::width(). Referenced by neg_log_pi().

Var matrixInverse (  Var  v  )  [inline]

Definition at line 73 of file MatrixInverseVariable.h.

template<class T> T matRowDotVec (  const TMat< T > &  mat, int  i, const TVec< T >  v )

return dot product of i-th row with vector v Definition at line 2714 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, PLearn::TMat< T >::rowdata(), and PLearn::TMat< T >::width().

template<class T> void matRowsDots (  TVec< T >  v, const TMat< T > &  A, const TMat< T > &  B )

return dot products of i-th row of A with i-th row of B in vector v Definition at line 2748 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void matRowsDotsAcc (  TVec< T >  v, const TMat< T > &  A, const TMat< T > &  B )

return dot products of i-th row of A with i-th row of B in vector v Definition at line 2776 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

Var max (  Var  v  )  [inline]

Definition at line 73 of file MaxVariable.h.

Var max (  Var  v1, Var  v2 )  [inline]

Definition at line 81 of file Max2Variable.h.

template<class T> T max (  const TMat< T > &  mat  )

Definition at line 3930 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void max (  const TVec< T > &  source1, T  source2, TVec< T > &  destination )

Definition at line 1569 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), MAX, and PLearn::TVec< T >::resize().

template<class T> void max (  const TVec< T > &  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1552 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), MAX, PLERROR, and PLearn::TVec< T >::resize().

template<class T> T max (  const TVec< T > &  vec  )

Definition at line 56 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::UniformizeVMatrix::build_(), PLearn::MovingAverageVMatrix::build_(), PLearn::FinancePreprocVMatrix::build_(), PLearn::CumVMatrix::build_(), PLearn::AddCostToLearner::build_(), PLearn::RealMapping::checkConsistency(), columnMax(), PLearn::AddCostToLearner::computeCostsFromOutputs(), PLearn::GaussMix::computeLogLikelihood(), computeRange(), displayDecisionSurface(), PLearn::DiagonalNormalRandomVariable::EMUpdate(), PLearn::ConcatRowsVMatrix::ensureMappingsConsistency(), PLearn::ClassMarginCostFunction::evaluate(), PLearn::ClassDistanceProportionCostFunction::evaluate(), PLearn::Gnuplot::featureplot(), PLearn::ConcatRowsVMatrix::fullyCheckMappings(), PLearn::GeneralizedOneHotVMatrix::GeneralizedOneHotVMatrix(), PLearn::GaussMix::generateFromGaussian(), PLearn::JoinVMatrix::getNewRow(), PLearn::Gnuplot::histoplot(), log_softmax(), PLearn::RealMapping::maxMappedToValue(), PLearn::ScaledGradientOptimizer::optimize(), PLearn::ConjGradientOptimizer::optimize(), PLearn::ConjGradientOptimizer::optimizeN(), PLearn::GaussMix::precomputeStuff(), PLearn::ArrayAllocatorTrivial< T, SizeBits >::resize(), PLearn::ArrayAllocator< T, SizeBits >::resize(), PLearn::RGBImageVMatrix::RGBImageVMatrix(), rowMax(), PLearn::TestDependenciesCommand::run(), PLearn::ShellProgressBar::set(), PLearn::PDistribution::setConditionalFlagsWithoutUpdate(), PLearn::SourceVMatrix::setMetaInfoFromSource(), PLearn::ShellProgressBar::ShellProgressBar(), softmax(), PLearn::GraphicalBiText::test_WSD(), PLearn::TestDependenciesCommand::TestDependenciesCommand(), PLearn::EmbeddedSequentialLearner::train(), PLearn::GaussMix::updateFromConditionalSorting(), PLearn::Function::verifyGradient(), PLearn::Function::verifyHessian(), PLearn::Function::verifyrfprop(), viewVMat(), and vmatmain().

real PLearn::max_cdf_diff (  Vec &  v1, Vec &  v2 )

Returns the max of the difference between the empirical cdf of 2 series of values Side-effect: the call sorts v1 and v2. Definition at line 140 of file stats_utils.cc. References PLearn::TVec< T >::length(), and sortElements(). Referenced by KS_test().

StatsIt max_stats (   )  [inline]

Definition at line 412 of file StatsIterator.h.

template<class T> T maxabs (  const TVec< T > &  vec  )

Definition at line 497 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by det(), and LU_decomposition().

Var PLearn::mean (  Var  v  )

******************************* user-friendly Var interface * Definition at line 57 of file Var_utils.cc. References sum().

template<class T> T mean (  const TMat< T > &  mat  )

Definition at line 3808 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> T mean (  const TVec< T > &  vec, bool  ignore_missing = false )

if ignore_missing==true, then the mean is computed by ignoring the possible MISSING_VALUE in the Vec. if ignore_missing==false, then MISSING_VALUE is returned if one element of the Vec is MISSING_VALUE. Definition at line 301 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), MISSING_VALUE, and PLERROR. Referenced by columnWeightedVariance(), computeConditionalMeans(), computeLocalPrincipalComponents(), PLearn::GaussMix::computeMeansAndCovariances(), computeRowMean(), PLearn::DiagonalNormalRandomVariable::DiagonalNormalRandomVariable(), PLearn::DiagonalNormalRandomVariable::EMUpdate(), PLearn::EmpiricalDistribution::expectation(), fill_random_normal(), PLearn::StddevStatsIterator::finish(), PLearn::StatsCollector::getAllValuesMapping(), PLearn::FinancePreprocVMatrix::getNewRow(), normal(), PLearn::ScaledGradientOptimizer::optimize(), paired_t_test(), PLearn::SequentialModelSelector::paired_t_test(), rowMean(), PLearn::TestDependenciesCommand::run(), PLearn::SequentialModelSelector::sequenceCost(), PLearn::GaussianProcessRegressor::setInput_const(), PLearn::TestDependenciesCommand::TestDependenciesCommand(), PLearn::GaussianProcessRegressor::train(), PLearn::EmpiricalDistribution::variance(), and vmatmain().

StatsIt mean_stats (   )  [inline]

Definition at line 408 of file StatsIterator.h. Referenced by PLearn::Learner::Learner().

Var meanOf (  Var  output, const VarArray &  inputs, VMat  distr, int  nsamples, VarArray  parameters = VarArray() )  [inline]

deprecated old version do not use! Definition at line 107 of file SumOfVariable.h. References meanOf().

Var meanOf (  VMat  distr, Func  f, int  nsamples )  [inline]

meanOf Definition at line 103 of file SumOfVariable.h.

Var meanOf (  VMat  distr, Func  f, int  nsamples, int  input_size )  [inline]

Definition at line 100 of file MatrixSumOfVariable.h. Referenced by EM(), meanOf(), PLearn::TangentLearner::train(), PLearn::NNet::train(), PLearn::NeuralNet::train(), PLearn::GaussianContinuum::train(), and PLearn::ConditionalDensityNet::train().

template<class T> T median (  const TVec< T > &  vec  )  [inline]

returns the median value of vec Definition at line 1880 of file TMat_maths_impl.h. References kthOrderedElement(), and PLearn::TVec< T >::length().

void * PLearn::MemoryMap (  const char *  filename, tFileHandle &  handle, bool  read_only, off_t &  filesize )

returns a pointer to the memory-mapped file or 0 if it fails for some reason. Definition at line 116 of file MemoryMap.cc. References filesize(), open, PLERROR, and tFileHandle. Referenced by PLearn::Storage< pair< real, real > >::Storage().

void PLearn::memoryUnmap (  void *  mapped_pointer, tFileHandle  handle, int  length )

Definition at line 141 of file MemoryMap.cc. Referenced by PLearn::Storage< pair< real, real > >::pointTo(), and PLearn::Storage< pair< real, real > >::~Storage().

void merge (  Set  a, Set  b, Set  res )  [inline]

Definition at line 86 of file Set.h. References PLearn::Set::begin(), and PLearn::Set::end().

Var min (  Var  v  )  [inline]

Definition at line 71 of file MinVariable.h.

template<class T> T min (  const TMat< T > &  mat  )

Definition at line 3914 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void min (  const TVec< T > &  source1, T  source2, TVec< T > &  destination )

Definition at line 1600 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), MIN, and PLearn::TVec< T >::resize().

template<class T> void min (  const TVec< T > &  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1583 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), MIN, PLERROR, and PLearn::TVec< T >::resize().

template<class T> T min (  const TVec< T > &  vec  )

Definition at line 482 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::UniformizeVMatrix::build_(), PLearn::AddCostToLearner::build_(), columnMin(), PLearn::GeodesicDistanceKernel::computeNearestGeodesicNeighbour(), computeRange(), displayDecisionSurface(), PLearn::GeodesicDistanceKernel::evaluate(), PLearn::ConjGradientOptimizer::fletcherSearchMain(), PLearn::GeneralizedOneHotVMatrix::GeneralizedOneHotVMatrix(), PLearn::MovingAverageVMatrix::getNewRow(), PLearn::JoinVMatrix::getNewRow(), lapackSVD(), PLearn::ConjGradientOptimizer::optimize(), PLearn::ConjGradientOptimizer::optimizeN(), PLearn::PDateTime::PDateTime(), PLearn::ArrayAllocatorTrivial< T, SizeBits >::resize(), PLearn::ArrayAllocator< T, SizeBits >::resize(), PLearn::RGBImageVMatrix::RGBImageVMatrix(), rowMin(), PLearn::TestDependenciesCommand::run(), PLearn::NearestNeighborPredictionCost::run(), PLearn::ShellProgressBar::set(), PLearn::GeodesicDistanceKernel::setDataForKernelMatrix(), PLearn::ShellProgressBar::ShellProgressBar(), sortColumns(), PLearn::TestDependenciesCommand::TestDependenciesCommand(), PLearn::GaussianDistribution::train(), PLearn::ConditionalDensityNet::train(), viewVMat(), and vmatmain().

StatsIt min_stats (   )  [inline]

Definition at line 411 of file StatsIterator.h.

template<class T> T minabs (  const TVec< T > &  vec, int  index = int() )

Definition at line 533 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

Var minus (  Var  v, Var  w )  [inline]

Definition at line 77 of file MinusVariable.h. Referenced by PLearn::NeighborhoodSmoothnessNNet::build_(), and PLearn::FieldConvertCommand::FieldConvertCommand().

RandomVar PLearn::mixture (  RVArray  components, RandomVar  log_weights )

A mixture of distributions, with the given components and the convex weights given by weights = softmax(log_weights). Note that the log_weights argument represents unnormalized log-probabilities (i.e normalization is automatically done inside the mixture). Definition at line 736 of file RandomVar.cc. Referenced by PLearn::RVArrayRandomElementRandomVariable::logP().

time_t PLearn::mtime (  const string &  path  )

returns the time of last modification of file (or 0 if file does not exist). Definition at line 162 of file fileutils.cc. Referenced by PLearn::MultiInstanceVMatrix::build_(), PLearn::FileVMatrix::build_(), PLearn::DiskVMatrix::build_(), PLearn::VVMatrix::buildFilteredVMatFromVPL(), PLearn::VVMatrix::createPreproVMat(), getDataSetDate(), getDateOfCode(), PLearn::VVMatrix::getDateOfVMat(), PLearn::VMatrix::getStats(), PLearn::VVMatrix::isPrecomputedAndUpToDate(), loadAsciiAsVMat(), PLearn::FilteredVMatrix::openIndex(), PLearn::VMatLanguage::preprocess(), and PLearn::AutoRunCommand::run().

Var multiclass_loss (  Var  network_output, Var  targets )  [inline]

Definition at line 81 of file MulticlassLossVariable.h. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), and PLearn::NeighborhoodSmoothnessNNet::build_().

RandomVar PLearn::multinomial (  RandomVar  log_probabilities  )

A discrete probability distribution which assigns probabilities[i] = softmax(log_probabilities)[i] to each of the discrete values i=0, 1, ... N-1. Note that the argument represents unnormalized log-probabilities (i.e normalization is automatically done inside the multinomial). Definition at line 741 of file RandomVar.cc.

int PLearn::multinomial_sample (  const Vec &  distribution  )

returns a random deviate from a discrete distribution given explicitely by 'distribution' Definition at line 536 of file random.cc. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and uniform_sample(). Referenced by PLearn::MultinomialSampleVariable::fprop(), PLearn::GaussMix::generateFromGaussian(), and PLearn::CompactVMatrix::perturb().

template<class T> void multiply (  const TMat< T > &  result, const TMat< T > &  x, T  scale )

Definition at line 3462 of file TMat_maths_impl.h. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::compact_begin(), PLearn::TMat< T >::compact_end(), PLearn::TMat< T >::end(), PLearn::TMat< T >::isCompact(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::width(), and x.

template<class T> void multiply (  const TVec< T > &  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1418 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize().

template<class T> void multiply (  const TVec< T > &  source1, T  source2, TVec< T > &  destination )  [inline]

destination = source1*source2 Definition at line 230 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLearn::TVec< T >::resize(). Referenced by computeMeanAndVariance(), divide(), PLearn::MultinomialRandomVariable::EMUpdate(), PLearn::MixtureRandomVariable::EMUpdate(), PLearn::DiagonalNormalRandomVariable::EMUpdate(), PLearn::MinusRandomVariable::EMUpdate(), PLearn::PlusRandomVariable::EMUpdate(), PLearn::ProjectionErrorVariable::fprop(), PLearn::GaussianProcessRegressor::inverseCovTimesVec(), operator *(), operator/(), and PLearn::ConstantRegressor::train().

template<class T> void multiplyAcc (  const TMat< T > &  mat, const TMat< T > &  x, const TMat< T > &  y )

Definition at line 3533 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::width(), and x.

template<class T> void multiplyAcc (  const TMat< T > &  mat, const TMat< T > &  x, T  scale )

Definition at line 3506 of file TMat_maths_impl.h. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::compact_begin(), PLearn::TMat< T >::compact_end(), PLearn::TMat< T >::end(), PLearn::TMat< T >::isCompact(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::width(), and x.

template<class T> void multiplyAcc (  const TVec< T > &  vec, const TVec< T > &  x, const TVec< T > &  y )

vec[i] += x[i]*y[i]; Definition at line 2110 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x.

template<class T> void multiplyAcc (  const TVec< T > &  vec, const TVec< T > &  x, T  scale )

vec[i] += x[i]*scale; Definition at line 2043 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::ProjectionErrorVariable::bprop(), PLearn::LogSumVariable::bprop(), PLearn::AffineTransformWeightPenalty::bprop(), computeInputMean(), computeInputMeanAndCovar(), computeInputMeanAndVariance(), computeWeightedMeanAndCovar(), diagonalizeSubspace(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::ProductRandomVariable::EMBprop(), PLearn::MinusRandomVariable::EMBprop(), PLearn::PlusRandomVariable::EMBprop(), PLearn::VMatrix::evaluateKernelWeightedTargetSum(), projectOnOrthogonalSubspace(), and PLearn::PCA::reconstruct().

template<class T> void multiplyAdd (  const TVec< T > &  source1, const TVec< T > &  source2, T  source3, TVec< T > &  destination )

Definition at line 1435 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize(). Referenced by PLearn::GaussianProcessRegressor::inverseCovTimesVec(), and PLearn::ConstantRegressor::train().

template<class T> void multiplyScaledAdd (  const TVec< T > &  source, T  a, T  b, TVec< T > &  destination )

Definition at line 1453 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR.

void PLearn::multivariate_normal (  Vec &  x, const Vec &  mu, const Vec &  e_values, const Mat &  e_vectors, Vec &  z )

generate a vector x sampled from the normal with mean mu and covariance matrix A = evectors * diagonal(e_values) * evectors' (the normal(0,I) originally sampled to obtain x is stored in z). Unlike the other variants of this function, this one does not allocate anything. Definition at line 444 of file plapack.cc. References gaussian_01(), PLearn::TVec< T >::length(), PLearn::TVec< T >::resize(), sqrt(), and x.

Vec PLearn::multivariate_normal (  const Vec &  mu, const Vec &  e_values, const Mat &  e_vectors )

generate 1 vector sampled from the normal with mean mu and covariance matrix A = evectors * diagonal(e_values) * evectors' Definition at line 429 of file plapack.cc. References gaussian_01(), PLearn::TVec< T >::length(), sqrt(), and x.

Vec PLearn::multivariate_normal (  const Vec &  mu, const Mat &  A )

generate a vector sampled from the normal with mean vector mu and covariance matrix A Definition at line 424 of file plapack.cc. References multivariate_normal(), and PLearn::TMat< T >::toVec().

Mat PLearn::multivariate_normal (  const Vec &  mu, const Mat &  A, int  N )

generate N vectors sampled from the normal with mean vector mu and covariance matrix A Definition at line 411 of file plapack.cc. References PLearn::TMat< T >::appendRow(), PLearn::TMat< T >::copy(), eigen_SymmMat(), and PLearn::TVec< T >::length(). Referenced by PLearn::ConditionalGaussianDistribution::generate(), and multivariate_normal().

void PLearn::mv (  const string &  file  )

calls system mv command with string file as parameters Definition at line 390 of file fileutils.cc. Referenced by PLearn::DiagonalNormalRandomVariable::EMUpdate(), and PLearn::VVMatrix::generateVMatIndex().

void PLearn::mvforce (  const string &  file  )

calls system mv command with string file as parameters will not prompt before overwriting Definition at line 396 of file fileutils.cc. Referenced by PLearn::VVMatrix::createPreproVMat().

real mypow (  real  x, real  p )  [inline]

Definition at line 258 of file pl_math.h. References pow(), and x. Referenced by PLearn::PowVariable::bprop(), PLearn::SDBVMFieldSignedPower::convertField(), dist(), PLearn::PowVariable::fprop(), norm(), powdistance(), pownorm(), weighted_distance(), and weighted_powdistance().

int n_choose (  int  M, int  N )  [inline]

Return M choose N, i.e., M! / ( N! (M-N)! ). Definition at line 375 of file pl_math.h. References k.

Var PLearn::neg_log_pi (  Var  p, Var  index )

Definition at line 60 of file Var_utils.cc. References log(), and matrixIndex(). Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), and PLearn::NeighborhoodSmoothnessNNet::build_().

CostFunc neg_output_costfunc (   )  [inline]

returns -output[0]. This is for density estimators whose use(x) method typically computes log(p(x)) Definition at line 70 of file NegOutputCostFunction.h. Referenced by PLearn::Distribution::Distribution().

Var negateElements (  Var  v  )  [inline]

Definition at line 76 of file NegateElementsVariable.h.

template<class T> void negateElements (  const TMat< T > &  m  )

x'_ij = -x_ij; Definition at line 4632 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void negateElements (  const TVec< T > &  vec  )

Definition at line 1037 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length(). Referenced by PLearn::ShiftAndRescaleVMatrix::build_(), normalize(), PLearn::PLS::train(), and PLearn::PCA::train().

Var negative (  Var  v  )  [inline]

Definition at line 79 of file CutAboveThresholdVariable.h. References cutAboveThreshold().

real negative (  real  a  )  [inline]

Definition at line 98 of file pl_math.h.

unsigned char new_get_compr_data_type (  float  x  )

Definition at line 733 of file pl_io.cc. References is_missing(), and x.

unsigned char new_get_compr_data_type (  double  x, double  tolerance )

Definition at line 720 of file pl_io.cc. References is_missing(), and x. Referenced by new_write_compressed().

size_t PLearn::new_read_compressed (  FILE *  in, real *  vec, int  l, bool  swap_endians = false )

Reads the l doubles in the new compressed formtat from in Returns the number of bytes read. Set swap_endians to true if the data was written on a machine with a different endianness than the current one, so that the endians get swapped. Definition at line 623 of file pl_io.cc. References endianswap(), MISSING_VALUE, mode, and val. Referenced by PLearn::DiskVMatrix::getNewRow().

size_t PLearn::new_write_compressed (  FILE *  out, real *  vec, int  l, double  tolerance = 1e-6, bool  swap_endians = false )

Writes the l doubles in new compressed format to out. Returns the number of bytes written tolerance is the maximum allowed error tolerance to store doubles as floats. Set swap_endians to true if the data is to be written in the different byte-order from this machine's Definition at line 819 of file pl_io.cc. References new_get_compr_data_type(), new_write_mode_and_size(), new_write_raw_data_as(), and PLERROR. Referenced by PLearn::DiskVMatrix::appendRow().

size_t new_write_mode_and_size (  FILE *  out, bool  insert_zeroes, unsigned int  N, unsigned char  data_type )

returns number of bytes written Definition at line 745 of file pl_io.cc. References mode. Referenced by new_write_compressed().

size_t new_write_raw_data_as (  FILE *  out, real *  vec, int  l, unsigned char  data_type )

Definition at line 782 of file pl_io.cc. References is_missing(), and val. Referenced by new_write_compressed().

string PLearn::newFilename (  const string  directory = "/tmp/", const string  prefix = "", bool  is_directory = false )

Returns a temporary file (or directory) name suitable for a unique (one time) use. < save in current dir Definition at line 498 of file fileutils.cc. References mode, PLERROR, and remove_trailing_slash(). Referenced by PLearn::TmpFilenames::addFilename(), PLearn::MatlabInterface::eigs_r11(), PLearn::FilterSplitter::getSplit(), PLearn::MatlabInterface::launch(), and PLearn::MatlabInterface::launchAndWaitFor().

Mat * PLearn::newIndexedMatArray (  int  n, Mat &  m, int  indexcolumn )

Returns an array of n matrices, that are submatrices of m Such that marray[i] contains all the rows of m that had value i in their indexcolumn. The matrices of the returned array do not contain the indexcolumn Side effect: rows of m are sorted according to indexcolumn Definition at line 100 of file Mat.cc. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLERROR, sortRows(), PLearn::TMat< T >::subMatColumns(), PLearn::TMat< T >::subMatRows(), and PLearn::TMat< T >::width().

Mat * PLearn::newMatArray (  int  n, int  the_length, int  the_width )

Definition at line 92 of file Mat.cc. References PLearn::TMat< T >::resize().

Mat * PLearn::newMatArray (  int  n  )

Definition at line 87 of file Mat.cc.

Object* newObject (  const string &  representation  )  [inline]

Creates a new object according to the given representation. This actually calls readObject on an istrstream, so anything understandable by readObject can be used here Definition at line 609 of file Object.h. References readObject(). Referenced by PLearn::VVMatrix::build_(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::VVMatrix::createPreproVMat(), and getDataSet().

PPointableSet* newSet (   )  [inline]

Definition at line 83 of file Set.h.

Vec * PLearn::newVecArray (  int  n, int  the_length )

Definition at line 70 of file Mat.cc. References PLearn::TVec< T >::resize().

Vec * PLearn::newVecArray (  int  n  )

Definition at line 65 of file Mat.cc.

Var nll_semispherical_gaussian (  Var  tangent_plane_var, Var  mu_var, Var  sm_var, Var  sn_var, Var  neighbors_dist_var, Var  p_target_var, Var  p_neighbors_var, Var  noise, Var  mu_noisy, bool  use_noise = false, real  epsilon = 1e-6 )  [inline]

Definition at line 91 of file NllSemisphericalGaussianVariable.h. Referenced by PLearn::GaussianContinuum::build_().

VarArray PLearn::nonInputParentsOfPath (  VarArray  inputs, VarArray  outputs )

Isn't this useless? as we have a constructor of VarArray from Var that should be called automatically !!!???? (Pascal). returns the set of all the direct parents of the vars on the path from inputs to outputs. (inputs are not included, neither are the direct parents of inputs unless they are also direct parents of other Vars in the path) Definition at line 1082 of file VarArray.cc. References PLearn::VarArray::clearMark(), PLearn::VarArray::parents(), propagationPath(), and PLearn::VarArray::setMark(). Referenced by PLearn::ConcatOfVariable::ConcatOfVariable(), PLearn::MatrixSumOfVariable::MatrixSumOfVariable(), PLearn::Function::operator()(), propagationPathToParentsOfPath(), PLearn::SumOfVariable::SumOfVariable(), PLearn::SumOverBagsVariable::SumOverBagsVariable(), PLearn::UnfoldedFuncVariable::UnfoldedFuncVariable(), and PLearn::UnfoldedSumOfVariable::UnfoldedSumOfVariable().

VarArray PLearn::nonInputSources (  const VarArray &  inputs, const VarArray &  outputs )

returns all sources that influence outputs except those that influence it only through inputs Definition at line 1121 of file VarArray.cc. References PLearn::VarArray::setMark(), PLearn::VarArray::sources(), and PLearn::VarArray::unmarkAncestors(). Referenced by PLearn::Function::build_().

template<class T> TVec<T> nonZero (  const TVec< T > &  vec  )

returns a vector composed of the values of v that are different from 0; Definition at line 1942 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> TVec<T> nonZeroIndices (  TVec< bool >  v  )

Definition at line 1659 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TVec< T >::resize(), and val.

template<class T> TVec<T> nonZeroIndices (  TVec< T >  v  )

Definition at line 1643 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TVec< T >::resize(), and val.

Var PLearn::norm (  Var  input, real  n = 2.0 )

Definition at line 81 of file Var_utils.cc. References abs(), pow(), sqrt(), square(), and sum().

template<class T> T norm (  const TVec< T > &  vec  )  [inline]

Definition at line 705 of file TMat_maths_impl.h. References norm().

template<class T> T norm (  const TVec< T > &  vec, double  n )

Definition at line 694 of file TMat_maths_impl.h. References mypow(), pownorm(), and sqrt(). Referenced by PLearn::SubsampleVariable::bprop(), PLearn::IsomapTangentLearner::computeOutput(), PLearn::GaussianContinuum::computeOutput(), diagonalizeSubspace(), distance(), PLearn::NormalizedDotProductKernel::evaluate(), PLearn::ProjectionErrorVariable::fprop(), PLearn::GaussMix::generateFromGaussian(), PLearn::VecStatsCollector::getCorrelation(), GramSchmidtOrthogonalization(), norm(), normalize(), PLearn::ScaledGradientOptimizer::optimize(), PLearn::GaussianProcessRegressor::QFormInverse(), subsample(), and PLearn::Function::verifyGradient().

RandomVar PLearn::normal (  RandomVar  mean, RandomVar  log_variance, real  minimum_standard_deviation = 1e-6 )

diagonal normal with general parameters given by the provided RandomVar's. Actual variance is variance = minimum_variance + exp(log_variance) Definition at line 729 of file RandomVar.cc. References mean().

RandomVar PLearn::normal (  real  mean = 0, real  standard_dev = 1, int  d = 1, real  minimum_standard_deviation = 1e-6 )

Functions to build a normal distribution. multivariate d-dimensional diagonal normal with NON-RANDOM and CONSTANT parameters (default means = 0, default standard deviations = 1) Actual variance is variance = minimum_variance + exp(log_variance) Definition at line 712 of file RandomVar.cc. References log(), mean(), PLERROR, and variance(). Referenced by PLearn::FieldConvertCommand::FieldConvertCommand().

real normal_cdf (  real  x  )  [inline]

Definition at line 72 of file pl_erf.h. References gauss_01_cum(), and x.

real normal_sample (   )  [inline]

Definition at line 89 of file random.h. References gaussian_01(). Referenced by PLearn::GaussianContinuum::make_random_walk(), and PLearn::CompactVMatrix::perturb().

VMat normalize (  VMat  d, int  inputsize )  [inline]

Definition at line 143 of file VMat_maths.h. References PLearn::VMat::length(), and normalize().

VMat PLearn::normalize (  VMat  d, int  inputsize, int  ntrain )

Here, mean and stddev are estimated on d.subMat(0,0,ntrain,inputsize). Definition at line 681 of file VMat_maths.cc. References computeMeanAndStddev(), normalize(), and PLearn::VMat::subMat().

VMat PLearn::normalize (  VMat  d, Vec  meanvec, Vec  stddevvec )

subtracts mean and divide by stddev meanvec and stddevvec can be shorter than d.width() (as we probably only want to 'normalize' the 'input' part of the sample, and not the 'output' that is typically present in the last columns) Definition at line 666 of file VMat_maths.cc. References invertElements(), PLearn::TVec< T >::length(), negateElements(), PLearn::TVec< T >::subVec(), and PLearn::VMat::width().

template<class T> void normalize (  TMat< T > &  m, double  n )

divide each row by its n norm Definition at line 4390 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and normalize().

template<class T> void normalize (  TMat< T > &  m  )

substract mean, and divide by stddev (these are estimated globally) Definition at line 4355 of file TMat_maths_impl.h. References computeMeanAndStddev(), and PLearn::TMat< T >::width().

template<class T> void normalize (  const TVec< T > &  vec, double  n )

Definition at line 708 of file TMat_maths_impl.h. References norm(). Referenced by condprob_cost(), entropy(), PLearn::Gnuplot::histoplot(), PLearn::KernelProjection::KernelProjection(), loadBreastCancerWisconsin(), loadClassificationDataset(), loadDiabetes(), loadHousing(), loadLetters(), loadPimaIndians(), loadUCI(), PLearn::PLS::NIPALSEigenvector(), normalize(), PLearn::PCA::PCA(), splitTrainValidTest(), PLearn::PLS::train(), and PLearn::PCA::train().

template<class T> void normalizeColumns (  const TMat< T > &  m  )

Divides each column by the sum of its elements. Definition at line 4378 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), sum(), and PLearn::TMat< T >::width().

void normalizeDataSet (  Mat &  m  )

Definition at line 122 of file databases.cc. References computeMeanAndStddev(), and PLearn::TMat< T >::width(). Referenced by loadBreastCancerWisconsin(), loadDiabetes(), loadHousing(), loadLetters(), and loadPimaIndians().

void PLearn::normalizeDataSets (  Mat &  training_set, Mat &  test_set )

normalize both training_set and test_set according to mean and stddev computed on training_set Definition at line 108 of file databases.cc. References computeMeanAndStddev(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().

void PLearn::normalizeDataSets (  VMat &  training_set, VMat &  validation_set, VMat &  test_set )

Definition at line 90 of file databases.cc. References computeMeanAndStddev(), PLearn::VMat::subMatColumns(), and PLearn::VMat::width().

void PLearn::normalizeDataSets (  Mat &  training_set, Mat &  validation_set, Mat &  test_set )

Definition at line 73 of file databases.cc. References computeMeanAndStddev(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width(). Referenced by loadATT800(), loadBreastCancer(), loadDiabetes(), loadLetters(), and splitTrainValidTest().

template<class T> void normalizeRows (  const TMat< T > &  m  )

Divides each row by the sum of its elements. Definition at line 4366 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and sum().

istream nullin (  &  null_streambuf  )

iostream nullinout (  &  null_streambuf  )

ostream nullout (  &  null_streambuf  )

int PLearn::numericType (  const char *  word  )

assigns a code to a "word" Definition at line 244 of file TypesNumeriques.cc. References PLearn::tRule::attributs, compactRepresentation(), looksNumeric(), NT_NOT_NUMERIC, NT_PERCENT, NT_UNKNOWN_NUMERIC_TYPE, PLearn::tRule::pattern, and rules.

real oEM (  ConditionalExpression  conditional_expression, RVArray  parameters_to_learn, VMat  distr, int  n_samples, Optimizer &  MStepOptimizer, int  max_n_iterations, real  relative_improvement_threshold = 0.001, bool  compute_final_train_NLL = true )

real oEM (  ConditionalExpression  conditional_expression, RVArray  parameters_to_learn, VMat  distr, int  n_samples, int  max_n_iterations, real  relative_improvement_threshold = 0.001, bool  compute_final_train_NLL = true )

int PLearn::old_plearn_main (  int  argc, char **  argv )

Definition at line 502 of file old_plearn_main.cc. References cross_valid(), displayObjectHelp(), endl(), exitmsg(), getDataSetHelp(), getMultipleModelAliases(), isfile(), seed(), stringvector(), toint(), train_and_test(), usage(), and use().

void OldDisplayVarGraph (  const VarArray &  outputs, bool  display_values, real  boxwidth, const char *  the_filename, bool  must_wait, VarArray  display_only_these )

Definition at line 414 of file DisplayUtils.cc. References PLearn::TmpFilenames::addFilename(), PLearn::TVec< Var >::append(), center(), PLearn::GhostScript::centerShow(), PLearn::VarArray::clearMark(), PLearn::TVec< Var >::contains(), PLearn::GhostScript::drawArrow(), PLearn::GhostScript::drawBox(), PLearn::Var::length(), PLearn::VarArray::parents(), PLearn::VarArray::setMark(), PLearn::TVec< Var >::size(), PLearn::VarArray::sources(), PLearn::VarArray::unmarkAncestors(), PLearn::GhostScript::usefont(), PLearn::Var::width(), and x.

template<class T> TVec<T> one_hot (  int  length, int  hotpos, T  coldvalue, T  hotvalue )

Definition at line 1326 of file TMat_maths_impl.h. References fill_one_hot().

VMat onehot (  VMat  d, int  nclasses, real  cold_value = 0.0, real  hot_value = 1.0 )  [inline]

Definition at line 107 of file OneHotVMatrix.h.

Var onehot (  int  the_length, Var  hotindex, real  coldvalue = 0.0, real  hotvalue = 1.0 )  [inline]

Definition at line 86 of file OneHotVariable.h. Referenced by PLearn::FieldConvertCommand::FieldConvertCommand().

Var onehot_squared_loss (  Var  network_output, Var  classnum, real  coldval = 0., real  hotval = 1. )  [inline]

Definition at line 88 of file OneHotSquaredLoss.h. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), and PLearn::NeighborhoodSmoothnessNNet::build_().

template<class T> TVec< T > PLearn::operator & (  const T &  x, const TVec< T > &  v )

Definition at line 63 of file PExperiment.cc. References PLearn::TVec< T >::size(), PLearn::TVec< T >::subVec(), and x.

Array<VMat> operator & (  const VMat &  d1, const VMat &  d2 )  [inline]

******************************** User-friendly VMat interface * Definition at line 163 of file VMat.h.

VarArray operator & (  Var  v1, Var  v2 )  [inline]

* To allow for easy building of VarArray * Definition at line 296 of file VarArray.h.

StatsItArray operator & (  const StatsIt &  statsit1, const StatsIt &  statsit2 )  [inline]

Definition at line 405 of file StatsIterator.h.

Array<Ker> operator & (  const Ker &  k1, const Ker &  k2 )  [inline]

******************** inline Ker operators Definition at line 237 of file Kernel.h.

template<class T> Array< TMat<T> > operator & (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

This will allow a convenient way of building arrays of Matrices by writing ex: m1&m2&m3. Definition at line 218 of file Array_impl.h.

template<class T> Array< TVec<T> > operator & (  const TVec< T > &  m1, const TVec< T > &  m2 )  [inline]

This will allow a convenient way of building arrays of Matrices by writing ex: m1&m2&m3. Definition at line 146 of file Array_impl.h.

template<class T> Array<T> operator & (  const Array< T > &  a, const vector< T > &  ar )

Definition at line 124 of file Array_impl.h. References PLearn::TVec< T >::append(), and PLearn::TVec< T >::size().

template<class T> Array<T> operator & (  const Array< T > &  a, const Array< T > &  ar )

Definition at line 115 of file Array_impl.h. References PLearn::TVec< T >::append(), and PLearn::TVec< T >::size().

template<class T> Array<T> operator & (  const Array< T > &  a, const T &  elem )

Definition at line 106 of file Array_impl.h. References PLearn::TVec< T >::append(), and PLearn::TVec< T >::size().

template<class T> Array<T> operator & (  const T &  elem, const Array< T > &  a )

Definition at line 90 of file Array_impl.h. Referenced by PLearn::VarArray::operator &(), PLearn::StatsItArray::operator &(), and PLearn::RVInstanceArray::operator &&().

template<class T> Array<T>& operator &= (  Array< T > &  a, const vector< T > &  ar )

Definition at line 102 of file Array_impl.h. References PLearn::TVec< T >::append().

template<class T> Array<T>& operator &= (  Array< T > &  a, const Array< T > &  ar )

Definition at line 98 of file Array_impl.h. References PLearn::TVec< T >::append().

template<class T> Array<T>& operator &= (  Array< T > &  a, const T &  elem )

Definition at line 94 of file Array_impl.h. References PLearn::TVec< T >::append(). Referenced by PLearn::VarArray::operator &=(), and PLearn::StatsItArray::operator &=().

Var PLearn::operator * (  Var  v1, Var  v2 )

element-wise multiplications < v1 and v2 must have the same dimensions (it is checked by the constructor of TimesVariable) Definition at line 155 of file Var_operators.cc.

Var PLearn::operator * (  real  cte, Var  v )

Definition at line 150 of file Var_operators.cc.

Var PLearn::operator * (  Var  v, real  cte )

Definition at line 147 of file Var_operators.cc.

RandomVar PLearn::operator * (  RandomVar  a, RandomVar  b )

********************** GLOBAL FUNCTIONS ********************** //!< Return a RandomVar that is the product of two RandomVar's. If a and b are matrices, this is a matrix product. If one of them is a vector it is interpreted as a column vector (nx1), but if both are vectors, this is a dot product (a is interpreted as a 1xn). The result contains a ProductRandomVariable, which can be "trained" by EM: if one of the two arguments is non-random and is considered to be a parameter, it can be learned (e.g. for implementing a linear regression). Definition at line 408 of file RandomVar.cc. References PLERROR.

template<class T> TMat<T> operator * (  const TMat< T > &  m, const TVec< T > &  v )  [inline]

does an elementwise multiplication of every row by v Definition at line 5719 of file TMat_maths_impl.h. References PLearn::TMat< T >::copy().

template<class T> TMat<T> operator * (  const T &  scalar, const TMat< T > &  m )  [inline]

Definition at line 3496 of file TMat_maths_impl.h.

template<class T> TMat<T> operator * (  const TMat< T > &  m, const T &  scalar )  [inline]

Definition at line 3488 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), multiply(), and PLearn::TMat< T >::width().

template<class T> TVec<T> operator * (  const TVec< T > &  v1, T  v2 )

Definition at line 1225 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and multiply().

template<class T> TVec<T> operator * (  T  scalar, const TVec< T > &  v )

Definition at line 1217 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and multiply().

template<class T> void operator *= (  const TMat< T > &  m1, const TMat< T > &  m2 )

does an elementwise division of every row by v Definition at line 4474 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator *= (  const TMat< T > &  m, const TVec< T > &  v )

does an elementwise multiplication of every row by v Definition at line 4459 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator *= (  const TMat< T > &  m, T  scalar )

Definition at line 4409 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void operator *= (  const TVec< T > &  vec, T  factor )

Definition at line 2021 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> void operator *= (  const TVec< T > &  vec1, const TVec< T > &  vec2 )

Definition at line 845 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

Var PLearn::operator!= (  Var  v1, Var  v2 )

Definition at line 195 of file Var_operators.cc. References isequal().

Var operator!= (  real  cte, Var  v1 )  [inline]

result[i] = 1 if v1[i]!=cte, 0 otherwise Definition at line 85 of file UnequalConstantVariable.h.

Var operator!= (  Var  v1, real  cte )  [inline]

result[i] = 1 if v1[i]!=cte, 0 otherwise Definition at line 81 of file UnequalConstantVariable.h.

template<class T, unsigned N, class TTrait> bool operator!= (  const TinyVector< T, N, TTrait > &  x, const TinyVector< T, N, TTrait > &  y )  [inline]

Other operators (should be defined in std::rel_ops, but does not work properly with gcc yet). Definition at line 168 of file TinyVector.h. References x.

template<class T, unsigned SizeBits, class Allocator> bool operator!= (  const SmallVector< T, SizeBits, Allocator > &  x, const SmallVector< T, SizeBits, Allocator > &  y )  [inline]

const SmallVector<T,SizeBits,Allocator>& y) { return !(x==y); } Definition at line 182 of file SmallVector.h. References x.

template<class T> TVec<T> operator% (  const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 1205 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and PLERROR.

Var PLearn::operator+ (  Var  v1, Var  v2 )

Definition at line 81 of file Var_operators.cc.

Var PLearn::operator+ (  real  cte, Var  v )

Definition at line 75 of file Var_operators.cc.

Var PLearn::operator+ (  Var  v, real  cte )

Definition at line 72 of file Var_operators.cc.

RandomVar PLearn::operator+ (  RandomVar  a, RandomVar  b )

Return a RandomVar that is the element-by-element sum of two RandomVar's. The result contains a PlusRandomVariable, which can be "trained" by EM: if one of the two arguments is non-random and is considered to be a parameter, it can be learned (e.g. for implementing a linear regression). Definition at line 432 of file RandomVar.cc.

template<class T> TMatRowsIterator<T> operator+ (  typename TMatRowsIterator< T >::difference_type  n, const TMatRowsIterator< T > &  y )

Definition at line 57 of file TMatRowsIterator_impl.h.

template<class T> TMatRowsAsArraysIterator<T> operator+ (  typename TMatRowsAsArraysIterator< T >::difference_type  n, const TMatRowsAsArraysIterator< T > &  y )

Definition at line 57 of file TMatRowsAsArraysIterator_impl.h.

template<class T> TMatColRowsIterator<T> operator+ (  typename TMatColRowsIterator< T >::difference_type  n, const TMatColRowsIterator< T > &  y )

Definition at line 58 of file TMatColRowsIterator_impl.h.

template<class T> TMat<T> operator+ (  const TMat< T > &  m, const TVec< T > &  v )  [inline]

return m + v (added to every ROW of m) Definition at line 5709 of file TMat_maths_impl.h. References PLearn::TMat< T >::copy().

template<class T> TMat<T> operator+ (  const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 4566 of file TMat_maths_impl.h. References add(), PLearn::TMat< T >::length(), and PLearn::TMat< T >::width().

template<class T> TVec<T> operator+ (  const TVec< T > &  v1, T  v2 )

Definition at line 1197 of file TMat_maths_impl.h. References add(), and PLearn::TVec< T >::length().

template<class T> TVec<T> operator+ (  T  v1, const TVec< T > &  v2 )

Definition at line 1189 of file TMat_maths_impl.h. References add(), and PLearn::TVec< T >::length().

template<class T> TVec<T> operator+ (  const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 1177 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and PLERROR.

SparseMatrix PLearn::operator+ (  const SparseMatrix &  A, const SparseMatrix &  B )

add two sparse matrices (of same dimensions but with values in possibly different places) Definition at line 259 of file SparseMatrix.cc. References PLearn::SparseMatrix::beginRow, PLearn::TVec< T >::clear(), PLearn::TVec< T >::data(), PLearn::SparseMatrix::endRow, PLearn::TVec< T >::length(), PLearn::SparseMatrix::n_rows, PLERROR, PLearn::TVec< T >::resize(), PLearn::SparseMatrix::row, and PLearn::SparseMatrix::values.

PDate operator+ (  const PDate &  pdate, int  ndays )  [inline]

add a number of days Definition at line 145 of file PDate.h. References PLearn::PDate::toJulianDay().

void PLearn::operator+= (  Var &  v1, const Var &  v2 )

Definition at line 99 of file Var_operators.cc. References PLearn::PP< Variable >::isNull().

template<class T> void operator+= (  const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 4524 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator+= (  const TMat< T > &  m, const TVec< T > &  v )

adds v to every row Definition at line 4429 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator+= (  const TMat< T > &  m, T  scalar )

Definition at line 4400 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void operator+= (  const TVec< T > &  vec1, const TVec< T > &  vec2 )  [inline]

element-wise + Definition at line 2031 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> void operator+= (  const TVec< T > &  vec, T  scalar )

Definition at line 1064 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

VarArray PLearn::operator- (  const VarArray &  a, const VarArray &  b )

returns all variables of a that are not in b Definition at line 1104 of file VarArray.cc. References PLearn::TVec< Var >::append(), and PLearn::TVec< Var >::size().

Var PLearn::operator- (  real  cte, Var  v )

Definition at line 142 of file Var_operators.cc.

Var PLearn::operator- (  Var  v  )

Definition at line 128 of file Var_operators.cc.

Var PLearn::operator- (  Var  v1, Var  v2 )

Definition at line 110 of file Var_operators.cc.

Var PLearn::operator- (  Var  v, real  cte )

Definition at line 78 of file Var_operators.cc.

RandomVar PLearn::operator- (  RandomVar  a, RandomVar  b )

Return a MatRandomVar that is the element-by-element difference of two RandomVar's. The result contains a MinusRandomVariable. Definition at line 437 of file RandomVar.cc.

template<class T> TMat<T> operator- (  const TMat< T > &  m, const TVec< T > &  v )  [inline]

return m - v (subtracted from every ROW of m) Definition at line 5714 of file TMat_maths_impl.h. References PLearn::TMat< T >::copy().

template<class T> TMat<T> operator- (  const TMat< T > &  m  )

return a negated copy of m Definition at line 4619 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> TMat<T> operator- (  const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 4558 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), substract(), and PLearn::TMat< T >::width().

template<class T> TVec<T> operator- (  const TVec< T > &  v1, T  v2 )

Definition at line 1169 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and substract().

template<class T> TVec<T> operator- (  T  v1, const TVec< T > &  v2 )

Definition at line 1160 of file TMat_maths_impl.h. References PLearn::TVec< T >::length().

template<class T> TVec<T> operator- (  const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 1148 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and PLERROR.

template<class T> TVec<T> operator- (  TVec< T >  vec  )

Definition at line 1076 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

Ker operator- (  const Ker &  k  )  [inline]

Definition at line 76 of file NegKernel.h. References k.

double operator- (  const PDateTime &  to_date, const PDateTime &  from_date )  [inline]

subtract two dates, the result being counted in days (+ fractions) Definition at line 145 of file PDateTime.h.

PDate operator- (  const PDate &  pdate, int  ndays )  [inline]

subtract a number of days add a number of days Definition at line 152 of file PDate.h. References PLearn::PDate::toJulianDay().

int operator- (  const PDate &  to_date, const PDate &  from_date )  [inline]

substract two dates, the result being counted in days. Definition at line 139 of file PDate.h. References PLearn::PDate::toJulianDay().

void PLearn::operator-= (  Var &  v1, const Var &  v2 )

Definition at line 131 of file Var_operators.cc. References PLearn::PP< Variable >::isNull().

template<class T> void operator-= (  const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 4541 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator-= (  const TMat< T > &  m, const TVec< T > &  v )

subtracts v from every row Definition at line 4444 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator-= (  const TMat< T > &  m, T  scalar )  [inline]

Definition at line 4418 of file TMat_maths_impl.h.

template<class T> void operator-= (  const TVec< T > &  vec, T  scalar )

Definition at line 1072 of file TMat_maths_impl.h.

template<class T> void operator-= (  const TVec< T > &  vec1, const TVec< T > &  vec2 )

Definition at line 836 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

Var PLearn::operator/ (  Var  v1, Var  v2 )

Definition at line 184 of file Var_operators.cc. References invertElements(), PLearn::Var::length(), and PLearn::Var::width().

Var PLearn::operator/ (  real  cte, Var  v )

Definition at line 176 of file Var_operators.cc. References invertElements().

Var PLearn::operator/ (  Var  v, real  cte )

Definition at line 173 of file Var_operators.cc.

Func PLearn::operator/ (  Func  f, real  value )

Definition at line 86 of file Func.cc.

RandomVar PLearn::operator/ (  RandomVar  a, RandomVar  b )

Return a MatRandomVar that is the element-by-element ratio of two RandomVar's. The result contains a RandomVariable. Definition at line 442 of file RandomVar.cc.

template<class T> TMat<T> operator/ (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

elementwise division of every row by v Definition at line 5729 of file TMat_maths_impl.h. References PLearn::TMat< T >::copy().

template<class T> TMat<T> operator/ (  const TMat< T > &  m, const TVec< T > &  v )  [inline]

elementwise division of every row by v Definition at line 5724 of file TMat_maths_impl.h. References PLearn::TMat< T >::copy().

template<class T> TMat<T> operator/ (  const TMat< T > &  m, const T &  scalar )  [inline]

Definition at line 3501 of file TMat_maths_impl.h.

template<class T1, class T2> TVec<T1> operator/ (  const TVec< T1 > &  v1, T2  scalar )

Definition at line 1258 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and multiply().

template<class T> TVec<T> operator/ (  T  v1, const TVec< T > &  v2 )

Definition at line 1245 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> TVec<T> operator/ (  const TVec< T > &  v1, const TVec< T > &  v2 )

Definition at line 1233 of file TMat_maths_impl.h. References PLearn::TVec< T >::length(), and PLERROR.

template<class T> void operator/= (  const TMat< T > &  m1, const TMat< T > &  m2 )

does an elementwise division Definition at line 4507 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator/= (  const TMat< T > &  m, const TVec< T > &  v )

Definition at line 4492 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void operator/= (  const TMat< T > &  m, int  scalar )  [inline]

Definition at line 4424 of file TMat_maths_impl.h.

template<class T> void operator/= (  const TMat< T > &  m, T  scalar )  [inline]

Definition at line 4421 of file TMat_maths_impl.h.

template<class T> void operator/= (  const TVec< T > &  vec1, const TVec< T > &  vec2 )

Definition at line 916 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> void operator/= (  const TVec< T > &  vec, int  scalar )  [inline]

Definition at line 858 of file TMat_maths_impl.h.

template<class T> void operator/= (  const TVec< T > &  vec, T  scalar )  [inline]

Definition at line 854 of file TMat_maths_impl.h.

Var operator< (  Var  v1, Var  v2 )  [inline]

++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Definition at line 78 of file IsSmallerVariable.h.

template<class T> bool operator< (  const TVec< T > &  left, const TVec< T > &  right )

Definition at line 270 of file TVec_impl.h. References left(), PLERROR, and right().

template<class T, unsigned N, class TTrait> bool PLearn::operator< (  const TinyVector< T, N, TTrait > &  , const TinyVector< T, N, TTrait > &  )

Lexicographical Ordering. Definition at line 458 of file TinyVector.h. References x.

template<class T, unsigned SizeBits, class Allocator> bool operator< (  const SmallVector< T, SizeBits, Allocator > &  x, const SmallVector< T, SizeBits, Allocator > &  y )

Definition at line 491 of file SmallVector.h. References x.

template<class T, unsigned SizeBits> bool operator< (  const SmallVector< T, SizeBits > &  , const SmallVector< T, SizeBits > &  )

const SmallVector<T,SizeBits,Allocator>&);

bool operator< (  RealMapping::single_mapping_t  a, RealMapping::single_mapping_t  b )

Definition at line 122 of file RealMapping.cc. References PLearn::RealMapping::single_mapping_t.

ostream& operator<< (  ostream &  out, Set  s )  [inline]

Definition at line 107 of file Set.h. References PLearn::Set::begin(), PLearn::Set::end(), and SetIterator.

PStream& operator<< (  PStream &  out, const PPointableSet &  pp_set )  [inline]

Definition at line 16 of file Set.h.

ostream& operator<< (  ostream &  out, ProbabilitySparseMatrix &  pyx )  [inline]

Definition at line 545 of file ProbabilitySparseMatrix.h. References endl(), NUMWIDTH, PLearn::ProbabilitySparseMatrix::nx(), PLearn::ProbabilitySparseMatrix::ny(), PLearn::ProbabilitySparseMatrix::raise_error, and x.

void operator<< (  const Vec &  v, const VVec &  vv )  [inline]

Definition at line 133 of file VVec.h. References PLearn::VVec::toVec().

void operator<< (  const VVec &  vv, const Vec &  v )  [inline]

Definition at line 130 of file VVec.h. References PLearn::VVec::copyFrom().

ostream& operator<< (  ostream &  out, const VMat &  m )  [inline]

Definition at line 166 of file VMat.h. References PLearn::VMat::print().

void operator<< (  const Mat &  dest, const VMat &  src )  [inline]

Definition at line 150 of file VMat.h.

void operator<< (  const Mat &  dest, const VMatrix &  src )  [inline]

Definition at line 140 of file VMat.h. References PLearn::VMatrix::getMat(), PLearn::VMatrix::length(), PLearn::TMat< T >::length(), PLERROR, PLearn::VMatrix::width(), and PLearn::TMat< T >::width().

PStream& operator<< (  PStream &  out, const VarArray &  o )  [inline]

Definition at line 304 of file VarArray.h.

void operator<< (  VarArray &  ar, const Vec &  datavec )  [inline]

Definition at line 240 of file VarArray.h. References PLearn::VarArray::copyFrom().

void PLearn::operator<< (  VarArray &  ar, const Array< Vec > &  values )

Definition at line 1131 of file VarArray.cc. References k, PLERROR, PLearn::TVec< T >::size(), and PLearn::TVec< Var >::size().

ostream& operator<< (  ostream &  out, const Var &  v )  [inline]

Definition at line 56 of file Var.h. References endl(), and PLearn::Var::width().

template<class T> ostream& operator<< (  ostream &  out, const TVec< T > &  v )  [inline]

Definition at line 208 of file TVec_impl.h.

template<class T> PStream& operator<< (  PStream &  out, const TVec< T > &  v )  [inline]

Read and Write from C++ stream: write saves length and read resizes accordingly (the raw modes don't write any size information). Definition at line 146 of file TVec_impl.h.

template<class T, class U> void operator<< (  const TVec< T > &  m1, const TVec< U > &  m2 )

copy TVec << TVec (different types) Definition at line 117 of file TVec_impl.h. References copy_cast(), and PLERROR.

template<class T> void operator<< (  const TVec< T > &  m1, const TVec< T > &  m2 )  [inline]

copy TVec << TVec Definition at line 106 of file TVec_impl.h. References std::copy(), and PLERROR.

void operator<< (  const Vec &  v, real  f )  [inline]

Same as fill(f) (will only work with Vec, because of a potential conflict with T == string if we wanted to make it generic). Definition at line 778 of file TVec_decl.h. References PLearn::TVec< T >::fill().

template<class T> PStream& operator<< (  PStream &  out, const TMat< T > &  m )  [inline]

Read and Write from C++ stream: write saves length and read resizes accordingly (the raw modes don't write any size information). Definition at line 819 of file TMat_impl.h.

template<class T> ostream& operator<< (  ostream &  out, const TMat< T > &  m )  [inline]

printing a TMat Definition at line 717 of file TMat_impl.h. References PLearn::TMat< T >::print().

template<class T, class U> void operator<< (  const TVec< T > &  m1, const TMat< U > &  m2 )  [inline]

copy TVec << TMat (different types) Definition at line 689 of file TMat_impl.h. References copy_cast(), and PLERROR.

template<class T> void operator<< (  const TVec< T > &  m1, const TMat< T > &  m2 )  [inline]

copy TVec << TMat Definition at line 678 of file TMat_impl.h. References std::copy(), and PLERROR.

template<class T, class U> void operator<< (  const TMat< T > &  m1, const TVec< U > &  m2 )  [inline]

copy TMat << Tvec (different types) Definition at line 667 of file TMat_impl.h. References copy_cast(), and PLERROR.

template<class T> void operator<< (  const TMat< T > &  m1, const TVec< T > &  m2 )  [inline]

copy TMat << Tvec Definition at line 656 of file TMat_impl.h. References std::copy(), and PLERROR.

template<class T, class U> void operator<< (  const TMat< T > &  m1, const TMat< U > &  m2 )

copy TMat << TMat (different types) Definition at line 645 of file TMat_impl.h. References copy_cast(), and PLERROR.

template<class T> void operator<< (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

copy TMat << TMat Definition at line 633 of file TMat_impl.h. References std::copy(), and PLERROR.

PStream& operator<< (  PStream &  out, const StatsItArray &  o )  [inline]

Definition at line 397 of file StatsIterator.h.

PStream& operator<< (  PStream &  out, const StatsCollectorCounts &  c )  [inline]

Definition at line 72 of file StatsCollector.h. References PLearn::StatsCollectorCounts::id, PLearn::StatsCollectorCounts::n, PLearn::StatsCollectorCounts::nbelow, PLearn::StatsCollectorCounts::sum, and PLearn::StatsCollectorCounts::sumsquare.

template<class T> PStream& operator<< (  PStream &  out, const ProbSparseMatrix &  p )  [inline]

Definition at line 73 of file ProbSparseMatrix.h. References PLearn::DoubleAccessSparseMatrix< real >::write().

ostream& operator<< (  ostream &  out, const Vec &  v )  [inline]

Definition at line 56 of file Mat.h. References PLearn::TVec< T >::print().

template<class T> PStream& operator<< (  PStream &  out, const DoubleAccessSparseMatrix< T > &  m )  [inline]

Definition at line 181 of file DoubleAccessSparseMatrix.h.

template<class T> PStream& operator<< (  PStream &  out, const set< T > &  v )  [inline]

Definition at line 1162 of file PStream.h. References writeSet().

template<class T> PStream& operator<< (  PStream &  out, const vector< T > &  v )  [inline]

Definition at line 1114 of file PStream.h. References writeSequence().

template<class Key, class Value> PStream& operator<< (  PStream &  out, const hash_multimap< Key, Value > &  m )  [inline]

Definition at line 752 of file PStream.h. References writeMap().

template<class Key, class Value> PStream& operator<< (  PStream &  out, const hash_map< Key, Value > &  m )  [inline]

Definition at line 744 of file PStream.h. References writeMap().

template<class Key, class Value> PStream& operator<< (  PStream &  out, const multimap< Key, Value > &  m )  [inline]

Definition at line 735 of file PStream.h. References writeMap().

template<class Key, class Value> PStream& operator<< (  PStream &  out, const map< Key, Value > &  m )  [inline]

Definition at line 727 of file PStream.h. References writeMap().

template<class A, class B> PStream& operator<< (  PStream &  out, const pair< A, B > &  x )  [inline]

Definition at line 648 of file PStream.h. References x.

PStream& operator<< (  PStream &  out, bool  x )  [inline]

Definition at line 608 of file PStream.h. References PLearn::PStream::outmode, PLERROR, PLearn::PStream::put(), and x.

template<class T> PStream& operator<< (  PStream &  out, T *&  ptr )  [inline]

Definition at line 602 of file PStream.h.

template<class T> PStream& operator<< (  PStream &  out, const PP< T > &  o )  [inline]

Definition at line 594 of file PStream.h.

template<class T> PStream& operator<< (  PStream &  out, const T *&  x )  [inline]

Definition at line 553 of file PStream.h. References PLearn::PStream::copies_map_out, PLearn::PStream::put(), PLearn::PStream::write(), and x.

ostream & PLearn::operator<< (  ostream &  o, const Row &  row )

outputs all fields in a row, separated by " | " Definition at line 916 of file SimpleDB.cc. References PLearn::Row::begin(), PLearn::Row::end(), and endl().

ostream & PLearn::operator<< (  ostream &  o, const Row::iterator &  field )

outputs a single field flushed right in a cell of apropriate width (as given by field.char_width()) Definition at line 856 of file SimpleDB.cc. References PLearn::RowIterator::asCharacter(), PLearn::RowIterator::asDate(), PLearn::RowIterator::asDouble(), PLearn::RowIterator::asFloat(), PLearn::RowIterator::asInt(), PLearn::RowIterator::asShort(), PLearn::RowIterator::asSignedChar(), PLearn::RowIterator::asString(), center(), PLearn::RowIterator::char_width(), PLearn::RowIterator::isMissing(), PLERROR, and x.

ostream & PLearn::operator<< (  ostream &  os, const FieldValue &  ft )

Definition at line 551 of file SimpleDB.cc. References PLearn::FieldValue::toString().

template<class U, class V> ostream& operator<< (  ostream &  out, const pair< U, V > &  p )

Formatted printing of a pair<U,V> as U:V. Definition at line 263 of file stringutils.h.

ostream & PLearn::operator<< (  ostream &  out, const vector< string > &  vs )

formatted printing of vector<string> prints strings separated by a ", " Definition at line 608 of file stringutils.cc.

ostream& operator<< (  ostream &  out, const StringTable &  st )

Definition at line 42 of file StringTable.cc. References PLearn::StringTable::data, PLearn::StringTable::fieldnames, left(), PLearn::TVec< T >::length(), PLearn::StringTable::length(), and PLearn::StringTable::width().

template<class T> PStream& operator<< (  PStream &  out, const Storage< T > &  seq )

Definition at line 327 of file Storage.h. References writeSequence().

PStream& operator<< (  PStream &  out, const SetOption &  o )  [inline]

Definition at line 113 of file SetOption.h.

PStream& operator<< (  PStream &  out, const RealMapping &  o )  [inline]

Definition at line 237 of file RealMapping.h.

ostream& operator<< (  ostream &  out, const RealRange &  range )  [inline]

Definition at line 117 of file RealMapping.h. References PLearn::RealRange::print().

PStream & PLearn::operator<< (  PStream &  out, const RealRange &  x )

Definition at line 54 of file RealMapping.cc. References PLearn::PStream::put(), and x.

ostream& operator<< (  ostream &  out, Range  r )  [inline]

Definition at line 77 of file Range.h. References PLearn::Range::length, and PLearn::Range::start.

ostream& operator<< (  ostream &  os, const PDateTime &  date )  [inline]

Definition at line 150 of file PDateTime.h. References PLearn::PDateTime::info().

ostream& operator<< (  ostream &  os, const PDate &  date )  [inline]

Definition at line 158 of file PDate.h. References PLearn::PDate::info().

PStream& operator<< (  PStream &  out, const Object &  o )  [inline]

Definition at line 617 of file Object.h. References PLearn::Object::newwrite().

ostream& operator<< (  ostream &  out, const Object &  obj )  [inline]

Definition at line 612 of file Object.h. References PLearn::Object::print().

template<class T> ostream& operator<< (  ostream &  out, const Array< T > &  a )

Definition at line 82 of file Array_impl.h.

template<class T> PStream& operator<< (  PStream &  out, const Array< T > &  a )  [inline]

Definition at line 78 of file Array_impl.h. References writeSequence(). Referenced by PLearn::PStream::operator<<().

Var operator<= (  Var  v1, Var  v2 )  [inline]

Definition at line 80 of file IsLargerVariable.h.

Var operator<= (  Var  v, real  threshold )  [inline]

Definition at line 86 of file IsAboveThresholdVariable.h.

template<class T> bool operator<= (  const TVec< T > &  left, const TVec< T > &  right )

A simple family of relational operators for TVec. Definition at line 249 of file TVec_impl.h. References left(), PLERROR, and right().

template<class T, unsigned N, class TTrait> bool operator<= (  const TinyVector< T, N, TTrait > &  x, const TinyVector< T, N, TTrait > &  y )  [inline]

Definition at line 182 of file TinyVector.h. References x.

Var PLearn::operator== (  Var  v1, Var  v2 )

Definition at line 192 of file Var_operators.cc. References isequal().

Var operator== (  real  cte, Var  v1 )  [inline]

result[i] = 1 if v1[i]==cte, 0 otherwise Definition at line 85 of file EqualConstantVariable.h.

Var operator== (  Var  v1, real  cte )  [inline]

result[i] = 1 if v1[i]==cte, 0 otherwise Definition at line 81 of file EqualConstantVariable.h.

template<class T, unsigned N, class TTrait> bool PLearn::operator== (  const TinyVector< T, N, TTrait > &  , const TinyVector< T, N, TTrait > &  )

Equality operator. Definition at line 444 of file TinyVector.h. References PLearn::TinyVector< T, N, TTrait >::begin(), PLearn::TinyVector< T, N, TTrait >::end(), and x.

template<class T, unsigned SizeBits, class Allocator> bool PLearn::operator== (  const SmallVector< T, SizeBits, Allocator > &  a, const SmallVector< T, SizeBits, Allocator > &  b )

Equality operator. Definition at line 476 of file SmallVector.h. References PLearn::SmallVector< T, SizeBits, Allocator >::begin(), PLearn::SmallVector< T, SizeBits, Allocator >::end(), and x. Referenced by PLearn::TMatRowsIterator< T >::operator!=(), and PLearn::TMatRowsAsArraysIterator< T >::operator!=().

Var operator> (  Var  v1, Var  v2 )  [inline]

Definition at line 77 of file IsLargerVariable.h.

template<class T> bool operator> (  const TVec< T > &  left, const TVec< T > &  right )

Definition at line 290 of file TVec_impl.h. References left(), PLERROR, and right().

template<class T, unsigned N, class TTrait> bool operator> (  const TinyVector< T, N, TTrait > &  x, const TinyVector< T, N, TTrait > &  y )  [inline]

Definition at line 175 of file TinyVector.h. References x.

Var operator>= (  Var  v1, Var  v2 )  [inline]

Definition at line 81 of file IsSmallerVariable.h.

Var operator>= (  Var  v, real  threshold )  [inline]

Definition at line 83 of file IsAboveThresholdVariable.h.

template<class T> bool operator>= (  const TVec< T > &  left, const TVec< T > &  right )

Definition at line 259 of file TVec_impl.h. References left(), PLERROR, and right().

template<class T, unsigned N, class TTrait> bool operator>= (  const TinyVector< T, N, TTrait > &  x, const TinyVector< T, N, TTrait > &  y )  [inline]

Definition at line 189 of file TinyVector.h. References x.

PStream& operator>> (  PStream &  in, PPointableSet &  pp_set )  [inline]

Definition at line 19 of file Set.h.

void operator>> (  const Vec &  v, const VVec &  vv )  [inline]

Definition at line 136 of file VVec.h. References PLearn::VVec::copyFrom().

void operator>> (  const VVec &  vv, const Vec &  v )  [inline]

Definition at line 127 of file VVec.h. References PLearn::VVec::toVec().

void operator>> (  const VMat &  src, const Mat &  dest )  [inline]

Definition at line 153 of file VMat.h.

void operator>> (  const VMatrix &  src, const Mat &  dest )  [inline]

Definition at line 147 of file VMat.h.

PStream& operator>> (  PStream &  in, VarArray &  o )  [inline]

Definition at line 301 of file VarArray.h.

void operator>> (  VarArray &  ar, const Vec &  datavec )  [inline]

Definition at line 243 of file VarArray.h. References PLearn::VarArray::copyTo().

void PLearn::operator>> (  VarArray &  ar, const Array< Vec > &  values )

Definition at line 1146 of file VarArray.cc. References k, PLERROR, PLearn::TVec< T >::size(), and PLearn::TVec< Var >::size().

template<class T> istream& operator>> (  istream &  in, const TVec< T > &  v )  [inline]

Definition at line 215 of file TVec_impl.h. References PLearn::TVec< T >::input().

template<class T> PStream& operator>> (  PStream &  in, TVec< T > &  v )

Definition at line 153 of file TVec_impl.h. References PLearn::TVec< T >::read().

template<class T, class U> void operator>> (  const TVec< T > &  m1, const TVec< U > &  m2 )  [inline]

copy TVec >> TVec Definition at line 128 of file TVec_impl.h.

template<class T> PStream& operator>> (  PStream &  in, TMat< T > &  m )

Definition at line 826 of file TMat_impl.h. References PLearn::TMat< T >::read().

template<class T> istream& operator>> (  istream &  in, const TMat< T > &  m )  [inline]

inputing a TMat Definition at line 726 of file TMat_impl.h. References PLearn::TMat< T >::input().

template<class T, class U> void operator>> (  const TMat< T > &  m1, const TVec< U > &  m2 )  [inline]

copy TMat >> Tvec Definition at line 710 of file TMat_impl.h.

template<class T, class U> void operator>> (  const TVec< T > &  m1, const TMat< U > &  m2 )  [inline]

copy TVec >> TMat Definition at line 705 of file TMat_impl.h.

template<class T, class U> void operator>> (  const TMat< T > &  m1, const TMat< U > &  m2 )  [inline]

copy TMat >> TMat Definition at line 700 of file TMat_impl.h.

PStream& operator>> (  PStream &  in, StatsItArray &  o )  [inline]

Definition at line 394 of file StatsIterator.h.

PStream& operator>> (  PStream &  in, StatsCollectorCounts &  c )  [inline]

this class holds simple statistics about a field Definition at line 69 of file StatsCollector.h. References PLearn::StatsCollectorCounts::id, PLearn::StatsCollectorCounts::n, PLearn::StatsCollectorCounts::nbelow, PLearn::StatsCollectorCounts::sum, and PLearn::StatsCollectorCounts::sumsquare.

template<class T> PStream& operator>> (  PStream &  in, ProbSparseMatrix &  p )  [inline]

Definition at line 80 of file ProbSparseMatrix.h. References PLearn::DoubleAccessSparseMatrix< real >::read().

template<class T> PStream& operator>> (  PStream &  in, DoubleAccessSparseMatrix< T > &  m )  [inline]

Definition at line 188 of file DoubleAccessSparseMatrix.h. References PLearn::DoubleAccessSparseMatrix< T >::read().

template<class T> PStream& operator>> (  PStream &  in, set< T > &  v )  [inline]

Definition at line 1158 of file PStream.h. References readSet().

template<class T> PStream& operator>> (  PStream &  in, vector< T > &  v )  [inline]

Definition at line 1110 of file PStream.h. References readSequence().

template<class Key, class Value> PStream& operator>> (  PStream &  in, hash_multimap< Key, Value > &  m )  [inline]

Definition at line 756 of file PStream.h. References readMap().

template<class Key, class Value> PStream& operator>> (  PStream &  in, hash_map< Key, Value > &  m )  [inline]

Definition at line 748 of file PStream.h. References readMap().

template<class Key, class Value> PStream& operator>> (  PStream &  in, multimap< Key, Value > &  m )  [inline]

Definition at line 739 of file PStream.h. References readMap().

template<class Key, class Value> PStream& operator>> (  PStream &  in, map< Key, Value > &  m )  [inline]

Definition at line 731 of file PStream.h. References readMap().

template<typename S, typename T> PStream& operator>> (  PStream &  in, pair< S, T > &  x )  [inline]

Definition at line 658 of file PStream.h. References PLearn::PStream::get(), PLERROR, PLearn::PStream::skipBlanksAndComments(), PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and x.

template<class T> PStream& operator>> (  PStream &  in, PP< T > &  o )  [inline]

Definition at line 581 of file PStream.h. References PLearn::PP< T >::isNull().

template<class T> PStream& operator>> (  PStream &  in, T *&  x )  [inline]

Definition at line 509 of file PStream.h. References PLearn::PStream::copies_map_in, PLearn::PStream::get(), PLearn::PStream::peek(), PLERROR, PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and x.

template<class T> PStream& operator>> (  PStream &  in, Storage< T > &  seq )

Definition at line 334 of file Storage.h. References readSequence().

PStream& operator>> (  PStream &  in, PP< SetOption > &  o )  [inline]

Definition at line 113 of file SetOption.h.

PStream& operator>> (  PStream &  in, SetOption *&  o )  [inline]

Definition at line 113 of file SetOption.h.

PStream& operator>> (  PStream &  in, SetOption &  o )  [inline]

Definition at line 113 of file SetOption.h.

PStream& operator>> (  PStream &  in, PP< RealMapping > &  o )  [inline]

Definition at line 237 of file RealMapping.h.

PStream& operator>> (  PStream &  in, RealMapping *&  o )  [inline]

Definition at line 237 of file RealMapping.h.

PStream& operator>> (  PStream &  in, RealMapping &  o )  [inline]

Definition at line 237 of file RealMapping.h.

PStream & PLearn::operator>> (  PStream &  in, RealRange &  x )

Definition at line 62 of file RealMapping.cc. References PLearn::PStream::get(), PLearn::PStream::skipBlanksAndComments(), PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and x.

PStream& operator>> (  PStream &  in, Object &  o )  [inline]

Definition at line 615 of file Object.h. References PLearn::Object::newread().

PStream & PLearn::operator>> (  PStream &  in, Object *&  o )

Definition at line 428 of file Object.cc. References PLearn::PStream::copies_map_in, PLearn::PStream::get(), PLearn::PStream::peek(), PLERROR, readObject(), PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and x.

template<class T> PStream& operator>> (  PStream &  in, Array< T > &  a )  [inline]

Definition at line 74 of file Array_impl.h. References readSequence(). Referenced by PLearn::PStream::operator>>().

Mat PLearn::operator^ (  const Mat &  m1, const Mat &  m2 )

"cross" product of two sets. Matrices m1 and m2 are regarded as two sets of vectors (their rows) m1^m2 returns the set of all possible concatenations of a vector from m1 and a vector from m2 ex: Mat(2,1,"1 2 3 4") ^ Mat(2,2,"10 20 30 40") ==> 1 2 10 20 1 2 30 40 3 4 10 20 3 4 30 40 Definition at line 132 of file Mat.cc. References PLearn::TMat< T >::length(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().

template<class T> T output_margin (  const TVec< T > &  class_scores, int  correct_class )

Definition at line 1296 of file TMat_maths_impl.h. References PLearn::TVec< T >::length().

CostFunc PLearn::output_minus_target (  int  singleoutputindex = -1  )  [inline]

Definition at line 59 of file DifferenceKernel.cc.

Var PLearn::P (  ConditionalExpression  conditional_expression, bool  clearMarksUponReturn = true )

Construct a Var that computes P(LHS == observation | RHS == values ) in terms of the Var observation and the Vars in the RHS, where RHS is a RVArray such as (X1==x1 && X2==x2 && X3==x3) where Xi are RandomVar's and xi are Var's which represent the value that are given to the conditioning variables Xi. Normally the marks used to identify RVs which are deterministically determined from the RHS are cleared upon return (unless specified with the optional 2nd argument). Definition at line 593 of file RandomVar.cc. References PLearn::ConditionalExpression::LHS, PLearn::ConditionalExpression::RHS, PLearn::RVInstance::v, and PLearn::RVInstance::V. Referenced by PLearn::PLS::train().

real PLearn::p_value (  real  mu, real  vn )

Definition at line 219 of file pl_erf.cc. References gauss_01_cum(), and sqrt(). Referenced by KS_test().

real PLearn::paired_t_test (  Vec  u, Vec  v )

Given two paired sets u and v of n measured values, the paired t-test determines whether they differ from each other in a significant way under the assumptions that the paired differences are independent and identically normally distributed. Definition at line 254 of file stats_utils.cc. References PLearn::TVec< T >::length(), mean(), MISSING_VALUE, PLWARNING, sqrt(), and sumsquare(). Referenced by PLearn::SequentialModelSelector::test().

void PLearn::parseSizeFromRemainingLines (  const string &  filename, ifstream &  in, bool &  could_be_old_amat, int &  length, int &  width )

Definition at line 1510 of file MatIO.cc. References countNonBlankLinesOfFile(), getNextNonBlankLine(), and split(). Referenced by loadAscii().

template<class T> void partialSortRows (  const TMat< T > &  mat, int  k, int  sortk = 1, int  col = 0 )

Uses partial_sort. Sorts only the first k smallests rows and put it in the k first rows. The other rows are in an arbitrary order. If sortk is 0, the k smallest rows are put in the k first rows but in an arbitrary order. This implementation should be very efficient, but it does two memory allocation: a first one of mat.length()*(sizeof(real)+sizeof(int)) and a second one of mat.length()*sizeof(int). Definition at line 126 of file TMat_sort.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::swapRows(). Referenced by PLearn::Kernel::computeKNNeighbourMatrixFromDistanceMatrix(), and PLearn::Kernel::computeNearestNeighbors().

bool PLearn::pathexists (  const string &  path  )

returns true if the given path points to an existing regular file or directory Definition at line 129 of file fileutils.cc. Referenced by PLearn::SequentialValidation::build_(), PLearn::PTester::build_(), PLearn::VVMatrix::createPreproVMat(), getDataSet(), getDataSetDate(), PLearn::VVMatrix::getPrecomputedDataName(), PLearn::VVMatrix::isPrecomputedAndUpToDate(), locateDatasetAliasesDir(), PLearn::VMatrix::lockMetaDataDir(), makeFileNameValid(), and PLearn::Experiment::run().

char peekAfterSkipBlanks (  istream &  in  )  [inline]

peeks the first char after removal of blanks Definition at line 158 of file fileutils.h. Referenced by PLearn::VMatLanguage::generateCode(), and PLearn::RealMapping::read().

char peekAfterSkipBlanksAndComments (  istream &  in  )  [inline]

peeks the first char after removal of blanks and comments Definition at line 161 of file fileutils.h. References skipBlanksAndComments(). Referenced by readAndMacroProcess().

string PLearn::pgetline (  istream &  in = cin  )

returns the next line read from the stream, after removing any trailing '' and/or ' ' Definition at line 293 of file stringutils.cc. References removenewline(). Referenced by extractWordSet(), PLearn::ShellProgressBar::getAsciiFileLineCount(), interactiveDisplayCDF(), PLearn::VMatrix::loadFieldInfos(), PLearn::WordNetOntology::loadPredominentSyntacticClasses(), PLearn::GraphicalBiText::loadSensemap(), main(), and PLearn::Grapher::plot_1D_regression().

real PLearn::pl_dgammlndz (  real  z  )

d(pl_gammln(z))/dz derivate of pl_gammln(z) Definition at line 74 of file pl_erf.cc. References log(), and pl_gammln_cof.

real PLearn::pl_erf (  real  x  )

The error function. Definition at line 151 of file pl_erf.cc. References pl_gammq(), and x. Referenced by PLearn::ErfVariable::fprop(), and gauss_01_cum().

real PLearn::pl_gammln (  real  z  )

function gamma returns log(Gamma(z)), where Gamma(z) = ^infty t^{z-1}*e^{-t} dt Definition at line 60 of file pl_erf.cc. References log(), Pi, and pl_gammln_cof. Referenced by pl_gcf(), and pl_gser().

real PLearn::pl_gammq (  real  a, real  x )

returns the incomplete gamma function Q(a,x) = 1 - P(a,x) it either uses the series or the continued fraction formula Definition at line 141 of file pl_erf.cc. References pl_gcf(), pl_gser(), PLERROR, and x. Referenced by pl_erf().

real PLearn::pl_gcf (  real  a, real  x )

returns the continued fraction representation of the incomplete gamma function Definition at line 110 of file pl_erf.cc. References EPS, exp(), FPMIN, ITMAX, log(), pl_gammln(), PLERROR, and x. Referenced by pl_gammq().

real PLearn::pl_gser (  real  a, real  x )

returns the series value of the incomplete gamma function Definition at line 89 of file pl_erf.cc. References exp(), ITMAX, log(), pl_gammln(), PLERROR, sum(), and x. Referenced by pl_gammq().

bool PLearn::pl_isnumber (  const string &  s, float *  dbl )

Definition at line 96 of file stringutils.cc. References MISSING_VALUE, and removeblanks().

bool PLearn::pl_isnumber (  const string &  s, double *  dbl = NULL )

Definition at line 83 of file stringutils.cc. References MISSING_VALUE, and removeblanks(). Referenced by PLearn::VMatLanguage::generateCode(), PLearn::VecStatsCollector::getFieldNum(), getList(), loadAscii(), loadUCIMLDB(), PLearn::PDate::PDate(), PLearn::VMatLanguage::preprocess(), readAndMacroProcess(), PLearn::StrTableVMatrix::StrTableVMatrix(), and viewVMat().

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  SequentialLearner  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  StatefulLearner  , "PLearner with an internal state"  , "PLearner with an internal state.\n""It   replaces, for efficacity and compatibility  reasons, SequentialLearner."  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  PLearner  , "The base class for all PLearn learners."  , ""  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  Learner  , "DEPRECATED CLASS: Derive from PLearner instead"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  VMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

VMatrix *.

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  Splitter  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  RowBufferedVMatrix  , "A base class for VMatrices that keep the last row(s) in a buffer for faster access."  , ""  )

RowBufferedVMatrix *.

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  DatedVMatrix  , "ONE_LINE_DESC"  , "ONE_LINE_HELP"  )

DatedVMatrix *.

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  Variable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  UnaryVariable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  NaryVariable  , "ONE LINE DESCR"  , "NO HELP"  )

NaryVariable *.

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  BinaryVariable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  Optimizer  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  HyperOptimizer  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  ObjectGenerator  , "ObjectGenerator is the base class for implementing object-generation techniques."  , "The OptionGenerator takes a template   Object, and from a list of  options, \n""it will generate another Object(or a complete list).\n"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  StatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  Smoother  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_ABSTRACT_OBJECT (  Kernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  TangentLearner  , "Learns local tangent plane of the manifold near which the data lie."  , "This learner models a manifold near which the data are supposed to lie.\n""The manifold is represented by a function which predicts a basis for the\n""tangent planes at each point   x, given x in R^n.Let f_i(x) be the predicted i-th tangent\n""vector(in R^n).Then we will optimize the parameters that define the d functions f_i by\n""pushing the f_i so that they span the local tangent directions.Three criteria are\n""  possible, according to the 'training_targets'  , 'normalize_by_neighbor_distance'and\n""'use_subspace_distance'option.The default criterion is the recommanded  one, with\n""  training_targets = 'local_neighbors', normalize_by_neighbor_distance  = 1, \n""and  use_subspace_distance = 0 (it really did not work well in our experiments with\n""use_subspace_distance=1). This corresponds to the following cost function:\n""    sum_x sum_j min_w ||t(x,j) - sum_i w_i f_i(x)||^2 / ||t(x,j)||^2\n""where x is an example, t(x, j) is the difference vector between x and its j-th  neighbor, \n""and the w_i are chosen freely for each j and x and correspond to the weights given to\n""each basis vector f_i(x) to obtain the projection of t(x, j) on the tangent plane.\n""More  generally, if  use_subspace_distance )

PLEARN_IMPLEMENT_OBJECT (  SpectralClustering  , "Spectral Clustering dimensionality reduction."  , "The current code only performs dimensionality   reduction, and does not do\n""clustering."  )

PLEARN_IMPLEMENT_OBJECT (  PCA  , "Performs a Principal Component Analysis preprocessing (projecting on the principal directions)."  , "This learner finds the empirical covariance matrix of the input part of\n""the training   data, and learns to project its input vectors along the\n""principal eigenvectors of that  matrix, optionally scaling by the inverse\n""of the square root of the eigenvalues(to obtained 'sphered', i.e.\n""Normal(0, I) data).\n""Alternative EM algorithms are  provided, that may be useful when there is\n""a lot of data or the dimension is very high.\n"  )

PLEARN_IMPLEMENT_OBJECT (  LLE  , "Performs Locally Linear Embedding."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  KPCATangentLearner  , "Tangent learning based on KPCA Kernel"  , "MULTI-LINE \nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  KernelProjection  , "Performs dimensionality reduction by learning eigenfunctions of a kernel."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  KernelPCA  , "Kernel Principal Component Analysis"  , "Perform PCA in a feature space   phi(x) )

PLEARN_IMPLEMENT_OBJECT (  IsomapTangentLearner  , "Tangent learning based on Isomap Kernel"  , "MULTI-LINE \nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  Isomap  , "Performs ISOMAP dimensionality reduction."  , "Be careful that when looking for the 'knn' nearest neighbors of a point   x, \n""we consider all points from the training data  D, including x itself if it\n""belongs to D.  Thus, to obtain the same result as with the classical ISOMAP\n""  algorithm, one should use one more neighbor.\n""Note also that when used out-of-  sample, this will result in a different output\n""than an algorithm applying the same  formula, but considering one less neighbor.\n"  )

PLEARN_IMPLEMENT_OBJECT (  GaussianContinuum  , "Learns a continuous (uncountable) Gaussian mixture with non-local parametrization"  )

PLEARN_IMPLEMENT_OBJECT (  EntropyContrast  , "Performs a EntropyContrast search"  , "Detailed Description "  )

PLEARN_IMPLEMENT_OBJECT (  Train  , "An easy PTester"  , "  Sometimes )

PLEARN_IMPLEMENT_OBJECT (  TestMethod  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  PTester  , "Manages a learning   experiment, with training and estimation of generalization error."  , "The PTester class allows you to describe a typical learning experiment that you wish to   perform, \n""as a training/testing of a learning algorithm on a particular dataset.\n""The splitter is used to obtain one or several(such as for k-fold) splits of the dataset\n""and training/testing is performed on each split.\n""Requested statistics are  computed, and all requested results are written in an appropriate\n""file inside the specified experiment directory.\n""Statistics can be either specified entirely from the 'statnames'  option, or built from\n""'statnames'and 'statmask'.For  instance, one may set:\n""  statnames = [ \"NLL\" \"mse\" ]\n""   statmask  = [ [ \"E[*]\" ] [ \"test1.*\" \"test2.*\" ] [ \"E[*]\" \"STDERROR[*]\" ] ]\n""and this will compute:\n""   E[test1.E[NLL]], STDERROR[test1.E[NLL]], E[test2.E[NLL]], STDERROR[test2.E[NLL]]\n""   E[test1.E[mse]], STDERROR[test1.E[mse]], E[test2.E[mse]], STDERROR[test2.E[mse]]\n" )

PLEARN_IMPLEMENT_OBJECT (  SequentialValidation  , "The SequentialValidation class allows you to describe a typical ""sequential validation experiment that you wish to perform."  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SequentialModelSelector  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MovingAverage  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  EmbeddedSequentialLearner  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  PLS  , "Partial Least Squares Regression (PLSR)."  , "You can use this learner to perform   regression, and/or dimensionality\n""reduction.\n""PLS regression assumes the target Y and the data X are linked through:\n""  Y = T.Q' + E\n"" X = T.P' + F\n""The underlying coefficients T (the 'scores') and the loading matrices\n""Q and P are seeked. It is then possible to compute the prediction y for\n""a new input x, as well as its score vector t(its representation in\n""lower-dimensional coordinates).\n""The available algorithms to perform PLS(chosen by the 'method'option) are:\n""\n""  = ===  PLS1  ====\n""The classical PLS algorithm, suitable only for a 1-dimensional target.The\n""following algorithm is taken from 'Factor Analysis in Chemistry'  , with an\n""additional loop   that(I believe) was missing:\n""(1) Let X(n x p) = the centered and normalized input data\n""     Let y (n x 1) = the centered and normalized target data\n""     Let k be the number of components extracted\n"" (2) s = y\n"" (3) lx' = s' X, s  = X lx (normalized)\n"" (4) If s has changed by more than 'precision', loop to(3)\n""(5)  ly = s' y\n"" (6) lx' = s' X\n"" (7) Store s, lx and ly in the columns of respectively  T, P and Q\n""(8)  X = X - s lx', y  = y - s ly, loop to(2) k times\n""(9) Set  W = (T P')^(+) T, where the^(+) is the right pseudoinverse\n""\n""  = === Kernel ====\n""The code implements a NIPALS-PLS-like algorithm, which is a so-called\n""'kernel'algorithm(faster than more classical implementations).\n""The  algorithm, inspired from 'Factor Analysis in Chemistry'and above all\n""www.statsoftinc.com/textbook/stpls.  html, is the following:\n""  (1) Let X(n x p) = the centered and normalized input data\n""     Let Y (n x m) = the centered and normalized target data\n""     Let k be the number of components extracted\n"" (2) Initialize A_0 = X'Y, M_0  = X'X, C_0  = Identity(p), and  h = 0\n"" (3) q_h = largest eigenvector of B_h = A_h' A_h, found by the NIPALS method:\n""(3.a)  q_h = a (normalized) randomn column of B_h\n""       (3.b) q_h = B_h q_h\n""       (3.c) normalize q_h\n""       (3.d) if q_h has changed by more than 'precision', go to(b)\n""(4)  w_h = C_h A_h q_h, normalize w_h and store it in a column of W(p x k)\n""(5)  p_h = M_h w_h, c_h  = w_h' p_h, p_h  = p_h / c_h and store it in a column\n""     of P (p x k)\n"" (6) q_h = A_h' w_h / c_h, and store it in a column of Q(m x k)\n""(7) A_h+  1 = A_h - c_h p_h q_h'\n""     M_h+1 = M_h - c_h p_h p_h', \n""C_h+  1 = C_h - w_h p_h\n"" (8) h = h+1 )

PLEARN_IMPLEMENT_OBJECT (  LinearRegressor  , "Ordinary Least Squares and Ridge   Regression, optionally weighted"  , "This class performs OLS (Ordinary Least Squares) and Ridge   Regression, optionally on weighted\n""  data, by solving the linear equation(X'W X+weight_decay *n_examples *I)  theta = X'W Y\n""where X is the (n_examples x (1+inputsize)) matrix of extended inputs (with a 1 in the first column), \n""Y is   the(n_example x targetsize) )

PLEARN_IMPLEMENT_OBJECT (  ConstantRegressor  , "PLearner that outputs a constant (input-independent) vector.\n"  )

PLEARN_IMPLEMENT_OBJECT (  PTester  , "Evaluates the performance of a PLearner"  , "The PTester class allows you to describe a typical learning experiment that you wish to   perform, \n""as a training/testing of a learning algorithm on a particular dataset.\n""The splitter is used to obtain one or several(such as for k-fold) splits of the dataset\n""and training/testing is performed on each split.\n""Requested statistics are  computed, and all requested results are written in an appropriate\n""file inside the specified experiment directory.\n"  )

PLEARN_IMPLEMENT_OBJECT (  Grapher  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  GenerateDecisionPlot  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  Experiment  , "DEPRECATED: use PTester instead"  , ""  )

PLEARN_IMPLEMENT_OBJECT (  TextSenseSequenceVMatrix  , "VMat class that takes another VMat which contains a sequence (rows) ""of words/sense/POS triplets extracted from a corpus and implements a ""representation of a target word and its context."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  Dictionary  , "Mapping string->int and int->string"  , "MULTI LINE\nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  GraphicalBiText  , "Probabilistically tag a bitext (english-other language) with senses from WordNet"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  TestingLearner  , "ONE LINE DESCRIPTION"  , "MULTI-LINE \nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  StackedLearner  , "Implements   stacking, that combines two levels of  learner, the 2nd level using the 1st outputs as inputs"  , "Stacking is a generic strategy in which two levels (or more, recursively) of learners\n""are combined. The lower level may have one or more   learners, and they may be trained\n""on the same or different data from the upper level single learner.The outputs of the\n""1st level learners are concatenated and serve as inputs to the second level learner.\n""IT IS ASSUMED THAT ALL BASE LEARNERS HAVE THE SAME NUMBER OF INPUTS AND OUTPUTS\n""There is also the option to copy the input of the 1st level learner as additional\n""inputs for the second level(put_raw_input).\n""A Splitter can optionally be provided to specify how to split the data into\n""the training/validation sets for the lower and upper levels respectively\n"  )

PLEARN_IMPLEMENT_OBJECT (  SelectInputSubsetLearner  , "PLearner which selects a subset of the inputs for an embedded learner."  , "This learner class contains an embedded learner for which it selects a subset of the inputs.\n""The subset can be either selected explicitly or chosen randomly (the user chooses what fraction\n""of the original inputs will be selected)."  )

PLEARN_IMPLEMENT_OBJECT (  NNet  , "Ordinary Feedforward Neural Network with 1 or 2 hidden layers"  , "Neural network with many bells and whistles..."  )

PLEARN_IMPLEMENT_OBJECT (  NeuralNet  , "DEPRECATED: Use NNet instead"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  NeighborhoodSmoothnessNNet  , "Feedforward neural network whose hidden units are smoothed according to input neighborhood\n"  , "TODO"  )

PLEARN_IMPLEMENT_OBJECT (  EmbeddedLearner  , "Wraps an underlying learner"  , "EmbeddedLearner implements nothing but forwarding \n""calls to an underlying learner. It is typically used as\n""baseclass for learners that are built on top of another learner"  )

PLEARN_IMPLEMENT_OBJECT (  AddCostToLearner  , "A PLearner that just adds additional costs to another PLearner."  , "In   addition, this learner can be used to compute costs on bags instead of\n""individual  samples, using the option 'compute_costs_on_bags'.\n""\n""Feel free to make this class evolve by adding new  costs, or rewriting it\n""in a better  fashion, because this one is certainly not perfect.\n""To use the lift  cost )

PLEARN_IMPLEMENT_OBJECT (  UniformDistribution  , "Implements uniform distribution over intervals."  , "  Currently, only very few methods are implemented.\n""For  example, to sample points in 2D  inx[a, b][c, d] )

PLEARN_IMPLEMENT_OBJECT (  UnconditionalDistribution  , "This class is a simplified version of PDistribution for unconditional distributions."  , "Its only goal is to hide the conditional side of PDistributions to make it simpler."  )

PLEARN_IMPLEMENT_OBJECT (  SpiralDistribution  , "Generates samples drawn from a 2D spiral"  )

PLEARN_IMPLEMENT_OBJECT (  PDistribution  , "PDistribution is the base class for distributions.\n"  , "PDistributions derive from PLearner   (as some of them may be fitted to data with train()), \n""but they have additional methods allowing for ex.to compute density or generate data points.\n""The default implementations of the learner-type methods for computing outputs and costs work as follows:\n""-the outputs_def option allows to choose which outputs are produced\n""-cost is a vector of size 1 containing only the negative log-  likelihood(NLL), i.e.-log_density.\n""A PDistribution may be conditional   P(Y|X), if the option 'conditional_flags'is set.If it is the  case, \n""the input should always be made of both the 'input'  part(X) and the 'target'part(Y), even if the\n""output may not need to use the Y part.The exception is when computeOutput() needs to be called\n""successively with the same value of X:in this  case, after a first call with both X and  Y, one may\n""only provide Y as  input, and X will be assumed to be unchanged.\n"  )

PLEARN_IMPLEMENT_OBJECT (  PConditionalDistribution  , "(THIS CLASS IS DEPRECATED, use PDistribution instead). Conditional distribution or conditional density model P(Y|X)"  , "Abstract superclass for conditional distribution classes.\n""It is a subclass of   PDistribution, with the added method\n""setInput(Vec &input)\n""to set  X, that must be called before PDistribution methods such as\n""  log_density, cdf  , survival_fn  , expectation  , variance  , generate.\n""The PDistribution option output_defs must be set to specify\n""what the PLearner method computeOutput will produce.If it is\n""set to 'l'  (log_density), 'd'  (density), 'c'  (cdf), or 's'(survival_fn)\n""then the input part of the data should contain both the input X and\n""the 'target'Y values(targetsize()==0).  Instead, if output_defs is set to\n"" 'e'  (expectation) or 'v'(variance), then the input part of the data should\n""contain only  X, while the target part should contain Y\n"  )

PLEARN_IMPLEMENT_OBJECT (  ManifoldParzen2  , "ManifoldParzen implements a manifold Parzen."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  LocallyWeightedDistribution  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  HistogramDistribution  , "Represents and possibly learns (using a smoother) a univariate distribution as a histogram."  , "This class represents a univariate distribution with a set of bins and their densities\n""The bins can be fixed or learned by a Binner   object )

PLEARN_IMPLEMENT_OBJECT (  GaussMix  , "Gaussian   mixture, either set non-parametrically or trained by EM."  , "GaussMix implements a mixture of L gaussians.\n""There are 4 possible parametrization types:\n"" - spherical : gaussians have covar   matrix = diag(sigma). Parameter used : sigma.\n"" - diagonal  : gaussians have covar matrix = diag(sigma_i). Parameters used : diags.\n"" - general   : gaussians have an unconstrained covariance matrix.\n""               The user specifies the number 'n_eigen' of eigenvectors kept when\n""               decomposing the covariance matrix. The remaining eigenvectors are\n""               considered as having a fixed eigenvalue equal to the next highest\n""               eigenvalue in the decomposition.\n"" - factor    : (not implemented!) as in the general case, the gaussians are defined\n""with K<=D   vectors(through KxD matrix 'V'), but these need not be\n""orthogonal/orthonormal.\n""The covariance matrix used will be V(t) V+psi with psi a D-vector\n""(through parameter diags).\n""2 parameters are common to all 4 types:\n""-alpha:the ponderation factor of the gaussians\n""-mu:their centers\n"  )

PLEARN_IMPLEMENT_OBJECT (  GaussianProcessRegressor  , "Basic version of Gaussian Process regression."  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  GaussianDistribution  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  EmpiricalDistribution  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  Distribution  , "This class is   deprecated, use PDistribution instead."  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ConditionalGaussianDistribution  , "ConditionalGaussianDistribution is a gaussian distribution ""in which the parameters could be learned or specified manually."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  ConditionalDistribution  , "ONE LINE DESCR"  , "You must call setInput to set the condition before using the distribution"  )

PLEARN_IMPLEMENT_OBJECT (  ConditionalDensityNet  , "Neural Network that Implements a Positive Random Variable Conditional Density"  , "The input vector is used to compute parameters of an output density or output\n""cumulative distribution as well as output expected value. The ASSUMPTIONS\n""on the generating distribution P(Y|X) are the following:\n"" * Y is a single real value\n"" * 0 <= Y <=   maxY, with maxY a known finite value\n""*the density has a mass point at  Y = 0\n""  * the density is continuous for Y )

PLEARN_IMPLEMENT_OBJECT (  MultiInstanceNNet  , "Multi-instance feedforward neural network for probabilistic classification"  , "The data has the form of a set of input vectors x_i associated with a single\n""label y. Each x_i is an instance and the overall set of instance is called a bag.\n""We don't know which of the inputs is responsible for the   label, i.e.\n""there are hidden(not observed) labels y_i associated with each of the inputs x_i.\n""We also know that  y = 1 if at least one of the y_i is 1, otherwise  y = 0, i.e.\n""  y = y_1 or y_2 or ... y_m\n""In terms of probabilities )

PLEARN_IMPLEMENT_OBJECT (  ClassifierFromDensity  , "A classifier built from density estimators using Bayes' rule."  , "ClassifierFromDensity allowd to build a classifier\n""by building one density estimator for each   class, \n""and using Bayes rule to combine them.\n"  )

PLEARN_IMPLEMENT_OBJECT (  AdaBoost  , "AdaBoost boosting algorithm for TWO-CLASS classification"  , "Given a classification weak-  learner, this algorithm\"boosts\" it in\n""order to obtain a much more powerful classification algorithm.\n""The classifier is two-  class, returning 0 or  1 )

PLEARN_IMPLEMENT_OBJECT (  YMDDatedVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  VVMatrix  , "A VMat that reads a '.vmat' file."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  VVec  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  VMatrixFromDistribution  , "A VMatrix built from sampling a distribution"  , "VMatrixFromDistribution implements a VMatrix whose data rows are drawn from a distribution\n""or that contains the density or log density sampled on a grid (depending on \"mode\").\n""The matrix is computed in memory at build time\n"  )

PLEARN_IMPLEMENT_OBJECT (  PreprocessingVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  VMatLanguage  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  VecExtendedVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

VecExtendedVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  UpsideDownVMatrix  , "ONE LINE DESCRIPTION"  , "MULTI-LINE \nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  UniformVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

Uniform VMatrix *.

PLEARN_IMPLEMENT_OBJECT (  UniformizeVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

UniformizeVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  TransposeVMatrix  , "A VMatrix that sees the transpose of another VMatrix."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  TrainValidTestSplitter  , "This splitter will basically return ."  [Train+Valid, Test], "The train test returned by the splitter is formed from the first n_train+n_valid\n""samples in the dataset. The other samples are returned in the test set.\n""The validation and test sets (given by the samples after the n_train-th one) can\n""be shuffled in order to get a different validation and test sets at each split.\n""  However, the train set(the first n_train samples) remains fixed."  )

PLEARN_IMPLEMENT_OBJECT (  TrainTestSplitter  , "ONE LINE DESCR"  , "TrainTestSplitter implements a single split of the dataset into a training-set and a test-set (the test part being the last few samples of the dataset)"  )

PLEARN_IMPLEMENT_OBJECT (  TrainTestBagsSplitter  , "Splits a dataset in two parts"  , "TrainTestBagsSplitter implements a single split of the dataset into\n""a training set and a test set (the test part being the last few samples of the dataset)\n""Optionally a third set is provided which is the training set itself (in order to test on it)\n"  )

PLEARN_IMPLEMENT_OBJECT (  ToBagSplitter  , "A Splitter that makes any existing splitter operate on bags only."  , "The dataset provided must contain bag   information, as described in\n""SumOverBagsVariable"  )

PLEARN_IMPLEMENT_OBJECT (  TestInTrainSplitter  , "A splitter that adds the test points given by another splitter into the training set."  , "The underlying splitter should return train / test sets of constant size.\n""For   instance, if the underlying splitter returns 3 splits of(train, test)\n""pairs with size 2000 and  500, this splitter will return:\n""-for 'percentage_added'  = = 5%, 15 splits of size 2100 and  100, with each\n""test point appearing once and only once in a train set and a test set\n""-for 'percentage_added'  = = 20%, 6 splits of size  2400, 400 and  2400, 100  , with\n""each test point appearing once or more in a train  set, and only once in a\n""test set(note that the test points appearing more than once in a train set\n""will be those at the beginning of the test sets returned by the underlying\n""splitter)\n"  )

PLEARN_IMPLEMENT_OBJECT (  TemporalHorizonVMatrix  , "ONE LINE DESCR"  , " VMat class that delay the last entries of an underlying VMat by a certain horizon.\n"  )

TemporalHorizonVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  SubVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

SubVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  SubInputVMatrix  , "A VMat that only takes part of the input of its source VMat."  , "This can be useful for instance to only take the first k components\n""after applying some dimensionality reduction method."  )

PLEARN_IMPLEMENT_OBJECT (  StrTableVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SparseVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

SparseVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  SourceVMatrixSplitter  , "Returns the splits of an underlying   splitter, seen by a SourceVMatrix."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  SourceVMatrix  , "Super-class for VMatrices that point to another one (the source vmatrix)"  , ""  )

PLEARN_IMPLEMENT_OBJECT (  SortRowsVMatrix  , "Sort the samples of a VMatrix according to one (or more) given columns."  , "The implementation is not efficient at   all, feel free to improve it!"  )

SortRowsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ShiftAndRescaleVMatrix  , "ONE LINE DESCR"  , "ShiftAndRescaleVMatrix allows to shift and scale the first n_inputs columns of an underlying_vm.\n"  )

ShiftAndRescaleVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  SequentialSplitter  , "ONE LINE DESCR"  , "SequentialSplitter implements several   splits, TODO:Comments"  )

PLEARN_IMPLEMENT_OBJECT (  SelectRowsVMatrix  , "VMat class that selects samples from a source matrix according to given vector of indices."  , ""  )

SelectRowsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  SelectRowsFileIndexVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

SelectRowsFileIndexVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  SelectColumnsVMatrix  , "Selects variables from a source matrix according to given vector of indices."  , "  Alternatively, the variables can be given by their names."  )

SelectColumnsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  RowsSubVMatrix  , "ONE LINE DESCRIPTION"  , "MULTI-LINE \nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  RepeatSplitter  , "Repeat a given splitter a certain amount of   times, with the possibility to\n""shuffle randomly the dataset each time"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  RemoveRowsVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

RemoveRowsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  RemoveDuplicateVMatrix  , "A VMatrix that removes any duplicated entry in its source VMat."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  RemapLastColumnVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

RemapLastColumnVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  RegularGridVMatrix  , "ONE LINE DESCR"  , "RegularGridVMatrix represents the list of coordinates along a regularly spaced grid."  )

PLEARN_IMPLEMENT_OBJECT (  RangeVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

Range VMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ProcessingVMatrix  , "ONE LINE DESCRIPTION"  , "MULTI-LINE \nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  PrecomputedVMatrix  , "VMatrix that caches (pre-computes on disk) the content of a source vmatrix"  , "This sub-class of SourceVMatrix pre-computes the content of a source vmatrix\n""in a dmat or pmat file. The name of the disk file is obtained from the metadatadir option\n""followed by precomp.dmat or precomp.pmat"  )

PLEARN_IMPLEMENT_OBJECT (  PLearnerOutputVMatrix  , "Use a PLearner (or a set of them) to transform the input part of a data set into the learners outputs"  , "The input part of this VMatrix is obtained from the input part an original data set on which\n""one or more PLearner's computeOutput method is applied. The other columns of the original data set\n""are copied as is.   Optionally, the raw input can be copied as well\n""always in the input part of the new VMatrix.The order of the elements of a new row is as follows:\n""-the outputs of the learners(concatenated) when applied on the input part of the original  data, \n""-  optionally, the raw input part of the original  data, \n""-all the non-input columns of the original data."  )

PLEARN_IMPLEMENT_OBJECT (  PairsVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

PairsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  OneHotVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

OneHotVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  MultiInstanceVMatrix  , "Virtual Matrix for a multi instance dataset"  , "In a multi-instance dataset examples come in 'bags' with only one target label\n""for each bag. This class is built upon a source text file that describes such\n""a dataset (see the help on the 'filename' option for format details).\n""The resulting VMatrix shows the following structure in its   rows, with\n""all the rows of a bag being consecutive.Each row represents an instance and has:\n""-the input features for the instance\n""-the bag's source_targetsize target values(repeated over bag instances)\n""-a bag signal integer that identifies the beginning and end of the bag:\n""1 means the first instance of the bag\n""2 means the last instance of the bag\n""3 is for a bag with a single row(=1+2)\n""0 is for intermediate instances.\n""The targetsize of the VMatrix is automatically set to source_targetsize+1\n""since the bag_signal is included(appended) in the target vector\n"  )

PLEARN_IMPLEMENT_OBJECT (  MovingAverageVMatrix  , "Perform moving average of given columns"  , "The user specifies one or more columns and for each such <column-name>\n""a moving average window size: a ma<windowsize>-<column-name> column is\n""created which will contain at row t the moving average from row t-<windowsize>+1\n""to t inclusively of <column-name>.\n"  )

PLEARN_IMPLEMENT_OBJECT (  MemoryVMatrix  , "A VMatrix whose data is stored in memory."  , "The data can either be given directly by a   Mat, or by another VMat that\n""will be precomputed in memory at build time.\n"  )

MemoryVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  LocalNeighborsDifferencesVMatrix  , "Computes the difference between each input row and its nearest neighbors."  , "For each row x of the source   VMatrix, the resulting row will be the\n""concatenation of n_neighbors  vectors, each of which is the difference\n""between one of the nearest neighbors of x in the source and x itself.\n"  )

PLEARN_IMPLEMENT_OBJECT (  LearnerProcessedVMatrix  , "ONE LINE DESCR"  , "LearnerProcessedVMatrix implements a VMatrix processed on the fly by a learner (which will optionally be first trained on the source vmatrix)"  )

PLEARN_IMPLEMENT_OBJECT (  KNNVMatrix  , "A VMatrix that sees the nearest neighbours of each sample in the source VMat."  , "Each sample is followed by its (knn-1) nearest neighbours.\n""To each row is appended an additional   target, which is:\n""-1 if it is the first of a bag of  neighbours, \n""-2 if it is the last of a  bag, \n""-0 if it is none of  these, \n""-3 if it is both(only for knn==1).\n""In  addition, if a kernel_pij kernel is  provided, in the input part of the VMatrix\n""is appended  p_ij )

PLEARN_IMPLEMENT_OBJECT (  KFoldSplitter  , "K-fold cross-validation splitter."  , "KFoldSplitter implements K splits of the dataset into a training-set and a test-set.\n""If the number of splits is higher than the number of   examples, leave-one-out cross-validation\n""will be performed."  )

PLEARN_IMPLEMENT_OBJECT (  KernelVMatrix  , "ONE LINE DESC"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  JulianizeVMatrix  , "ONE LINE DESCR"  , "JulianizeVMatrix provides a conversion from a VMat containing dates\n""in an explicit 3-column (YYYY,MM,DD) or 6-column (YYYY,MM,DD,HH,MM,SS)\n""format to a Julian day number format (including fractional part to\n""represent the hour within the day). The dates can be at any   columns, \n""not only columns 0-2(or 0-5).More than a single date can be\n""converted.\n"  )

PLEARN_IMPLEMENT_OBJECT (  JoinVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  InterleaveVMatrix  , "ONE LINE DESC"  , "ONE LINE HELP"  )

InterleaveVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  IndexedVMatrix  , "ONE LINE DESCR"  , " VMat class that sees a matrix as a collection of triplets (row, column, value)\n""Thus it is a N x 3   matrix )

PLEARN_IMPLEMENT_OBJECT (  GramVMatrix  , "Computes the Gram matrix of a given kernel."  , "  Currently, this class inherits from a  MemoryVMatrix, and the Gram matrix\n""is stored in memory.\n"  )

PLEARN_IMPLEMENT_OBJECT (  GetInputVMatrix  , "This VMatrix only sees the input part of its source VMatrix."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  GeneralizedOneHotVMatrix  , "ONE LINE DESC"  , "ONE LINE HELP"  )

GeneralizedOneHotVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  FractionSplitter  , "A Splitter that can extract several subparts of a dataset in each split."  , "Ranges of the dataset are specified explicitly as start:end   positions, \n""that can be absolute or relative to the number of samples in the training set."  )

PLEARN_IMPLEMENT_OBJECT (  ForwardVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  FinancePreprocVMatrix  , "ONE LINE DESCR"  , "FinancePreprocVMatrix implements a VMatrix with extra preprocessing columns."  )

PLEARN_IMPLEMENT_OBJECT (  FilterSplitter  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  FilteredVMatrix  , "A filtered view of its source vmatrix"  , "The filter is an exression in VPL language.\n""The filtered indexes are saved in the metadata   directory, that NEEDS to\n""be provided.\n"  )

PLEARN_IMPLEMENT_OBJECT (  FileVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

FileVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ExtendedVMatrix  , "ONE_LINE_DESC"  , "ONE_LINE_HELP"  )

ExtendedVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ExplicitSplitter  , "ONE LINE DESCR"  , "ExplicitSplitter allows you to define a 'splitter' by giving explicitly the datasets for each split\n""as a matrix VMatrices.\n""(This splitter in effect ignores the 'dataset' it is given with setDataSet) \n"  )

PLEARN_IMPLEMENT_OBJECT (  DiskVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  DBSplitter  , "A Splitter that contains several databases."  , "The databases to be used can be specified with the 'databases' option. "  )

PLEARN_IMPLEMENT_OBJECT (  DatedJoinVMatrix  , "Join two   vmatrices, taking into account a date field."  , "The two vmatrices play an asymmetric role. They are called\n""master and slave. The resulting vmatrix has one row for each row\n""of the master vmatrix (or optionally of the slave vmatrix). Its\n""columns are a concatenation of selected columns of the master vmatrix\n""and of selected columns of the slave which 'match' according to a rule\n""(always in the order: master fields, slave fields). Matchint is\n""obtained using shared 'key fields'.   Optionally, for  matching, a date field\n""in the master is forced to belong to a date interval in the  slave )

PLEARN_IMPLEMENT_OBJECT (  CumVMatrix  , "Add columns that a cumulated values of given columns"  , "The user specifies one or more columns and for each such <column-name>\n""a cum-<column-name> column is created which will contain the sum from row 0\n""to the current row of <column-name>.\n"  )

PLEARN_IMPLEMENT_OBJECT (  CrossReferenceVMatrix  , "ONE LINE DESC"  , "ONE LINE HELP"  )

CrossReferenceVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ConcatRowsVMatrix  , "Concatenates the rows of a number of VMat."  , "It can also be used to select fields which are common to those   VMat, \n""using the 'only_common_fields'option.\n""  Otherwise, the fields are just assumed to be those of the first VMat.\n"  )

ConcatRowsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ConcatRowsSubVMatrix  , "ONE LINE DESC"  , "ONE LINE HELP"  )

ConcatRowsSubVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  ConcatColumnsVMatrix  , "ONE LINE DESCR"  , "NO HELP"  )

ConcatColumnsVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  CompressedVMatrix  , "ONE LINE DESCR"  , "ONE LINE HELP"  )

PLEARN_IMPLEMENT_OBJECT (  CenteredVMatrix  , "A VMatrix that centers a dataset."  , "The empirical mean is subtracted to each row of the underlying VMat."  )

PLEARN_IMPLEMENT_OBJECT (  ByteMemoryVMatrix  , "ONE_LINE_DESCR"  , "ONE LINE HELP"  )

PLEARN_IMPLEMENT_OBJECT (  BootstrapVMatrix  , "A VMatrix that sees a bootstrap subset of its parent VMatrix.\n""This is not a real bootstrap since a sample can only appear once."  , ""  )

BootstrapVMatrix *.

PLEARN_IMPLEMENT_OBJECT (  BootstrapSplitter  , "A splitter whose splits are bootstrap samples of the original dataset"  , "BootstrapSplitter implements a ..."  )

PLEARN_IMPLEMENT_OBJECT (  BatchVMatrix  , "ONE LINE DESCR"  , "VMat class that replicates small parts of a matrix   (mini-batches), so that each mini-batch appears twice(consecutively)."  )

PLEARN_IMPLEMENT_OBJECT (  AutoVMatrix  , "Automatically builds an appropriate VMat given its specification."  , "AutoVMatrix tries to interpret the given 'specification' (it will call getDataSet) and\n""will be a wrapper around the appropriate VMatrix   type, simply forwarding calls to it.\n""AutoVMatrix can be used to access the UCI databases.\n"  )

PLEARN_IMPLEMENT_OBJECT (  AsciiVMatrix  , "ONE LINE DESCR"  , "AsciiVMatrix implements a file in ascii format"  )

PLEARN_IMPLEMENT_OBJECT (  WeightedSumSquareVariable  , "ONE LINE DESCR"  , "NO HELP"  )

WeightedSumSquareVariable *.

PLEARN_IMPLEMENT_OBJECT (  VecElementVariable  , "Variable that is the element of vector vec indexed by variable input"  , "NO HELP"  )

VecElementVariable *.

PLEARN_IMPLEMENT_OBJECT (  VarRowVariable  , "Variable that is the row of the input1 variable indexed by the input2 variable"  , "NO HELP"  )

VarRowVariable *.

PLEARN_IMPLEMENT_OBJECT (  VarRowsVariable  , "Variable that is a subset of a matrix's rows; ""input1 : matrix from which rows are selected; ""input2 : vector whose elements are row indices in input1"  , "NO HELP"  )

VarRowsVariable *.

PLEARN_IMPLEMENT_OBJECT (  VarElementVariable  , "ONE LINE DESCR"  , "NO HELP"  )

VarElementVariable *.

PLEARN_IMPLEMENT_OBJECT (  VarColumnsVariable  , "ONE LINE DESCR"  , "NO HELP"  )

VarColumnsVariable *.

PLEARN_IMPLEMENT_OBJECT (  VarArrayElementVariable  , "Selects one element of a VarArray according to a Var index */"  , "NO HELP"  )

VarArrayElementVariable *.

PLEARN_IMPLEMENT_OBJECT (  UnfoldedSumOfVariable  , "Variable that sums the value of a Func evaluated on each row of a matrix.\n"  , "  However, unlike the  SumOfVariable, it does so by unfolding the   Func(up to given maximum number\n""of times 'max_bag_size'), and it allows that number to be variable.Each of the unfolded Func\n""is applied on a different row of the input matrix.The number of rows to sum is specified on the\n""fly by another  input, the bag_size.\n"  )

UnfoldedSumOfVariable *.

PLEARN_IMPLEMENT_OBJECT (  UnfoldedFuncVariable  , "Variable that puts in the rows of its output matrix the value\n""of a Func evaluated on each row of an input matrix.\n"  , "The input_matrix and output matrix have n_unfold rows. A separate propagation path\n""is created that maps (using the Func as a template) each input row to each output row.\n""The parents of this variable include the non-input parents of the Func.\n"  )

UnfoldedFuncVariable *.

PLEARN_IMPLEMENT_OBJECT (  UnequalConstantVariable  , "A scalar var; equal 1 if input1!  = c, 0 otherwise"  , "NO HELP"  )

UnequalConstantVariable *.

PLEARN_IMPLEMENT_OBJECT (  UnaryHardSlopeVariable  , "Hard slope function whose Var input is only the argument of the function."  , "Maps x (elementwise) to 0 if x<left, 1 if x>  right, and linear in between otherwise."  )

UnaryHardSlopeVariable *.

PLEARN_IMPLEMENT_OBJECT (  TransposeProductVariable  , "Matrix product between transpose of matrix1 and matrix2"  , "NO HELP"  )

TransposeProductVariable *.

PLEARN_IMPLEMENT_OBJECT (  TimesVariable  , "Multiplies 2 matrix vars of same size elementwise"  , "NO HELP"  )

TimesVariable *.

PLEARN_IMPLEMENT_OBJECT (  TimesScalarVariable  , "Multiplies a matrix var by a scalar var"  , "NO HELP"  )

TimesScalarVariable *.

PLEARN_IMPLEMENT_OBJECT (  TimesRowVariable  , "Multiplies each row of a matrix var elementwise with a single row variable"  , "NO HELP"  )

TimesRowVariable *.

PLEARN_IMPLEMENT_OBJECT (  TimesConstantVariable  , "Multiplies a matrix var by a scalar constant"  , "NO HELP"  )

TimesConstantVariable *.

PLEARN_IMPLEMENT_OBJECT (  TimesColumnVariable  , "Multiplies each column of a matrix var elementwise with a single column variable"  , "NO HELP"  )

TimesColumnVariable *.

PLEARN_IMPLEMENT_OBJECT (  TanhVariable  , "ONE LINE DESCR"  , "NO HELP"  )

TanhVariable *.

PLEARN_IMPLEMENT_OBJECT (  SumVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SumVariable *.

PLEARN_IMPLEMENT_OBJECT (  SumSquareVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SumSquareVariable *.

PLEARN_IMPLEMENT_OBJECT (  SumOverBagsVariable  , "Variable that sums the value of a Func each time evaluated on a subsequence of a VMat\n"  )

SumOverBagsVariable *.

PLEARN_IMPLEMENT_OBJECT (  SumOfVariable  , "Variable that sums the value of a Func evaluated on each row of a VMat"  , "NO HELP"  )

SumOfVariable *.

PLEARN_IMPLEMENT_OBJECT (  SumAbsVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SumAbsVariable *.

PLEARN_IMPLEMENT_OBJECT (  SubsampleVariable  , "A subsample var; equals subsample(input, the_subsamplefactor)"  , "NO HELP"  )

SubsampleVariable *.

PLEARN_IMPLEMENT_OBJECT (  SubMatVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SubMatVariable *.

PLEARN_IMPLEMENT_OBJECT (  SubMatTransposeVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SubMatTransposeVariable *.

PLEARN_IMPLEMENT_OBJECT (  SquareVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SquareVariable *.

PLEARN_IMPLEMENT_OBJECT (  SquareRootVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SquareRootVariable *.

PLEARN_IMPLEMENT_OBJECT (  SourceVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SourceVariable *.

PLEARN_IMPLEMENT_OBJECT (  SoftSlopeVariable  , "This Var computes the soft_slope function"  , "The soft_slope function is a soft version of linear by parts function.\n""(as smoothness goes to infty). More precisely it converges to a function that is\n""0   in [-infty, left], linear  in[left, right], and 1  in[right, infty], and continuous\n""It is always monotonically increasing wrt x(positive derivative in x).\n""If the arguments are vectors than the operation is performed element by element on all of them.\n"  )

SoftSlopeVariable *.

PLEARN_IMPLEMENT_OBJECT (  SoftSlopeIntegralVariable  , "This Var computes the integral of the soft_slope function in an interval."  , "Compute the integral of soft_slope(x,s,l,r) over x from a to b\n"  )

SoftSlopeIntegralVariable *.

PLEARN_IMPLEMENT_OBJECT (  SoftplusVariable  , "This is the primitive of a sigmoid: log(1+exp(x))"  , "NO HELP"  )

SoftplusVariable *.

PLEARN_IMPLEMENT_OBJECT (  SoftmaxVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SoftmaxVariable *.

PLEARN_IMPLEMENT_OBJECT (  SoftmaxLossVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SoftmaxLossVariable *.

PLEARN_IMPLEMENT_OBJECT (  SignVariable  , "sign(x)  = 1 if x, 0  )

SignVariable *.

PLEARN_IMPLEMENT_OBJECT (  SigmoidVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SigmoidVariable *.

PLEARN_IMPLEMENT_OBJECT (  SemiSupervisedProbClassCostVariable  , "ONE LINE DESCR"  , "NO HELP"  )

SemiSupervisedProbClassVariable *.

PLEARN_IMPLEMENT_OBJECT (  RowSumVariable  , "Result is a single column that contains the sum of each row of the input"  , "NO HELP"  )

RowSumVariable *.

PLEARN_IMPLEMENT_OBJECT (  RowAtPositionVariable  , "Variables positionned inside a larger zero variable ..."  , "NO HELP"  )

RowAtPositionVariable *.

PLEARN_IMPLEMENT_OBJECT (  RightPseudoInverseVariable  , "ONE LINE DESCR"  , "NO HELP"  )

RightPseudoInverseVariable *.

PLEARN_IMPLEMENT_OBJECT (  ReshapeVariable  , "Variable that views another   variable, but with a different length() and width()""(the only restriction being that length()*width() remain the same)"  , "NO HELP"  )

ReshapeVariable *.

PLEARN_IMPLEMENT_OBJECT (  ProjectionErrorVariable  , "Computes the projection error of a set of vectors on a non-orthogonal basis.\n"  , "The first input is a set of n_dim vectors (possibly seen as a single vector of their concatenation)   f_i, each in R^n\n""The second input is a set of T vectors(possibly seen as a single vector of their concatenation)  t_j )

ProjectionErrorVariable *.

PLEARN_IMPLEMENT_OBJECT (  ProductVariable  , "Matrix product"  , "NO HELP"  )

ProductVariable *.

PLEARN_IMPLEMENT_OBJECT (  ProductTransposeVariable  , "Matrix product between matrix1 and transpose of matrix2"  , "NO HELP"  )

ProductTransposeVariable *.

PLEARN_IMPLEMENT_OBJECT (  PowVariableVariable  , "x^y where x and y are variables but y is scalar ""or it has the same size as x"  , "NO HELP"  )

PowVariableVariable *.

PLEARN_IMPLEMENT_OBJECT (  PowVariable  , "Elementwise pow (returns 0 wherever input is negative)"  , "NO HELP"  )

PowVariable *.

PLEARN_IMPLEMENT_OBJECT (  PlusVariable  , "Adds 2 matrix vars of same size"  , "NO HELP"  )

PlusVariable *.

PLEARN_IMPLEMENT_OBJECT (  PlusScalarVariable  , "Adds a scalar var to a matrix var"  , "NO HELP"  )

PlusScalarVariable *.

PLEARN_IMPLEMENT_OBJECT (  PlusRowVariable  , "Adds a single-row var to each row of a matrix var"  , "NO HELP"  )

PlusRowVariable *.

PLEARN_IMPLEMENT_OBJECT (  PlusConstantVariable  , "Adds a scalar constant to a matrix var"  , "NO HELP"  )

PlusConstantVariable *.

PLEARN_IMPLEMENT_OBJECT (  PlusColumnVariable  , "Adds a single-column var to each column of a matrix var"  , "NO HELP"  )

PlusColumnVariable *.

PLEARN_IMPLEMENT_OBJECT (  PLogPVariable  , "Returns the elementwise x*log(x) in a (hopefully!) numerically stable way"  , "NO HELP"  )

PLogPVariable *.

PLEARN_IMPLEMENT_OBJECT (  PDistributionVariable  , "Variable that represents a random variable according to some PDistribution object"  , ""  )

PDistributionVariable *.

PLEARN_IMPLEMENT_OBJECT (  OneHotVariable  , "Represents a vector of a given   lenth, that has value 1 at the index""given by another variable and 0 everywhere else"  , "NO HELP"  )

OneHotVariable *.

PLEARN_IMPLEMENT_OBJECT (  OneHotSquaredLoss   )

OneHotSquaredLoss *.

PLEARN_IMPLEMENT_OBJECT (  NllSemisphericalGaussianVariable  , "Computes the negative log-likelihood of a Gaussian on some data   point, depending on the nearest neighbors."  , " This class implements the negative log-likelihood cost of a Markov chain that\n"" uses semispherical gaussian transition probabilities. The parameters of the\n"" semispherical gaussians are a tangent   plane, two  variances, \n""one mean and the distance of the point with its nearest neighbors.\n""The two variances correspond to the shared variance of every manifold directions\n""and of every noise directions.\n""This variable is used to do gradient descent on the  parameters, but\n""not to estimate de likelihood of the Markov chain a some  point, which is\n""more complex to estimate.\n"  )

NllSemisphericalGaussianVariable *.

PLEARN_IMPLEMENT_OBJECT (  NegCrossEntropySigmoidVariable  , "Compute sigmoid of its first   input, and then computes the negative""cross-entropy cost"  , "NO HELP"  )

NegCrossEntropySigmoidVariable *.

PLEARN_IMPLEMENT_OBJECT (  NegateElementsVariable  , "Elementwise negation and inversion..."  , "NO HELP"  )

NegateElementsVariable *.

PLEARN_IMPLEMENT_OBJECT (  MulticlassLossVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MulticlassLossVariable *.

PLEARN_IMPLEMENT_OBJECT (  MinVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MinVariable *.

PLEARN_IMPLEMENT_OBJECT (  MinusVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MinusVariable *.

PLEARN_IMPLEMENT_OBJECT (  MinusTransposedColumnVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MinusTransposedColumnVariable *.

PLEARN_IMPLEMENT_OBJECT (  MinusScalarVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MinusScalarVariable *.

PLEARN_IMPLEMENT_OBJECT (  MinusRowVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MinusRowVariable *.

PLEARN_IMPLEMENT_OBJECT (  MinusColumnVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MinusColumnVariable *.

PLEARN_IMPLEMENT_OBJECT (  MiniBatchClassificationLossVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MiniBatchClassificationLossVariable *.

PLEARN_IMPLEMENT_OBJECT (  MaxVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MaxVariable *.

PLEARN_IMPLEMENT_OBJECT (  Max2Variable   )

Max2Variable *.

PLEARN_IMPLEMENT_OBJECT (  MatRowVariable  , "Variable that is the row of matrix mat indexed by variable input"  , "NO HELP"  )

MatRowVariable *.

PLEARN_IMPLEMENT_OBJECT (  MatrixSumOfVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MatrixSumOfVariable *.

PLEARN_IMPLEMENT_OBJECT (  MatrixSoftmaxVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MatrixSoftmaxVariable *.

PLEARN_IMPLEMENT_OBJECT (  MatrixSoftmaxLossVariable  , "ONE LINE DESCR"  , "NO HELP"  )

MatrixSoftmaxLossVariable *.

PLEARN_IMPLEMENT_OBJECT (  MatrixOneHotSquaredLoss  , "ONE LINE DESCR"  , "NO HELP"  )

MatrixOneHotSquaredLoss *.

PLEARN_IMPLEMENT_OBJECT (  MatrixInverseVariable  , "Matrix inversions... "  , "NO HELP"  )

MatrixInverseVariable *.

PLEARN_IMPLEMENT_OBJECT (  MatrixElementsVariable  , "Fills the elements of a matrix using the given scalar Variable ""  expression, that depends on index variables i and  j, that loop from""0 to ni-1 and 0 to nj-1 respectively."  , "NO HELP"  )

MatrixElementsVariable *.

PLEARN_IMPLEMENT_OBJECT (  MatrixAffineTransformVariable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MatrixAffineTransformFeedbackVariable  , "Affine transformation of a MATRIX variable."  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MarginPerceptronCostVariable  , "Compute sigmoid of its first   input, and then computes the negative""cross-entropy cost"  , "NO HELP"  )

MarginPerceptronCostVariable *.

PLEARN_IMPLEMENT_OBJECT (  LogVariable  , "ONE LINE DESCR"  , "NO HELP"  )

LogVariable *.

PLEARN_IMPLEMENT_OBJECT (  LogSumVariable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  LogSoftmaxVariable  , "ONE LINE DESCR"  , "NO HELP"  )

LogSoftmaxVariable *.

PLEARN_IMPLEMENT_OBJECT (  LogAddVariable   )

LogAddVariable *.

PLEARN_IMPLEMENT_OBJECT (  LiftOutputVariable  , "The result is the output if the target is   1, and the opposite of the output""otherwise.This variable is to be used with a  LiftStatsCollector, in a""stochastic gradient descent."  , "NO HELP"  )

LiftOutputVariable *.

PLEARN_IMPLEMENT_OBJECT (  LeftPseudoInverseVariable  , "ONE LINE DESCR"  , "NO HELP"  )

LeftPseudoInverseVariable *.

PLEARN_IMPLEMENT_OBJECT (  IsSmallerVariable  , "ONE LINE DESCR"  , "NO HELP"  )

IsSmallerVariable *.

PLEARN_IMPLEMENT_OBJECT (  IsMissingVariable  , "ONE LINE DESCR"  , "NO HELP"  )

IsMissingVariable *.

PLEARN_IMPLEMENT_OBJECT (  IsLargerVariable  , "ONE LINE DESCR"  , "NO HELP"  )

IsLargerVariable *.

PLEARN_IMPLEMENT_OBJECT (  IsAboveThresholdVariable  , "ONE LINE DESCR"  , "NO HELP"  )

IsAboveThresholdVariable *.

PLEARN_IMPLEMENT_OBJECT (  InvertElementsVariable  , "ONE LINE DESCR"  , "NO HELP"  )

InvertElementsVariable *.

PLEARN_IMPLEMENT_OBJECT (  InterValuesVariable  , "if   values = [x1, x2  ,   ..., x10]  , the resulting variable is"  [(x1+x2)/2,(x2+x3)/2,...(x9+x10)/2], "NO HELP"  )

InterValuesVariable *.

PLEARN_IMPLEMENT_OBJECT (  IndexAtPositionVariable  , "ONE LINE DESCR"  , "NO HELP"  )

IndexAtPositionVariable *.

PLEARN_IMPLEMENT_OBJECT (  IfThenElseVariable  , "Variable that represents the element-wise IF-THEN-ELSE"  , "NO HELP"  )

IfThenElseVariable *.

PLEARN_IMPLEMENT_OBJECT (  HardSlopeVariable  , "This Var computes the hard_slope function"  , "The hard_slope function is linear by parts function:\n""0   in [-infty, left], linear  in[left, right], and 1  in[right, infty], and continuous.\n""If the arguments are vectors than the operation is performed element by element on all of them.\n"  )

HardSlopeVariable *.

PLEARN_IMPLEMENT_OBJECT (  Function  , "Implements a function defined as a var graph"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ExtendedVariable  , "Variable that extends the input variable by appending rows at ""its top and bottom and columns at its left and right."  , "NO HELP"  )

ExtendedVariable *.

PLEARN_IMPLEMENT_OBJECT (  ExpVariable  , "ONE LINE DESCR"  , "NO HELP"  )

ExpVariable *.

PLEARN_IMPLEMENT_OBJECT (  ErfVariable  , "ONE LINE DESCR"  , "NO HELP"  )

ErfVariable *.

PLEARN_IMPLEMENT_OBJECT (  EqualVariable  , "A scalar var; equal 1 if   input1 = =input2, 0 otherwise"  , "NO HELP"  )

EqualVariable *.

PLEARN_IMPLEMENT_OBJECT (  EqualScalarVariable  , "A scalar var; equal 1 if   input1 = =input2, 0 otherwise"  , "NO HELP"  )

EqualScalarVariable *.

PLEARN_IMPLEMENT_OBJECT (  EqualConstantVariable  , "A scalar var; equal 1 if   input1 = =input2, 0 otherwise"  , "NO HELP"  )

EqualConstantVariable *.

PLEARN_IMPLEMENT_OBJECT (  ElementAtPositionVariable  , "A variable of size   length() x width() )

ElementAtPositionVariable *.

PLEARN_IMPLEMENT_OBJECT (  DuplicateScalarVariable  , "ONE LINE DESCR"  , "NO HELP"  )

DuplicateScalarVariable *.

PLEARN_IMPLEMENT_OBJECT (  DuplicateRowVariable  , "ONE LINE DESCR"  , "NO HELP"  )

DuplicateRowVariable *.

PLEARN_IMPLEMENT_OBJECT (  DuplicateColumnVariable  , "ONE LINE DESCR"  , "NO HELP"  )

DuplicateColumnVariable *.

PLEARN_IMPLEMENT_OBJECT (  DotProductVariable  , "Dot product between 2 matrices (or vectors) with same number of elements"  , "NO HELP"  )

DotProductVariable *.

PLEARN_IMPLEMENT_OBJECT (  DivVariable  , "Divide 2 matrix vars of same size elementwise"  , "NO HELP"  )

DivVariable *.

PLEARN_IMPLEMENT_OBJECT (  DilogarithmVariable  , "This Var computes the dilogarithm function"  )

DilogarithmVariable *.

PLEARN_IMPLEMENT_OBJECT (  DiagonalizedFactorsProductVariable  , "Variable that represents the leftmatrix*diag(vector)*rightmatrix product"  , "The three parents are respectively the left matrix   U, the center vector  d )

DiagonalizedFactorsProductVariable *.

PLEARN_IMPLEMENT_OBJECT (  DeterminantVariable  , "The argument must be a square matrix Var and the result is its determinant"  , "NO HELP"  )

DeterminantVariable *.

PLEARN_IMPLEMENT_OBJECT (  CutBelowThresholdVariable  , "ONE LINE DESCR"  , "NO HELP"  )

CutBelowThresholdVariable *.

PLEARN_IMPLEMENT_OBJECT (  CutAboveThresholdVariable  , "ONE LINE DESCR"  , "NO HELP"  )

CutAboveThresholdVariable *.

PLEARN_IMPLEMENT_OBJECT (  CrossEntropyVariable   )

CrossEntropyVariable *.

PLEARN_IMPLEMENT_OBJECT (  ConvolveVariable  , "A convolve var; equals convolve(input, mask)"  , "NO HELP"  )

ConvolveVariable *.

PLEARN_IMPLEMENT_OBJECT (  ConcatRowsVariable  , "Concatenation of the rows of several variables"  , "NO HELP"  )

ConcatRowsVariable *.

PLEARN_IMPLEMENT_OBJECT (  ConcatOfVariable  , "Concatenates the results of each operation in the loop into the resulting variable"  , "NO HELP"  )

ConcatOfVariable *.

PLEARN_IMPLEMENT_OBJECT (  ConcatColumnsVariable  , "Concatenation of the columns of several variables"  , "NO HELP"  )

ConcatColumnsVariable *.

PLEARN_IMPLEMENT_OBJECT (  ColumnSumVariable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ColumnIndexVariable  , "Return a row vector with the elements indexed in each column"  , "NO HELP"  )

ColumnIndexVariable *.

PLEARN_IMPLEMENT_OBJECT (  ClassificationLossVariable  , "Indicator(classnum==argmax(netout))"  , "NO HELP"  )

ClassificationLossVariable *.

PLEARN_IMPLEMENT_OBJECT (  BinaryClassificationLossVariable   )

BinaryClassificationLossVariable *.

PLEARN_IMPLEMENT_OBJECT (  ArgminVariable  , "Compute the index of the minimum value in the input"  , "NO HELP"  )

ArgminVariable *.

PLEARN_IMPLEMENT_OBJECT (  ArgminOfVariable  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ArgmaxVariable  , "Compute the index of the maximum value in the input"  , "NO HELP"  )

ArgmaxVariable *.

PLEARN_IMPLEMENT_OBJECT (  AffineTransformWeightPenalty  , "Affine transformation with Weight decay terms"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  AffineTransformVariable  , "Affine transformation of a vector variable."  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  AbsVariable  , "ONE LINE DESCR"  , "NO HELP"  )

AbsVariable *.

PLEARN_IMPLEMENT_OBJECT (  HTryCombinations  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  HCoordinateDescent  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  HTryAll  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  HSetVal  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  GradientOptimizer  , "Optimization by gradient descent."  , "GradientOptimizer is the simple usual gradient descent algorithm \n"" (the number of samples on which to estimate gradients before an \n"" update, which determines whether we are performing 'batch' \n"" 'stochastic' or even 'minibatch', is currently specified outside \n"" this class, typically in the numer of s/amples of the meanOf function \n"" to be optimized, as its 'nsamples' parameter). \n""Options for GradientOptimizer are : \n"" - start_learning_rate: <real> (0.01) \n"" the initial learning rate \n"" - decrease_constant: <real> (0) \n"" the learning rate decrease constant \n""\n""GradientOptimizer derives form Optimizer. \n"  [option_name:< type >(default)] )

PLEARN_IMPLEMENT_OBJECT (  ConjGradientOptimizer  , "Optimizer based on the conjugate gradient method."  , "The conjugate gradient algorithm is basically the following :\n""- 0: initialize the search direction   d = -gradient\n""- 1: perform a line search along direction d for the minimum of the gradient\n""- 2: move to this minimum, update the search direction d and go to step 1\n""There are various methods available through the options for both steps 1 and 2."  )

PLEARN_IMPLEMENT_OBJECT (  AdaptGradientOptimizer  , "An optimizer that performs gradient descent with learning rate adaptation."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  ShellScript  , "Allows one to run shell commands (especially within a script)."  , "This runnable object will execute the given shell commands when run."  )

PLEARN_IMPLEMENT_OBJECT (  RunObject  , "Allows to build a non-runnable object in a PLearn script."  , "This Object implements a run() method so that it can be used in\n""a PLearn   script, in order to build another Object given by the\n""'underlying_object'option without PLearn returning an error.\n"  )

PLEARN_IMPLEMENT_OBJECT (  NearestNeighborPredictionCost  , "ONE LINE DESCRIPTION"  , "MULTI LINE\nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  VecStatsCollector  , "Collects basic statistics on a vector"  , "VecStatsCollector allows to collect statistics on a series of vectors.\n""Individual vectors x are presented by calling   update(x), and this class will\n""collect both individual statistics for each element(as a Vec< StatsCollector >)\n""as well as(optionally) compute the covariance matrix."  )

PLEARN_IMPLEMENT_OBJECT (  QuantilesStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  LiftStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MaxStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MinStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SharpeRatioStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  StderrStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  StddevStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ExpMeanStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MeanStatsIterator  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  StatsCollector  , "Collects basic statistics"  , "A StatsCollector allows to compute basic global statistics for a series of   numbers, \n""as well as statistics within automatically determined ranges.\n""The first maxnvalues encountered values will be used as reference points to define\n""the  ranges, so to get reasonable  results, your sequence should be  iid, and NOT sorted!\n""\n""The following statistics are available:""-E Sample mean\n""-V Sample variance\n""-STDDEV Sample standard deviation\n""-STDERROR Standard error of the mean\n""-MIN Minimum value\n""-MAX Maximum value\n""-SUM Sum of observations\n""-SUMSQ Sum of squares\n""-FIRST First observation\n""-LAST Last observation\n""-N Total number of observations\n""-NMISSING Number of missing observations\n""-NNONMISSING Number of non-missing observations\n""-SHARPERATIO Mean divided by standard deviation\n"  )

PLEARN_IMPLEMENT_OBJECT (  ScaledConditionalCDFSmoother  , "This smoothes a low-resolution histogram using as prior a high-resolution one."  , "This class takes as 'prior_cdf' a detailed histogram (usually derived from\n""an unconditional distribution) and uses it to smooth a given survival\n""function and provide extra detail (high resolution).\n""Two smoothing formula are   provided, both of which guarantee that the smoothed\n""survival function takes the same value as the raw one at or near original bin\n""positions.In between the original bin  positions, the smoothed survival\n""is obtained by applying one of two possible  formula, according to the\n""preserve_relative_density option.\n"  )

PLEARN_IMPLEMENT_OBJECT (  ManualBinner  , "Binner with predefined cut-points."  , "ManualBinner implements a Binner for which cutpoints are predefined. ""It's getBinning function doesn't have to look at the data; it simply ""builds a RealMapping from the supplied bin_positions."  )

PLEARN_IMPLEMENT_OBJECT (  LimitedGaussianSmoother  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  LiftStatsCollector  , "Computes the performance of a binary classifier"  , "The following statistics can be requested out of getStat():\n""-   LIFT = % of positive examples in the first n samples, divided by the%of positive examples in the whole database\n""-  LIFT_MAX = best performance that could be achieved, if all positive examples were selected in the first n samples\n""  (where n=lift_fraction *nsamples).\n""IMPORTANT:if you add more samples after you call finalize()(or get any of the statistics above), some samples may\n""be wrongly discarded and further statistics may be wrong\n\n""Here are the typical steps to follow to optimize the lift with a neural network:\n""-add a lift_output cost to cost_funcs(e.g.cost_funcs=[\"stable_cross_entropy\" \"lift_output\"];)\n""- change the template_stats_collector of your PTester:\n""   template_stats_collector = \n""      LiftStatsCollector (\n""        output_column = \"lift_output\" ;\n""        opposite_lift = 1 ; # if you want to optimize the lift\n""        sign_trick = 1 ;\n""      )\n""- add the lift to its statnames:\n""    statnames = [ \"E[train.E[stable_cross_entropy]]\",\"E[test.E[stable_cross_entropy]]\",\n""                  \"E[train.LIFT]\", \"E[test.LIFT]\" ]\n""- maybe also change which_cost in your HyperOptimize strategy.\n" )

PLEARN_IMPLEMENT_OBJECT (  ConditionalStatsCollector  , "ONE LINE DESCRIPTION"  , "MULTI LINE\nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  ConditionalCDFSmoother  , "Smoother that combines a detailed prior curve with a rough input curve.\n"  , "This smoother is meant to smooth conditional distribution   functions, using\n""a high-resolution prior cdf provided as a HistogramDistribution.Its 'smooth'\n""function takes a lower-resolution curve and smooths it using the prior\n""to fill the gaps."  )

PLEARN_IMPLEMENT_OBJECT (  Binner  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  WeightedCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SquaredErrorCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SourceKernel  , "A kernel built upon an underlying source kernel"  , "The default behavior of a SourceKernel is to forward all calls to the underlying\n""kernel.   However, subclasses will probably want to override the methods to perform\n""more complex operations."  )

PLEARN_IMPLEMENT_OBJECT (  SigmoidPrimitiveKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SigmoidalKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  SelectedOutputCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ScaledLaplacianKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ScaledGeneralizedDistanceRBFKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ScaledGaussianKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ReconstructionWeightsKernel  , "Computes the reconstruction weights of a point given its neighbors."  , "K(x, x_i)  = the weight of x_i in the reconstruction of x by its knn\n""nearest neighbors. More precisely, we compute weights W_i such that\n""||x-\\sum_j W_i x_i||^2 is  minimized, and   K(x, x_i) = W_i.\n""If the second argument is not in the training set )

PLEARN_IMPLEMENT_OBJECT (  QuadraticUtilityCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  PricingTransactionPairProfitFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  PrecomputedKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  PowDistanceKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  PolynomialKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  NormalizedDotProductKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  NegOutputCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  NegLogProbCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  NegKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  MulticlassErrorCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  LogOfGaussianDensityKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  LLEKernel  , "The kernel used in Locally Linear Embedding."  , "This kernel is the (weighted) sum of two kernels K' and K''  )

PLEARN_IMPLEMENT_OBJECT (  LiftBinaryCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  LaplacianKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  GeodesicDistanceKernel  , "Computes the geodesic distance based on k nearest neighbors."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  GeneralizedDistanceRBFKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  GaussianKernel  , "The good old Gaussian kernel."  , ""  )

PLEARN_IMPLEMENT_OBJECT (  GaussianDensityKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  DotProductKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  DivisiveNormalizationKernel  , "Divisive normalization of an underlying kernel."  , "From a positive kernel   K, defines a new kernel K'such that:\n""K'  (x, y) = K(x,y) / sqrt(E[K(x,x_i)] . E[K(x_i,y)])\n""where the expectation is performed on the data set.\n""If the 'remove_bias' option is set, then the expectation will not\n""take into account terms of the form K(x_i, x_i).\n"  )

PLEARN_IMPLEMENT_OBJECT (  DistanceKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  DirectNegativeCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  DifferenceKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ConvexBasisKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  CompactVMatrixPolynomialKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLearn::PLEARN_IMPLEMENT_OBJECT (  CompactVMatrixGaussianKernel  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ClassMarginCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ClassErrorCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  ClassDistanceProportionCostFunction  , "ONE LINE DESCR"  , "NO HELP"  )

PLEARN_IMPLEMENT_OBJECT (  AdditiveNormalizationKernel  , "Normalizes additively an underlying kernel with respect to a training set."  , "From a kernel   K, defines a new kernel K'such that:\n""K'  (x, y) = K(x,y) - E[K(x,x_i)] - E[K(x_i,y)] + E[K(x_i,x_j)]\n""where the expectation is performed on the data set.\n""If the 'remove_bias' option is set, then the expectation will not\n""take into account terms of the form K(x_i, x_i).\n""If the 'double_centering'option is  set, this kernel K'will be\n""multiplied by-1/2(this turns a squared distance kernel into a\n""centered dot product kernel).\n"  )

PLEARN_IMPLEMENT_OBJECT (  FilePStreamBuf  , "ONE LINE DESCRIPTION"  , "MULTI LINE\nHELP"  )

PLEARN_IMPLEMENT_OBJECT (  UCISpecification  , "Describes the specifications of a UCI database."  , "This object specifies characteristics of a database from the UCI machine\n""learning   repository, such as the input  size, target  size, etc...\n""It is intended to be used in a script put in the same directory as the\n""  database, in order to be loaded by the getDataSet() method.\n"  )

int PLearn::plearn_main (  int  argc, char **  argv )

Definition at line 51 of file plearn_main.cc. References endl(), file_exists(), PLERROR, prgname(), seed(), and stringvector(). Referenced by main().

Var plogp (  Var  v  )  [inline]

Definition at line 73 of file PLogPVariable.h. Referenced by entropy().

void PLearn::plotVMats (  char *  defs[], int  ndefs )

Definition at line 1236 of file vmatmain.cc. References c_str(), endl(), PLearn::PStream::get(), getDataSet(), PLearn::TVec< T >::length(), PLERROR, PLearn::TVec< T >::resize(), saveGnuplot(), split(), toint(), and tostring(). Referenced by vmatmain().

real PLearn::poidev (  real  xm  )

returns a poisson random number with lambda = "xm" Definition at line 426 of file random.cc. References exp(), log(), log_gamma(), Pi, sqrt(), and uniform_sample().

template<class T> int positionOfClosestElement (  const TVec< T > &  vec, const T &  value, bool  is_sorted_vec = false )

returns the position of the element in the vector that is closest to value If is_sorted_vec is true the procedure assumes the vector's elements are sorted in ascending order and uses a dichotomy search. Definition at line 1972 of file TMat_maths_impl.h. References binary_search(), PLearn::TVec< T >::data(), dist(), and PLearn::TVec< T >::length().

template<class T> int positionOfkthOrderedElement (  const TVec< T > &  vec, int  k )

Definition at line 1834 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), k, PLearn::TVec< T >::length(), and PLERROR. Referenced by kthOrderedElement().

Var positive (  Var  v  )  [inline]

Definition at line 79 of file CutBelowThresholdVariable.h. References cutBelowThreshold().

real positive (  real  a  )  [inline]

Definition at line 97 of file pl_math.h. Referenced by PLearn::ConditionalDensityNet::build_().

real positive_dilogarithm (  real  x  )

Definition at line 171 of file pl_math.cc. References log(), Pi, small_dilogarithm(), and x. Referenced by dilogarithm().

template<class T> TVec<T> positiveValues (  const TVec< T > &  vec  )

returns a vector composed of the values of v that are greater than 0; Definition at line 1956 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

Var pow (  Var  v, Var  power )  [inline]

Definition at line 90 of file PowVariableVariable.h.

Var pow (  Var  v, real  power )  [inline]

Definition at line 77 of file PowVariable.h. Referenced by PLearn::VMatrixFromDistribution::build_(), PLearn::AsciiVMatrix::build_(), PLearn::EntropyContrast::compute_diversity_cost(), PLearn::ScaledGeneralizedDistanceRBFKernel::evaluate(), PLearn::GeneralizedDistanceRBFKernel::evaluate(), PLearn::SemiSupervisedProbClassCostVariable::fprop(), PLearn::PowVariableVariable::fprop(), PLearn::SpiralDistribution::generate(), mypow(), norm(), pownorm(), PLearn::VMatLanguage::run(), sqrt(), and PLearn::PowVariable::symbolicBprop().

Var PLearn::powdistance (  Var  input1, Var  input2, real  n )

Definition at line 106 of file Var_utils.cc. References pownorm().

template<class T> T powdistance (  const TVec< T > &  vec1, const TVec< T > &  vec2 )  [inline]

Definition at line 756 of file TMat_maths_impl.h. References powdistance().

template<class T> T powdistance (  const TVec< T > &  vec1, const TVec< T > &  vec2, double  n )

Definition at line 712 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), mypow(), and PLERROR. Referenced by PLearn::PCA::computeCostsFromOutputs(), PLearn::LinearRegressor::computeCostsFromOutputs(), PLearn::ConstantRegressor::computeCostsFromOutputs(), PLearn::AddCostToLearner::computeCostsFromOutputs(), computeNearestNeighbors(), dist(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::PowDistanceKernel::evaluate(), PLearn::LogOfGaussianDensityKernel::evaluate(), PLearn::GaussianDensityKernel::evaluate(), PLearn::DistanceKernel::evaluate(), PLearn::CompactVMatrixGaussianKernel::evaluate(), PLearn::VMatrix::find(), powdistance(), and PLearn::ConstantRegressor::train().

Var PLearn::pownorm (  Var  input, real  n = 2.0 )

Definition at line 71 of file Var_utils.cc. References abs(), pow(), square(), and sum().

template<class T> T pownorm (  const TVec< T > &  vec  )  [inline]

Definition at line 691 of file TMat_maths_impl.h. References pownorm().

template<class T> T pownorm (  const TVec< T > &  vec, double  n )

Definition at line 657 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), mypow(), and val. Referenced by PLearn::GaussianKernel::addDataForKernelMatrix(), PLearn::NllSemisphericalGaussianVariable::bprop(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), PLearn::KernelProjection::computeCostsFromOutputs(), PLearn::AddCostToLearner::computeCostsFromOutputs(), PLearn::GaussMix::computeLogLikelihood(), PLearn::GaussianContinuum::computeOutput(), PLearn::ConjGradientOptimizer::conjpomdp(), PLearn::ConjGradientOptimizer::daiYuan(), diagonalOfSquare(), PLearn::RowBufferedVMatrix::dot(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::GaussianKernel::evaluate_i_j(), PLearn::GaussianKernel::evaluate_i_x(), PLearn::GaussianKernel::evaluate_x_i(), PLearn::ConjGradientOptimizer::findDirection(), PLearn::ConjGradientOptimizer::fletcherReeves(), PLearn::ProjectionErrorVariable::fprop(), PLearn::NllSemisphericalGaussianVariable::fprop(), PLearn::GaussianContinuum::get_image_matrix(), PLearn::GaussMix::kmeans(), norm(), PLearn::ConjGradientOptimizer::polakRibiere(), powdistance(), pownorm(), and PLearn::GaussianContinuum::train().

void PLearn::pretty_print_number (  char *  buffer, real  number )

print a number without unnecessary trailing zero's, into buffer Definition at line 96 of file general.cc. Referenced by saveAsciiWithoutSize().

void prettyprint_test_results (  ostream &  out, const Learner &  learner, const Vec &  results )  [inline]

Definition at line 569 of file Learner.h. References endl(), PLearn::TVec< T >::size(), and PLearn::Learner::testResultsNames().

string PLearn::prgname (  const string &  setname = ""  )

Definition at line 149 of file general.cc. Referenced by PLearn::HelpCommand::helpCommands(), PLearn::HelpCommand::helpOverview(), PLearn::HelpCommand::helpScripts(), and plearn_main().

void print (  ostream &  out, const map< int, real > &  vec, Set  V )  [inline]

Definition at line 609 of file ProbabilitySparseMatrix.h. References PLearn::Set::begin(), PLearn::Set::end(), endl(), NUMWIDTH, and SetIterator.

void print (  ostream &  out, const map< int, real > &  vec )  [inline]

Definition at line 600 of file ProbabilitySparseMatrix.h. References endl(), and NUMWIDTH.

void print (  ostream &  out, const map< int, real > &  vec, int  size )  [inline]

Definition at line 587 of file ProbabilitySparseMatrix.h. References endl(), and NUMWIDTH.

void print (  ostream &  out, RowMapSparseMatrix< real > &  m )  [inline]

Definition at line 575 of file ProbabilitySparseMatrix.h. References endl(), PLearn::RowMapSparseMatrix< T >::length(), NUMWIDTH, and PLearn::RowMapSparseMatrix< T >::width().

void print (  ostream &  out, ProbabilitySparseMatrix &  pyx, Set  Y, Set  X )  [inline]

Definition at line 561 of file ProbabilitySparseMatrix.h. References PLearn::Set::begin(), PLearn::Set::end(), endl(), NUMWIDTH, SetIterator, and x. Referenced by PLearn::TVec< pair< real, real > >::debugPrint(), PLearn::TMat< pair< real, real > >::debugPrint(), main(), PLearn::TVec< pair< real, real > >::println(), and PLearn::RealMapping::write().

int PLearn::print_diff (  ostream &  out, VMat  m1, VMat  m2, double  tolerance = 1e-6 )

Prints where m1 and m2 differ by more than tolerance returns the number of such differences, or -1 if the sizes differ. Definition at line 65 of file vmatmain.cc. References endl(), is_missing(), PLearn::VMat::length(), and PLearn::VMat::width(). Referenced by vmatmain().

void PLearn::printDistanceStatistics (  VMat  vm, int  inputsize )

Definition at line 196 of file vmatmain.cc. References endl(), L2distance(), PLearn::VMat::length(), PLearn::TVec< T >::subVec(), PLearn::StatsCollector::update(), and PLearn::VMat::width(). Referenced by vmatmain().

void PLearn::printFieldName (  ostream &  o, const Row::iterator &  field )

outputs the given field name in a cell of apropriate size Definition at line 834 of file SimpleDB.cc. References PLearn::RowIterator::char_width(), PLearn::Row::iterator, and PLearn::RowIterator::name(). Referenced by printFieldNames().

void PLearn::printFieldNames (  ostream &  o, const Row &  row )

outputs all field names, separated by " | " Definition at line 842 of file SimpleDB.cc. References PLearn::Row::begin(), PLearn::Row::const_iterator, PLearn::Row::end(), endl(), and printFieldName().

void PLearn::printInfo (  VarArray  inputs, const Var &  output, bool  show_gradients = true )

Definition at line 1163 of file VarArray.cc. References PLearn::VarArray::fbprop(), PLearn::VarArray::fprop(), PLearn::VarArray::printInfo(), and PLearn::VarArray::setMark().

void PLearn::printInfo (  VarArray &  a  )

Definition at line 1161 of file VarArray.cc. References PLearn::VarArray::printInfo().

Mat PLearn::product (  Mat  m1, VMat  m2 )

computes M1.M2 Definition at line 883 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLearn::VMat::length(), PLERROR, productAcc(), rowmatrix(), PLearn::VMat::width(), and PLearn::TMat< T >::width().

Var product (  Var  v1, Var  v2 )  [inline]

general matrix product Definition at line 80 of file ProductVariable.h.

template<class T> TMat<T> product (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

return m1 x m2 Definition at line 5694 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), product(), and PLearn::TMat< T >::width().

template<class T> TVec<T> product (  const TMat< T > &  m, const TVec< T > &  v )  [inline]

products return m x v Definition at line 5684 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and product().

template<class T> T product (  const TMat< T > &  mat  )

Definition at line 3783 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void product (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 2867 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void product (  const TMat< T > &  mat, const TVec< T > &  x, TVec< T > &  y )

Definition at line 2846 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::width(), and x.

template<class T> void product (  const TVec< T > &  result, const TMat< T > &  m, const TVec< T > &  v )

result[i] = sum_j m[i,j] * v[j] Definition at line 2209 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> T product (  const TVec< T > &  vec  )

Definition at line 271 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> void product (  RowMapSparseMatrix< T > &  M, const Vec &  x, Vec &  y )

Definition at line 801 of file RowMapSparseMatrix.h. References PLearn::RowMapSparseMatrix< T >::product(), and x. Referenced by PLearn::TangentLearner::build_(), PLearn::RegularGridVMatrix::build_(), PLearn::GaussianContinuum::build_(), PLearn::EntropyContrast::compute_df_dx(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), diagonalizeSubspace(), eigenSparseNonSymmMat(), eigenSparseSymmMat(), PLearn::ProjectionErrorVariable::fprop(), PLearn::ProductVariable::fprop(), PLearn::NllSemisphericalGaussianVariable::fprop(), PLearn::GaussianContinuum::get_image_matrix(), PLearn::ProductRandomVariable::invertible(), linearRegression(), mahalanobis_distance(), PLearn::PLS::NIPALSEigenvector(), product(), PLearn::ProductVariable::rfprop(), PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost(), PLearn::GaussMix::setInput(), PLearn::TransposeProductVariable::symbolicBprop(), PLearn::ProductTransposeVariable::symbolicBprop(), PLearn::PLS::train(), PLearn::PCA::train(), PLearn::GaussianContinuum::train(), PLearn::EntropyContrast::train(), PLearn::GaussMix::updateFromConditionalSorting(), and weightedLinearRegression().

template<class T> void product2Acc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 2921 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductTransposeVariable::bbprop().

template<class T> void product2Transpose (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3136 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void product2TransposeAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3193 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductVariable::bbprop().

template<class T> void productAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 2894 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void productAcc (  const TVec< T > &  vec, const TMat< T > &  m, const TVec< T > &  v )

result[i] += sum_j m[i,j] * v[j] Definition at line 2229 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::TransposeProductVariable::bprop(), PLearn::ProductTransposeVariable::bprop(), PLearn::MatrixAffineTransformVariable::bprop(), PLearn::AffineTransformVariable::bprop(), PLearn::ProjectionErrorVariable::fprop(), product(), PLearn::ProductVariable::rfprop(), and PLearn::PCA::train().

Mat PLearn::productTranspose (  VMat  m1, VMat  m2 )

computes M1.M2' Definition at line 858 of file VMat_maths.cc. References dot(), PLearn::VMat::length(), PLERROR, and PLearn::VMat::width().

Var productTranspose (  Var &  m1, Var &  m2 )  [inline]

Definition at line 79 of file ProductTransposeVariable.h.

template<class T> TMat<T> productTranspose (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

return m1 x m2' Definition at line 5704 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and productTranspose().

template<class T> void productTranspose (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3079 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductRandomVariable::EMBprop(), PLearn::ProjectionErrorVariable::fprop(), PLearn::ProductTransposeVariable::fprop(), productTranspose(), PLearn::ProductTransposeVariable::rfprop(), PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost(), PLearn::TransposeProductVariable::symbolicBprop(), PLearn::ProductVariable::symbolicBprop(), PLearn::PLS::train(), PLearn::PCA::train(), and PLearn::GaussMix::updateFromConditionalSorting().

template<class T> void productTransposeAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3166 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::TransposeProductVariable::bprop(), PLearn::ProductVariable::bprop(), PLearn::MatrixAffineTransformVariable::bprop(), PLearn::MatrixAffineTransformFeedbackVariable::fprop(), and PLearn::ProductTransposeVariable::rfprop().

Var projection_error (  Var  f, Var  t, real  norm_penalization = 0, int  n = -1, bool  normalize_by_neighbor_distance = true, bool  use_subspace_distance = false, real  epsilon = 0, real  regularization = 0, bool  ordered_vectors = true )  [inline]

Definition at line 98 of file ProjectionErrorVariable.h. Referenced by PLearn::TangentLearner::build_(), and PLearn::NearestNeighborPredictionCost::run().

template<class T> void projectOnOrthogonalSubspace (  const TMat< T > &  mat, TMat< T >  orthonormal_subspace )

Definition at line 3673 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and projectOnOrthogonalSubspace().

template<class T> void projectOnOrthogonalSubspace (  const TVec< T > &  vec, const TMat< T > &  orthonormal_subspace )

project the Vec x on the linear subspace ORTHOGONAL to the subspace defined by the rows of the orthonormal_subspace matrix, which are ASSUMED to be ORTHORNORMAL. The method is based on substracting for each row v of the matrix the quantity v * x . v. Definition at line 2010 of file TMat_maths_impl.h. References dot(), PLearn::TMat< T >::length(), and multiplyAcc(). Referenced by GramSchmidtOrthogonalization(), and projectOnOrthogonalSubspace().

VarArray PLearn::propagationPath (  const VarArray &  outputs  )

returns the propagationpath going from all sources that influence the outputs to the outputs. The sources themselves are not included in the path. Definition at line 1054 of file VarArray.cc. References propagationPath(), PLearn::TVec< Var >::size(), PLearn::VarArray::sources(), and PLearn::VarArray::unmarkAncestors().

VarArray PLearn::propagationPath (  const VarArray &  inputs, const VarArray &  outputs )

The function that computes a propagation path *. returns the array of all the variables on which fprop is to be called sequentially to do a full fprop (for bprop it is the reverse order). NOTE THAT THE INPUTS ARE NOT IN THE RETURNED PATH ! so that a clearGradient() call on the array won't erase their gradients but fprop and bprop will still take into account their values. Definition at line 1018 of file VarArray.cc. References PLearn::VarArray::buildPath(), PLearn::VarArray::clearMark(), PLearn::VarArray::markPath(), PLearn::VarArray::setMark(), and PLearn::TVec< Var >::size(). Referenced by PLearn::ArgminOfVariable::ArgminOfVariable(), PLearn::UnfoldedSumOfVariable::build_(), PLearn::UnfoldedFuncVariable::build_(), PLearn::Optimizer::build_(), PLearn::MatrixElementsVariable::build_(), PLearn::Function::build_(), PLearn::AddCostToLearner::build_(), PLearn::Function::differentiate(), EM(), PLearn::Variable::fprop_from_all_sources(), nonInputParentsOfPath(), propagationPath(), propagationPathToParentsOfPath(), and PLearn::Optimizer::setToOptimize().

VarArray PLearn::propagationPathToParentsOfPath (  const VarArray &  inputs, const VarArray &  outputs )

from all sources to all direct non-inputs parents of the path inputs-->outputs Definition at line 1064 of file VarArray.cc. References nonInputParentsOfPath(), propagationPath(), and PLearn::TVec< Var >::size(). Referenced by PLearn::Optimizer::build_(), PLearn::Function::build_(), and PLearn::Optimizer::setToOptimize().

CostFunc quadratic_risk (  real  risk_aversion, CostFunc  profit_function )  [inline]

Definition at line 85 of file QuadraticUtilityCostFunction.h.

StatsIt quantiles_stats (  Vec  quantiles, int  n_data = 1000 )  [inline]

Definition at line 413 of file StatsIterator.h.

void PLearn::randomShuffleRows (  SDB &  sdb  )

Performs a random permutation of all the rows of the SDB (same algorithm as Mat::shuffle). Definition at line 954 of file SimpleDB.cc. References endl(), PLearn::SimpleDB< KeyType, QueryResult >::getInRow(), PLearn::SimpleDB< KeyType, QueryResult >::getSchema(), PLearn::SimpleDB< KeyType, QueryResult >::length(), PLearn::SimpleDB< KeyType, QueryResult >::setRow(), and uniform_sample().

void PLearn::randomSplit (  VMat  d, real  validation_fraction, real  test_fraction, VMat &  train, VMat &  valid, VMat &  test )

Splits the dataset d into 3 subsets (similar to above). Definition at line 131 of file Splitter.cc. References split().

Vec PLearn::randomSplit (  VMat  d, real  test_fraction, VMat &  train, VMat &  test )

Splits the dataset d into a train and a test subset randomly picking which samples should be in each subset. (test_fraction has the same meaning a sabove). Return the permuted indices (the last ntest of which are the test indices and the remainder are the train indices). Definition at line 104 of file Splitter.cc. References PLearn::VMat::length(), PLearn::VMat::rows(), shuffleElements(), and PLearn::TVec< T >::subVec().

template<class T> void read (  const string &  stringval, T &  x )  [inline]

Definition at line 1099 of file PStream.h. References x.

template<class T> void read (  istream &  in_, T &  o )  [inline]

Definition at line 1092 of file PStream.h.

void read (  istream &  in, RealRange &  range )  [inline]

Definition at line 118 of file RealMapping.h. References PLearn::RealRange::read(). Referenced by cross_valid(), getDatasetAliases(), PLearn::RowMapSparseMatrix< real >::load(), PLearn::NumToStringMapping::load(), PLearn::Variable::oldread(), PLearn::StatsCollector::oldread(), PLearn::PStream::operator>>(), PLearn::RowMapSparseMatrix< real >::read(), PLearn::PStream::read(), PLearn::NumToStringMapping::read(), readField(), train_and_test(), and PLearn::pl_fdstreambuf::underflow().

void read_bool (  istream &  in, bool *  ptr, int  n, bool  is_file_bigendian )

void read_compr_mode_and_size (  FILE *  in, unsigned char &  mode, int &  size )  [inline]

Definition at line 307 of file pl_io.cc. References binread(), and mode.

void read_compr_mode_and_size (  istream &  in, unsigned char &  mode, int &  size )  [inline]

Definition at line 104 of file pl_io.cc. References binread(), and mode. Referenced by binread_compressed().

void PLearn::read_compr_mode_and_size_ptr (  char *&  in, unsigned char &  mode, int &  size )  [inline]

DEPRECATED DO NOT USE! compressed vec to and from memory. Definition at line 463 of file pl_io.cc. References mode. Referenced by compressedTransposeProductAcc(), and uncompress_vec().

double read_double (  istream &  in, bool  is_file_bigendian = true )  [inline]

Definition at line 164 of file pl_io_deprecated.h. References read_double().

void PLearn::read_double (  istream &  in, double *  ptr, int  n, bool  is_file_bigendian )

Definition at line 429 of file pl_io_deprecated.cc. References reverse_double(). Referenced by read_double().

float read_float (  istream &  in, bool  is_file_bigendian = true )  [inline]

Definition at line 162 of file pl_io_deprecated.h. References read_float().

void PLearn::read_float (  istream &  in, float *  ptr, int  n, bool  is_file_bigendian )

Definition at line 416 of file pl_io_deprecated.cc. References reverse_float(). Referenced by read_float().

int read_int (  istream &  in, bool  is_file_bigendian = true )  [inline]

The following calls read a single value from the file, assuming it is in the specified representation (either little or big endian) If necessary the representation is translated to the endianness used on the current architecture. Definition at line 158 of file pl_io_deprecated.h. References read_int().

void PLearn::read_int (  istream &  in, int *  ptr, int  n, bool  is_file_bigendian )

Reads binary data from a file assuming it is in the specified representation (either little or big endian) If necessary the representation is translated to the endianness on the current architecture. Definition at line 390 of file pl_io_deprecated.cc. References reverse_int(). Referenced by read_int(), and read_uint().

signed char read_sbyte (  istream &  in  )  [inline]

Definition at line 170 of file pl_io_deprecated.h. Referenced by PLearn::VecCompressor::readCompressedVec().

short read_short (  istream &  in, bool  is_file_bigendian = true )  [inline]

Definition at line 160 of file pl_io_deprecated.h. References read_short().

void PLearn::read_short (  istream &  in, short *  ptr, int  n, bool  is_file_bigendian )

Definition at line 403 of file pl_io_deprecated.cc. References reverse_short(). Referenced by read_short(), and read_ushort().

unsigned char read_ubyte (  istream &  in  )  [inline]

Definition at line 176 of file pl_io_deprecated.h.

unsigned int read_uint (  istream &  in, bool  is_file_bigendian = true )  [inline]

Definition at line 166 of file pl_io_deprecated.h. References read_uint().

void read_uint (  istream &  in, unsigned int *  ptr, int  n, bool  is_file_bigendian )  [inline]

Definition at line 148 of file pl_io_deprecated.h. References read_int(). Referenced by read_uint().

unsigned short read_ushort (  istream &  in, bool  is_file_bigendian = true )  [inline]

Definition at line 168 of file pl_io_deprecated.h. References read_ushort().

void read_ushort (  istream &  in, unsigned short *  ptr, int  n, bool  is_file_bigendian )  [inline]

Definition at line 150 of file pl_io_deprecated.h. References read_short(). Referenced by read_ushort().

string PLearn::readAndMacroProcess (  istream &  in, map< string, string > &  variables )

Will return the text, macro processed, with each instance of ${varname} in the text that corresponds to a key in the given map replaced by its associated value. Also every $DEFINE{varname=... } in the text will add a new varname entry in the map. (The DEFINE macro will be discarded) Also every $INCLUDE{filepath} will be replaced in place by the text of the file it includes Definition at line 630 of file fileutils.cc. References c_str(), chdir(), endl(), extract_directory(), extract_filename(), getAfterSkipBlanksAndComments(), getcwd(), peekAfterSkipBlanksAndComments(), pl_isnumber(), PLERROR, readFileAndMacroProcess(), readWhileMatches(), removeblanks(), skipBlanksAndComments(), smartReadUntilNext(), toint(), tostring(), and val. Referenced by readFileAndMacroProcess().

template<class T> void readField (  istream &  in, const string &  fieldname, T &  x, T  default_value )

readField with a default value when the field is not found Definition at line 257 of file pl_io_deprecated.h. References read(), readFieldName(), and x.

template<class T> void readField (  istream &  in, const string &  fieldname, T &  x )

Definition at line 226 of file pl_io_deprecated.h. References read(), readFieldName(), and x. Referenced by PLearn::VMatrix::oldread(), PLearn::QuantilesStatsIterator::oldread(), PLearn::LiftStatsIterator::oldread(), PLearn::SharpeRatioStatsIterator::oldread(), PLearn::StderrStatsIterator::oldread(), PLearn::StddevStatsIterator::oldread(), PLearn::ExpMeanStatsIterator::oldread(), PLearn::MeanStatsIterator::oldread(), PLearn::StatsIterator::oldread(), PLearn::StatsCollector::oldread(), PLearn::Optimizer::oldread(), PLearn::Learner::oldread(), and PLearn::RowMapSparseMatrix< real >::read().

bool PLearn::readFieldName (  istream &  in, const string &  fieldname, bool  force = false )

consumes "fieldname: " if possible, and return true if it does however if force=true and fieldname is not found then call error. Definition at line 95 of file pl_io_deprecated.cc. References PLERROR, and PLearn::pl_streambuf::seekmark(). Referenced by binreadField(), binreadField_double(), PLearn::StatsCollector::oldread(), and readField().

string readFileAndMacroProcess (  const string &  filepath  )  [inline]

Definition at line 203 of file fileutils.h. References readFileAndMacroProcess().

string PLearn::readFileAndMacroProcess (  const string &  filepath, map< string, string > &  variables )

Same as readAndMacroProcess, but takes a filename instead of an istream The following variables are automatically set from the filepath: FILEPATH DIRPATH FILENAME FILEBASE FILEEXT. Ex: if the absolute path to filepath is /home/me/foo.plearn Then we'll get: FILEPATH = "/home/me/foo.plearn" DIRPATH = "/home/me" FILENAME = "foo.plearn" FILEBASE = "foo" FILEEXT = ".plearn" Definition at line 585 of file fileutils.cc. References abspath(), extract_directory(), extract_extension(), extract_filename(), PLERROR, readAndMacroProcess(), remove_extension(), and remove_trailing_slash(). Referenced by PLearn::VVMatrix::build_(), PLearn::VVMatrix::createPreproVMat(), getDataSet(), PLearn::VVMatrix::getDateOfVMat(), macroLoadObject(), readAndMacroProcess(), readFileAndMacroProcess(), PLearn::RunCommand::run(), and PLearn::ReadAndWriteCommand::run().

void PLearn::readFooter (  istream &  in, const string &  classname )

consumes "</ClassName>\n" Definition at line 81 of file pl_io_deprecated.cc. References PLERROR. Referenced by PLearn::VMatrix::oldread(), PLearn::QuantilesStatsIterator::oldread(), PLearn::LiftStatsIterator::oldread(), PLearn::MaxStatsIterator::oldread(), PLearn::MinStatsIterator::oldread(), PLearn::SharpeRatioStatsIterator::oldread(), PLearn::StderrStatsIterator::oldread(), PLearn::StddevStatsIterator::oldread(), PLearn::ExpMeanStatsIterator::oldread(), PLearn::MeanStatsIterator::oldread(), PLearn::StatsIterator::oldread(), PLearn::StatsCollector::oldread(), PLearn::Optimizer::oldread(), PLearn::Learner::oldread(), PLearn::RowMapSparseMatrix< real >::read(), and PLearn::RealMapping::read().

int PLearn::readHeader (  istream &  in, const string &  classname )

consumes "<ClassName:version>\n and returns version" Definition at line 61 of file pl_io_deprecated.cc. References header, PLERROR, and toint(). Referenced by PLearn::VMatrix::oldread(), PLearn::QuantilesStatsIterator::oldread(), PLearn::LiftStatsIterator::oldread(), PLearn::MaxStatsIterator::oldread(), PLearn::MinStatsIterator::oldread(), PLearn::SharpeRatioStatsIterator::oldread(), PLearn::StderrStatsIterator::oldread(), PLearn::StddevStatsIterator::oldread(), PLearn::ExpMeanStatsIterator::oldread(), PLearn::MeanStatsIterator::oldread(), PLearn::StatsIterator::oldread(), PLearn::StatsCollector::oldread(), PLearn::Optimizer::oldread(), PLearn::Learner::oldread(), PLearn::RowMapSparseMatrix< real >::read(), and PLearn::RealMapping::read().

template<class MapT> void readMap (  PStream &  in, MapT &  m )

Definition at line 700 of file PStream.h. References PLearn::PStream::get(), PLearn::PStream::peek(), PLERROR, PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and val. Referenced by operator>>().

void readNewline (  istream &  in  )  [inline]

Reads next character and issues an error if it's not a newline. Definition at line 200 of file pl_io_deprecated.h. References PLERROR. Referenced by PLearn::StatsCollector::oldread().

Object* readObject (  istream &  in_  )  [inline]

Definition at line 592 of file Object.h. References readObject().

Object * PLearn::readObject (  PStream &  in, unsigned int  id = UINT_MAX )

This function builds an object from its representation in the stream. It understands several representations: The <objectclass> ... type of representation as is typically produced by write() serialization methods and functions. This will call the object's read() method. The ObjectClass( optionname=optionvalue; ... ; optionname=optionvalue ) type of representation (typical form for human input), will result in appropriate calls of the object's setOption() followed by its build(). load( filepath ) will call loadObject Definition at line 367 of file Object.cc. References PLearn::PStream::_do_not_use_this_method_rawin_(), PLearn::PStream::copies_map_in, fname, PLearn::PStream::get(), PLearn::PStream::getline(), loadObject(), PLearn::Object::newread(), PLearn::PStream::peek(), PLearn::PStream::pl_rdbuf(), PLERROR, PLearn::Object::read(), removeblanks(), PLearn::pl_streambuf::seekmark(), and PLearn::PStream::skipBlanksAndCommentsAndSeparators(). Referenced by loadObject(), macroLoadObject(), newObject(), operator>>(), readObject(), PLearn::RunCommand::run(), and PLearn::ReadAndWriteCommand::run().

template<class SequenceType> void readSequence (  PStream &  in, SequenceType &  seq )

This reads into a sequence. For this to work with the current implementation, the SequenceType must have: typedefs defining (SequenceType::...) value_type, size_type, iterator a begin() method that returns a proper iterator, a size_type size() method returning the size of the current container a resize(size_type n) method that allows to change the size of the container (which should also work with resize(0) ) a push_back(const value_type& x) method that appends the element x at the end Definition at line 977 of file PStream.h. References BIG_ENDIAN_ORDER, binread_(), byte_order(), endianswap(), PLearn::PStream::eof(), PLearn::PStream::get(), PLearn::PStream::inmode, LITTLE_ENDIAN_ORDER, PLearn::PStream::peek(), PLERROR, PLearn::PStream::read(), PLearn::PStream::skipBlanks(), PLearn::PStream::skipBlanksAndComments(), PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and x. Referenced by operator>>(), and PLearn::TVec< pair< real, real > >::read().

template<class SetT> void readSet (  PStream &  in, SetT &  s )

Definition at line 1137 of file PStream.h. References PLearn::PStream::get(), PLearn::PStream::peek(), PLERROR, PLearn::PStream::skipBlanksAndCommentsAndSeparators(), and val. Referenced by operator>>().

void PLearn::readWhileMatches (  istream &  in, const string &  s )

Reads while the characters read exactly match those in s Will throw a PLERROR exception as soon as it doesn't match. Definition at line 405 of file fileutils.cc. References PLERROR. Referenced by readAndMacroProcess().

void PLearn::real2rgb (  real  colorval, real &  r, real &  g, real &  b )

Definition at line 67 of file GhostScript.cc. Referenced by PLearn::GhostScript::setcolor(), and PLearn::GhostScript::writeBitmapHexString24Bits().

VMat PLearn::rebalanceNClasses (  VMat  inputs, int  nclasses, const string &  filename )

Rebalance the input VMatrix so that each class has a probability 1/nclasses. Also, the return VMat class alternates between all classes cyclicly. The resulting VMat is a SelectRowsFileIndexVMatrix which has its IntVecFile load if filename already exist, or computed if not. Definition at line 1063 of file VMat_maths.cc. References binary_search(), PLearn::Array< T >::clear(), PLearn::TMat< T >::column(), PLearn::TVec< T >::data(), file_exists(), PLearn::VMat::lastColumn(), PLearn::TVec< T >::length(), PLERROR, PLearn::IntVecFile::put(), PLearn::TVec< T >::resize(), PLearn::VMat::rows(), sortRows(), PLearn::VMat::toMat(), and PLearn::TMat< T >::toVecCopy().

template<class ObjectType, class OptionType> void redeclareOption (  OptionList &  ol, const string &  optionname, OptionType ObjectType::*  member_ptr, OptionBase::flag_t  flags, const string &  description, const string &  defaultval = "" )  [inline]

Allows one to redeclare an option differently (e.g. in a subclass, after calling inherited::declareOptions). Parameters: ol  the list to which this option should be appended optionname  the name of this option member_ptr  &YourClass::your_field description  see the flags in OptionBase a description of the option defaultval  the default value for this option, as set by the default constructor Definition at line 129 of file Option.h. References PLERROR. Referenced by PLearn::UnconditionalDistribution::declareOptions(), PLearn::SpectralClustering::declareOptions(), PLearn::RemoveDuplicateVMatrix::declareOptions(), PLearn::LLE::declareOptions(), PLearn::KernelPCA::declareOptions(), PLearn::Isomap::declareOptions(), PLearn::GramVMatrix::declareOptions(), PLearn::BootstrapVMatrix::declareOptions(), and PLearn::AutoVMatrix::declareOptions().

VMat PLearn::reduceDataSetSize (  real  fraction, VMat  data )

Definition at line 156 of file databases.cc. References PLearn::VMat::length(), and PLearn::VMat::subMatRows().

VMat PLearn::reduceInputSize (  real  fraction, VMat  data )

Definition at line 148 of file databases.cc. References endl(), PLearn::VMat::subMatColumns(), and PLearn::VMat::width().

template<class T> void regularizeMatrix (  const TMat< T > &  mat, T  tolerance )

Applies a regularizer : diag(A) += (tolerance * trace(A)). Definition at line 3434 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and trace(). Referenced by PLearn::ReconstructionWeightsKernel::reconstruct(), and smartInitialization().

void PLearn::remapClassnums (  VMat &  data, real  remap_minval_to, real  remap_maxval_to )

remaps classnums to {0,1} or to {-1,+1} Definition at line 164 of file databases.cc. References PLearn::VMat::length(), and PLearn::VMat::width().

VMat remapLastColumn (  VMat  d, real  if_equals_value, real  then_value = 1.0, real  else_value = -1.0 )  [inline]

Definition at line 105 of file RemapLastColumnVMatrix.h.

VMat remapLastColumn (  VMat  d, Mat  mapping )  [inline]

Definition at line 102 of file RemapLastColumnVMatrix.h. Referenced by loadClassificationDataset().

vector< string > PLearn::remove (  const vector< string > &  v, string  element )

return vector with all instances of element removed Definition at line 517 of file stringutils.cc. Referenced by PLearn::TVec< pair< real, real > >::removeSorted(), and PLearn::Set::replace().

void PLearn::remove_comments (  string &  text, const string &  commentstart = "#" )

In a multiline text, removes everything starting at commentstart pattern until the end of line. Definition at line 484 of file stringutils.cc. Referenced by getModelAliases().

string PLearn::remove_extension (  const string &  filename  )

Return the filename withoug the extension (i.e. removing the last. Definition at line 178 of file stringutils.cc. Referenced by PLearn::MatlabInterface::eigs_r11(), getDataSet(), matlabR11eigs(), readFileAndMacroProcess(), and use().

template<class T> TVec<T> remove_missing (  const TVec< T > &  vec  )

Definition at line 1358 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), and PLearn::TVec< T >::resize(). Referenced by PLearn::SequentialModelSelector::paired_t_test(), and PLearn::SequentialModelSelector::train().

string PLearn::remove_trailing_slash (  const string &  path  )

removes any trailing '/' from the path Definition at line 246 of file stringutils.cc. References slash_char. Referenced by PLearn::VVMatrix::createPreproVMat(), PLearn::DiskVMatrix::DiskVMatrix(), force_mkdir(), PLearn::VVMatrix::getPrecomputedDataName(), PLearn::VVMatrix::isPrecomputedAndUpToDate(), locateDatasetAliasesDir(), lsdir_fullpath(), newFilename(), and readFileAndMacroProcess().

string PLearn::removeallblanks (  const string &  s  )

removes all blanks ' ','',' ','' Definition at line 225 of file stringutils.cc. Referenced by PLearn::StatSpec::parseStatname().

string PLearn::removeblanks (  const string &  s  )

removes starting and ending blanks ' ','',' ','' Definition at line 204 of file stringutils.cc. Referenced by PLearn::VVMatrix::build_(), PLearn::AsciiVMatrix::build_(), PLearn::VVMatrix::createPreproVMat(), PLearn::VVMatrix::extractSourceMatrix(), PLearn::VVMatrix::generateVMatIndex(), getDataSet(), getDatasetAliases(), getModelAliases(), PLearn::Object::getOption(), loadAscii(), loadAsciiSingleBinaryDescriptor(), makeExplicitPath(), PLearn::Object::newread(), pl_isnumber(), PLearn::VMatLanguage::preprocess(), PLearn::VVMatrix::processJoinSection(), PLearn::RealMapping::read(), readAndMacroProcess(), readObject(), PLearn::VMatrix::resolveFieldInfoLink(), PLearn::StringTable::StringTable(), and viewVMat().

template<class T> TMat< T > PLearn::removeColumn (  const TMat< T > &  m, int  colnum )

returns a new mat which is m with the given column removed if the column to remove is the first or the last one, then a submatrix (a view) of m will be returned (for efficiency) otherwise, it is a fresh copy with the column removed. Definition at line 566 of file TMat_impl.h. References hconcat(), PLearn::TMat< T >::subMatColumns(), and PLearn::TMat< T >::width().

void PLearn::removeDelimiters (  string &  s, string  delim, string  replace )

Definition at line 2889 of file WordNetOntology.cc. Referenced by PLearn::WordNetOntology::extractOntology(), and PLearn::WordNetOntology::getSynsetWords().

template<class T> TVec< T > PLearn::removeElement (  const TVec< T > &  v, int  elemnum )

if the element to remove is the first or the last one, then a submatrix (a view) of m will be returned (for efficiency) otherwise, it is a fresh copy with the element removed. Definition at line 238 of file TMat_impl.h. References concat(), PLearn::TVec< T >::length(), and PLearn::TVec< T >::subVec().

string PLearn::removenewline (  const string &  s  )

removes any trailing ' ' and/or '' Definition at line 238 of file stringutils.cc. Referenced by addprepostfix(), PLearn::AsciiVMatrix::build_(), and pgetline().

VMat removeRow (  VMat  d, int  rownum )  [inline]

Definition at line 105 of file RemoveRowsVMatrix.h. References Vec.

template<class T> TMat< T > PLearn::removeRow (  const TMat< T > &  m, int  rownum )

returns a new mat which is m with the given row removed if the row to remove is the first or the last one, then a submatrix (a view) of m will be returned (for efficiency) otherwise, it is a fresh copy with the row removed. Definition at line 554 of file TMat_impl.h. References PLearn::TMat< T >::length(), PLearn::TMat< T >::subMatRows(), and vconcat(). Referenced by PLearn::Learner::computeLeaveOneOutCosts().

VMat removeRows (  VMat  d, Vec  rownums )  [inline]

Definition at line 102 of file RemoveRowsVMatrix.h.

void PLearn::replaceChars (  string &  str, string  char_to_replace, string  replacing_char )

Definition at line 2907 of file WordNetOntology.cc.

Var reshape (  Var  v, int  newlength, int  newwidth )  [inline]

Definition at line 83 of file ReshapeVariable.h.

void reverse_double (  const double *  ptr, int  n )  [inline]

Definition at line 72 of file pl_io_deprecated.h. References endianswap(). Referenced by fread_double(), fwrite_double(), PLearn::SimpleDB< KeyType, QueryResult >::memoryToDisk(), read_double(), and write_double().

void reverse_float (  const float *  ptr, int  n )  [inline]

Definition at line 71 of file pl_io_deprecated.h. References endianswap(). Referenced by fread_float(), fwrite_float(), loadCorelDatamat(), PLearn::SimpleDB< KeyType, QueryResult >::memoryToDisk(), read_float(), and write_float().

void reverse_int (  const int *  ptr, int  n )  [inline]

Definition at line 70 of file pl_io_deprecated.h. References endianswap(). Referenced by fread_int(), fwrite_int(), PLearn::SimpleDB< KeyType, QueryResult >::memoryToDisk(), PLearn::FilesIntStream::read_current(), read_int(), and write_int().

void reverse_short (  const short *  ptr, int  n )  [inline]

Definition at line 74 of file pl_io_deprecated.h. References endianswap(). Referenced by PLearn::SimpleDB< KeyType, QueryResult >::memoryToDisk(), read_short(), and write_short().

void reverse_uint (  const unsigned int *  ptr, int  n )  [inline]

NOTE: these calls are deprecated, use directly endianswap from base/byte_order.h. Definition at line 69 of file pl_io_deprecated.h. References endianswap().

void reverse_ushort (  const unsigned short *  ptr, int  n )  [inline]

Definition at line 73 of file pl_io_deprecated.h. References endianswap(). Referenced by fread_short().

real PLearn::rgb2real (  real  r, real  g, real  b )

These are used to pack and unpack r,g,b triplets to and from a single real. As usual in postscript, r,g and b are values between 0 and 1 indicating quantity of light. Definition at line 59 of file GhostScript.cc. Referenced by color().

string PLearn::right (  const string &  s, size_t  width, char  padding = ' ' )

Definition at line 64 of file stringutils.cc. Referenced by PLearn::SoftSlopeVariable::bprop(), PLearn::SoftSlopeIntegralVariable::bprop(), PLearn::HardSlopeVariable::bprop(), d_hard_slope(), d_soft_slope(), PLearn::SoftSlopeVariable::fprop(), PLearn::SoftSlopeIntegralVariable::fprop(), PLearn::HardSlopeVariable::fprop(), hard_slope(), PLearn::HardSlopeVariable::HardSlopeVariable(), PLearn::PDateTime::info(), PLearn::PDate::info(), operator<(), operator<=(), operator>(), operator>=(), soft_slope(), soft_slope_integral(), soft_slope_limit(), PLearn::SoftSlopeIntegralVariable::SoftSlopeIntegralVariable(), PLearn::SoftSlopeVariable::SoftSlopeVariable(), split_on_first(), tabulated_soft_slope(), tabulated_soft_slope_integral(), and PLearn::UnaryHardSlopeVariable::UnaryHardSlopeVariable().

Var rightPseudoInverse (  Var  v  )  [inline]

Definition at line 76 of file RightPseudoInverseVariable.h.

template<class T> void rightPseudoInverse (  const TMat< T > &  m, TMat< T > &  inv )

Definition at line 4686 of file TMat_maths_impl.h. References inverse(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> TMat<T> rightPseudoInverse (  TMat< T > &  m  )

Definition at line 4676 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), and PLearn::TMat< T >::width(). Referenced by PLearn::RightPseudoInverseVariable::fprop(), and PLearn::ProductRandomVariable::invertible().

void PLearn::rm (  const string &  file  )

calls system rm command with string file as parameters Definition at line 364 of file fileutils.cc. Referenced by PLearn::VVMatrix::createPreproVMat(), PLearn::VVMatrix::generateFilterIndexFile(), PLearn::VVMatrix::generateVMatIndex(), PLearn::FilteredVMatrix::openIndex(), PLearn::RealMapping::operator==(), PLearn::VMatrix::saveStringMappings(), PLearn::VMatrix::setSFIFFilename(), PLearn::VMatrix::unlockMetaDataDir(), PLearn::PrecomputedVMatrix::usePrecomputed(), and vmatmain().

template<class T> void rowArgmax (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

Definition at line 4119 of file TMat_maths_impl.h. References argmax(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void rowArgmin (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

Definition at line 4130 of file TMat_maths_impl.h. References argmin(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> TMat<T> rowmatrix (  const TVec< T > &  v  )  [inline]

returns a view of this vector as a single row matrix Definition at line 734 of file TMat_impl.h. References PLearn::TVec< T >::length(), and PLearn::TVec< T >::toMat(). Referenced by product(), transposeProduct(), PLearn::GaussMix::updateFromConditionalSorting(), and PLearn::VVec::VVec().

template<class T> void rowMax (  const TMat< T > &  mat, const TVec< T > &  colvec )

Definition at line 4085 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), max(), and PLERROR.

template<class T> void rowMax (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

Definition at line 4074 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), max(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void rowMean (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

Definition at line 4036 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), mean(), PLERROR, and PLearn::TMat< T >::width(). Referenced by computeColumnsMeanAndStddev().

template<class T> void rowMin (  const TMat< T > &  mat, const TVec< T > &  colvec )

Definition at line 4108 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), min(), and PLERROR.

template<class T> void rowMin (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

Definition at line 4096 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), min(), PLERROR, and PLearn::TMat< T >::width().

Var rowSum (  Var  v  )  [inline]

Definition at line 73 of file RowSumVariable.h.

template<class T> void rowSum (  const TMat< T > &  mat, const TVec< T > &  colvec )

Definition at line 4025 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and sum().

template<class T> void rowSum (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

all the operations below result in a column vector and are obtained by iterating (e.g. summing) over the column index, e.g. yielding the sum of each row in the result. Definition at line 4013 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLERROR, sum(), and PLearn::TMat< T >::width(). Referenced by PLearn::KernelProjection::computeOutput(), PLearn::TimesColumnVariable::symbolicBprop(), PLearn::PlusColumnVariable::symbolicBprop(), PLearn::MinusColumnVariable::symbolicBprop(), and PLearn::DuplicateColumnVariable::symbolicBprop().

template<class T> void rowSumOfSquares (  const TMat< T > &  mat, const TMat< T > &  singlecolumn )

Definition at line 4058 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void rowVariance (  const TMat< T > &  mat, const TMat< T > &  singlecolumn, const TMat< T > &  rowmean )

Definition at line 4047 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLERROR, variance(), and PLearn::TMat< T >::width(). Referenced by computeColumnsMeanAndStddev().

real PLearn::safeexp (  real  a  )

Definition at line 84 of file pl_math.cc. References exp(). Referenced by PLearn::SoftmaxLossVariable::bprop(), PLearn::MatrixSoftmaxLossVariable::bprop(), PLearn::LogSoftmaxVariable::bprop(), PLearn::LogAddVariable::bprop(), PLearn::GraphicalBiText::compute_likelihood(), PLearn::MultiInstanceNNet::computeCostsFromOutputs(), PLearn::MixtureRandomVariable::EMBprop(), exp(), PLearn::SoftmaxLossVariable::fprop(), PLearn::MatrixSoftmaxVariable::fprop(), PLearn::MatrixSoftmaxLossVariable::fprop(), PLearn::ExpVariable::fprop(), PLearn::SoftmaxLossVariable::rfprop(), and softmax().

real PLearn::safeflog (  real  base, real  a )

Definition at line 106 of file pl_math.cc. References safeflog().

real PLearn::safeflog (  real  a  )

Definition at line 75 of file pl_math.cc. References log(), and PLERROR. Referenced by PLearn::GaussianProcessRegressor::BayesianCost(), PLearn::PowVariableVariable::bprop(), PLearn::PLogPVariable::bprop(), PLearn::MultiInstanceNNet::computeCostsFromOutputs(), PLearn::MultinomialRandomVariable::EMUpdate(), PLearn::MixtureRandomVariable::EMUpdate(), PLearn::DiagonalNormalRandomVariable::EMUpdate(), PLearn::NegLogProbCostFunction::evaluate(), PLearn::SemiSupervisedProbClassCostVariable::fprop(), PLearn::PLogPVariable::fprop(), PLearn::LogVariable::fprop(), safeflog(), safeflog2(), and PLearn::AdaBoost::train().

real PLearn::safeflog2 (  real  a  )

Definition at line 111 of file pl_math.cc. References LOG_2, and safeflog(). Referenced by PLearn::GraphicalBiText::computeKL().

template<class T> TVec<T> safelog (  const TVec< T > &  src  )  [inline]

Definition at line 912 of file TMat_maths_impl.h. References compute_safelog(), and PLearn::TVec< T >::length(). Referenced by compute_safelog().

void samePos (  ProbabilitySparseMatrix &  m1, ProbabilitySparseMatrix &  m2, string  m1name, string  m2name )  [inline]

Definition at line 448 of file ProbabilitySparseMatrix.h. References PLearn::Set::begin(), PLearn::Set::end(), PLearn::ProbabilitySparseMatrix::exists(), PLearn::ProbabilitySparseMatrix::getPyX(), PLERROR, SetIterator, x, and PLearn::ProbabilitySparseMatrix::Y.

Var PLearn::Sample (  ConditionalExpression  conditional_expression  )

Return a Var which depends functionally on the RHS instances and the value of other RandomVars which are non-random and influence the LHS. Definition at line 701 of file RandomVar.cc. References PLearn::ConditionalExpression::LHS, PLearn::ConditionalExpression::RHS, and PLearn::RVInstance::V. Referenced by PLearn::RandomVarVMatrix::RandomVarVMatrix(), and sample().

void PLearn::sample (  ConditionalExpression  conditional_expression, Mat &  samples )

Sample N instances from the given conditional expression, of the form (LHS|RHS) where LHS is a RandomVar and RHS is a RVInstanceArray, e.g. (X==x && Z==z && W==w). Put the N instances in the rows of the given Nxd matrix. THIS ALSO SHOWS HOW TO REPEATEDLY SAMPLE IN AN EFFICIENT MANNER (rather than call "Vec sample(ConditionalExpression)"). Definition at line 677 of file RandomVar.cc. References PLearn::VarArray::fprop(), PLearn::TMat< T >::length(), and Sample().

Vec PLearn::sample (  ConditionalExpression  conditional_expression  )

Sample an instance from the given conditional expression, of the form (LHS|RHS) where LHS is a RandomVar and RHS is a RVInstanceArray, e.g. (X==x && Z==z && W==w). Definition at line 664 of file RandomVar.cc. References Sample(). Referenced by PLearn::GraphicalBiText::GraphicalBiText(), PLearn::RGBImageVMatrix::sample(), PLearn::GaussMix::train(), and PLearn::VMatrixFromDistribution::VMatrixFromDistribution().

template<class T> void save (  const string &  filepath, const T &  x )  [inline]

If necessary, missing directories along the filepath will be created. Definition at line 1183 of file PStream.h. References force_mkdir_for_file(), PLERROR, and x. Referenced by PLearn::GaussianContinuum::compute_train_and_validation_costs(), cross_valid(), PLearn::GaussianContinuum::get_image_matrix(), PLearn::VMatrix::getRanges(), PLearn::VMatrix::getStats(), PLearn::LearnerCommand::LearnerCommand(), PLearn::GaussianContinuum::make_random_walk(), PLearn::Learner::measure(), PLearn::PTester::perform(), PLearn::VMat::precompute(), PLearn::Train::run(), PLearn::SequentialValidation::run(), PLearn::Grapher::run(), PLearn::GenerateDecisionPlot::run(), PLearn::Experiment::run(), PLearn::WordNetOntology::save(), PLearn::Object::save(), PLearn::RowMapSparseMatrix< real >::saveAscii(), PLearn::Learner::stop_if_wanted(), PLearn::LearnerCommand::test(), PLearn::NeuralNet::train(), PLearn::LearnerCommand::train(), PLearn::GaussianContinuum::train(), PLearn::AdaBoost::train(), and train_and_test().

template<class T> void PLearn::saveAscii (  const string &  filename, const TVec< T > &  vec )

first number in file is length Definition at line 464 of file MatIO.h. References PLearn::TVec< T >::begin(), PLearn::TVec< T >::end(), endl(), PLearn::TVec< T >::length(), and PLERROR.

template<class T> void PLearn::saveAscii (  const string &  filename, const TMat< T > &  mat )

Definition at line 431 of file MatIO.h. References saveAscii().

template<class T> void PLearn::saveAscii (  const string &  filename, const TMat< T > &  mat, const TVec< string > &  fieldnames )

Definition at line 437 of file MatIO.h. References endl(), k, PLearn::TMat< T >::length(), PLERROR, PLearn::TVec< T >::size(), space_to_underscore(), and PLearn::TMat< T >::width(). Referenced by PLearn::Grapher::plot_1D_regression(), PLearn::SequentialValidation::reportStats(), and saveAscii().

void PLearn::saveAsciiWithoutSize (  const string &  filename, const Mat &  mat )

Definition at line 855 of file MatIO.cc. References PLearn::TMat< T >::length(), PLERROR, and pretty_print_number().

void PLearn::saveAsciiWithoutSize (  const string &  filename, const Vec &  vec )

Definition at line 816 of file MatIO.cc. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and pretty_print_number(). Referenced by PLearn::SequentialModelSelector::test(), and PLearn::SequentialModelSelector::train().

void PLearn::saveGnuplot (  const string &  filename, const Mat &  mat )

Definition at line 597 of file MatIO.cc. References PLearn::TMat< T >::length(), and PLERROR.

void PLearn::saveGnuplot (  const string &  filename, const Vec &  vec )

Definition at line 584 of file MatIO.cc. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::Gnuplot::multiplot(), PLearn::Gnuplot::plot(), PLearn::Gnuplot::plot3d(), and plotVMats().

template<class T> void savePMat (  const string &  filename, const TMat< T > &  mat )

Definition at line 804 of file TMat_impl.h. References PLERROR.

void PLearn::savePMat (  const string &  filename, const TMat< double > &  mat )

Definition at line 361 of file MatIO.cc. References DATAFILE_HEADERLENGTH, header, PLearn::TMat< T >::length(), PLERROR, strlen(), and PLearn::TMat< T >::width().

void PLearn::savePMat (  const string &  filename, const TMat< float > &  mat )

Definition at line 329 of file MatIO.cc. References DATAFILE_HEADERLENGTH, header, PLearn::TMat< T >::length(), PLERROR, strlen(), and PLearn::TMat< T >::width(). Referenced by PLearn::TMat< pair< real, real > >::save().

template<class T> void savePVec (  const string &  filename, const TVec< T > &  vec )

Definition at line 133 of file TVec_impl.h. References PLERROR.

void PLearn::savePVec (  const string &  filename, const TVec< double > &  vec )

Definition at line 193 of file MatIO.cc. References PLearn::TVec< T >::data(), DATAFILE_HEADERLENGTH, header, PLearn::TVec< T >::length(), PLERROR, and strlen().

void PLearn::savePVec (  const string &  filename, const TVec< float > &  vec )

Old native PLearn binary format (.pmat). Definition at line 160 of file MatIO.cc. References PLearn::TVec< T >::data(), DATAFILE_HEADERLENGTH, header, PLearn::TVec< T >::length(), PLERROR, and strlen(). Referenced by PLearn::TVec< pair< real, real > >::save().

void PLearn::saveSNMat (  const string &  filename, const Mat &  mat )

Definition at line 899 of file MatIO.cc. References fwrite_float(), fwrite_int(), PLearn::TMat< T >::length(), and PLearn::TMat< T >::width().

void PLearn::saveSNVec (  const string &  filename, const Vec &  vec )

Definition at line 954 of file MatIO.cc. References PLearn::TVec< T >::data(), fwrite_float(), fwrite_int(), PLearn::TVec< T >::length(), and PLERROR.

void PLearn::saveStringInFile (  const string &  filepath, const string &  text )

Writes the raw string into the given file Intermediate directories in filepath are created if necessary. Definition at line 348 of file fileutils.cc. References force_mkdir_for_file(), and PLERROR. Referenced by PLearn::PTester::perform(), and PLearn::SequentialValidation::run().

int PLearn::search_replace (  string &  text, const string &  searchstr, const string &  replacestr )

replaces all occurences of searchstr in the text by replacestr returns the number of matches that got replaced Definition at line 366 of file stringutils.cc.

void PLearn::seed (   )

initializes the random number generator with the cpu time Definition at line 195 of file random.cc. References manual_seed(). Referenced by PLearn::RepeatSplitter::build_(), PLearn::EmpiricalDistribution::EmpiricalDistribution(), PLearn::AdaBoost::forget(), PLearn::VVMatrix::generateVMatIndex(), get_seed(), PLearn::TangentLearner::initializeParams(), PLearn::NNet::initializeParams(), PLearn::NeuralNet::initializeParams(), PLearn::NeighborhoodSmoothnessNNet::initializeParams(), PLearn::MultiInstanceNNet::initializeParams(), PLearn::GaussianContinuum::initializeParams(), PLearn::ConditionalDensityNet::initializeParams(), old_plearn_main(), plearn_main(), and PLearn::RepeatSplitter::RepeatSplitter().

template<class T> void select (  const TMat< T > &  source, const TVec< T > &  row_indices, const TVec< T > &  column_indices, TMat< T > &  destination )

template<class T, class I> void select (  const TMat< T > &  source, const TVec< I > &  row_indices, const TVec< I > &  column_indices, TMat< T > &  destination )

Definition at line 473 of file TMat_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by eigenSparseNonSymmMat(), eigenSparseSymmMat(), PLearn::TMat< pair< real, real > >::operator()(), and PLearn::GraphicalBiText::test_WSD().

template<class T> TVec<T> selectAndOrder (  const TMat< T > &  mat, int  pos, int  colnum = 0 )

Definition at line 3584 of file TMat_maths_impl.h. References PLearn::TMat< T >::column(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::swapRows(), PLearn::TMat< T >::width(), and x.

template<class T> T selectAndOrder (  const TVec< T > &  vec, int  pos )

find the element at position pos that would result from a sort and put all elements (not in order!) lower than v[pos] in v[i<pos]. Definition at line 1890 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, swap(), and x. Referenced by PLearn::LiftStatsCollector::finalize(), and PLearn::LiftStatsIterator::finish().

template<class T, class I> void PLearn::selectColumns (  const TMat< T > &  source, const TVec< I > &  column_indices, TMat< T > &  destination )

Definition at line 445 of file TMat_impl.h. References PLearn::TMat< T >::column(), PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::TMat< pair< real, real > >::columns().

template<class T, class I> void PLearn::selectElements (  const TVec< T > &  source, const TVec< I > &  indices, TVec< T > &  destination )

select the elements of the source as specified by the vector of indices (between 0 and source.length()-1) into the destination vector (which must have the same length() as the indices vector). Definition at line 161 of file TMat_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::TVec< pair< real, real > >::operator()().

template<class T, class I> void PLearn::selectRows (  const TMat< T > &  source, const TVec< I > &  row_indices, TMat< T > &  destination )

Definition at line 418 of file TMat_impl.h. References PLearn::TVec< T >::data(), PLearn::TMat< T >::length(), PLearn::TVec< T >::length(), and PLERROR. Referenced by PLearn::TMat< pair< real, real > >::rows().

void sgesdd_ (  char *  JOBZ, int *  M, int *  N, float *  A, int *  LDA, float *  S, float *  U, int *  LDU, float *  VT, int *  LDVT, float *  WORK, int *  LWORK, int *  IWORK, int *  INFO )

Referenced by lapack_Xgesdd_().

void sgesv_ (  int *  N, int *  NRHS, float *  A, int *  LDA, int *  IPIV, float *  B, int *  LDB, int *  INFO )

Referenced by lapackSolveLinearSystem().

void sgetrf_ (  int *  M, int *  N, float *  A, int *  LDA, int *  IPIV, int *  INFO )

Referenced by matInvert().

void sgetri_ (  int *  N, float *  A, int *  LDA, int *  IPIV, float *  WORK, int *  LWORK, int *  INFO )

Referenced by matInvert().

StatsIt sharpe_ratio_stats (   )  [inline]

exponential of the mean Definition at line 416 of file StatsIterator.h.

VMat PLearn::shuffle (  VMat  d  )

returns a SelectRowsVMatrix that has d's rows shuffled Definition at line 775 of file VMat_maths.cc. References PLearn::VMat::length(), PLearn::VMat::rows(), and shuffleElements(). Referenced by PLearn::BootstrapVMatrix::BootstrapVMatrix(), and PLearn::RepeatSplitter::RepeatSplitter().

template<class T> void shuffleElements (  const TVec< T > &  vec  )

randomly shuffle the entries of the TVector Definition at line 145 of file random.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and uniform_sample(). Referenced by bootstrap(), PLearn::TrainValidTestSplitter::build_(), PLearn::SelectInputSubsetLearner::build_(), PLearn::RepeatSplitter::build_(), PLearn::BootstrapVMatrix::build_(), PLearn::VVMatrix::generateVMatIndex(), randomSplit(), shuffle(), and split().

template<class T> void shuffleRows (  const TMat< T > &  mat  )

Definition at line 164 of file random.h. References PLearn::TMat< T >::length(), PLearn::TMat< T >::swapRows(), and uniform_sample(). Referenced by computeRanks(), input2dSet(), loadATT800(), loadBreastCancer(), loadBreastCancerWisconsin(), loadCorel(), loadDiabetes(), loadIonosphere(), loadLetters(), loadPimaIndians(), and loadSonar().

Var sigmoid (  Var  v  )  [inline]

Definition at line 73 of file SigmoidVariable.h.

template<class T> TVec<T> sigmoid (  const TVec< T > &  src  )  [inline]

Definition at line 995 of file TMat_maths_impl.h. References compute_sigmoid(), and PLearn::TVec< T >::length().

real sigmoid (  real  x  )  [inline]

numerically stable version of sigmoid(x) = 1.0/(1.0+exp(-x)) Definition at line 270 of file pl_math.h. References tanh(), and x. Referenced by PLearn::SoftSlopeVariable::bprop(), PLearn::SoftplusVariable::bprop(), PLearn::NegCrossEntropySigmoidVariable::bprop(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::ConditionalDensityNet::build_(), compute_sigmoid(), d_soft_slope(), PLearn::SigmoidalKernel::evaluate(), PLearn::LiftBinaryCostFunction::evaluate(), PLearn::SigmoidVariable::fprop(), PLearn::NegCrossEntropySigmoidVariable::fprop(), softmax(), PLearn::SoftplusVariable::symbolicBprop(), and PLearn::ConditionalDensityNet::train().

Var sign (  Var  input  )  [inline]

Definition at line 72 of file SignVariable.h.

real sign (  real  a  )  [inline]

Definition at line 92 of file pl_math.h. Referenced by PLearn::SignVariable::fprop().

void PLearn::skipBlanksAndComments (  istream &  in  )

will skip all blanks (white space, newline and #-style comments) Next character read will be first "non-blank" Definition at line 434 of file fileutils.cc. References skipRestOfLine(). Referenced by PLearn::MultiInstanceVMatrix::build_(), getAfterSkipBlanksAndComments(), loadAscii(), loadAsciiSingleBinaryDescriptor(), peekAfterSkipBlanksAndComments(), and readAndMacroProcess().

void PLearn::skipRestOfLine (  istream &  in  )

skips everything until ' ' (also consumes the ' ') Definition at line 427 of file fileutils.cc. Referenced by PLearn::VMatLanguage::preprocess(), and skipBlanksAndComments().

real small_dilogarithm (  real  x  )

Definition at line 150 of file pl_math.cc. References PLWARNING, and x. Referenced by positive_dilogarithm().

Mat PLearn::smartInitialization (  VMat  v, int  n, real  c, real  regularization )

Definition at line 79 of file GaussianContinuum.cc. References PLearn::TMat< T >::clear(), PLearn::VMat::length(), regularizeMatrix(), solveLinearSystem(), uniform_multinomial_sample(), and PLearn::VMat::width(). Referenced by PLearn::TangentLearner::initializeParams().

int PLearn::smartReadUntilNext (  istream &  in, string  stoppingsymbols, string &  characters_read, bool  ignore_brackets = false )

same as PStream's method smartReadUntilNext, but for istream Definition at line 491 of file fileutils.cc. References PLearn::PStream::smartReadUntilNext(). Referenced by getModelAliases(), readAndMacroProcess(), and PLearn::PStream::smartReadUntilNext().

template<class T> TMat<T> smooth (  TMat< T >  data, int  windowsize )

Definition at line 5494 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), and PLearn::TMat< T >::width().

Mat PLearn::smoothCorelHisto (  Mat &  data  )

Definition at line 595 of file databases.cc. References k, and PLearn::TMat< T >::length(). Referenced by loadCorel().

int snaupd_ (  long int *  , const char *  , long int *  , const char *  , long int *  , float *  , float *  , long int *  , float *  , long int *  , long int *  , long int *  , float *  , float *  , long int *  , long int *  , short  , short  )

Referenced by eigenSparseNonSymmMat().

int sneupd_ (  long int *  , const char *  , long int *  , float *  , float *  , float *  , long int *  , float *  , float *  , float *  , const char *  , long int *  , const char *  , long int *  , float *  , float *  , long int *  , float *  , long int *  , long int *  , long int *  , float *  , float *  , long int *  , long int *  , short  , short  , short  )

Referenced by eigenSparseNonSymmMat().

Var soft_slope (  Var  x, Var  smoothness, Var  left, Var  right )  [inline]

Definition at line 81 of file SoftSlopeVariable.h. References left(), right(), and x.

real soft_slope (  real  x, real  smoothness = 1, real  left = 0, real  right = 1 )  [inline]

Definition at line 349 of file pl_math.h. References hard_slope(), left(), right(), softplus(), and x. Referenced by PLearn::SoftSlopeVariable::bprop(), PLearn::ConditionalDensityNet::build_(), PLearn::SoftSlopeVariable::fprop(), and PLearn::ConditionalDensityNet::train().

Var soft_slope_integral (  Var  smoothness, Var  left, Var  right, real  a = 0, real  b = 1 )  [inline]

Definition at line 80 of file SoftSlopeIntegralVariable.h. References left(), and right().

real PLearn::soft_slope_integral (  real  smoothness = 1, real  left = 0, real  right = 1, real  a = 0, real  b = 1 )

Definition at line 241 of file pl_math.cc. References hard_slope_integral(), left(), right(), and softplus_primitive(). Referenced by PLearn::ConditionalDensityNet::build_(), and PLearn::SoftSlopeIntegralVariable::fprop().

Var soft_slope_limit (  Var  x, Var  smoothness, Var  left, Var  right )  [inline]

Definition at line 91 of file SoftSlopeVariable.h. References left(), PLearn::Var::length(), PLERROR, right(), PLearn::Var::width(), and x. Referenced by PLearn::ConditionalDensityNet::build_().

Var PLearn::softmax (  Var  input, int  index )

< should be numerically more stable Definition at line 66 of file Var_utils.cc. References exp(), and sum().

Var softmax (  Var  v  )  [inline]

Definition at line 75 of file SoftmaxVariable.h.

Var softmax (  Var  input, Var  index )  [inline]

Definition at line 79 of file SoftmaxLossVariable.h.

Var PLearn::softmax (  Var  x1, Var  x2, Var  hardness )

a soft version of the ordinary max(x1,x2) mathematical operation where the hardness parameter controls how close to an actual max(x1,x2) we are (i.e. as hardness --> infty we quickly get max(x1,x2), but as hardness --> 0 we get the simple average of x1 and x2. Definition at line 133 of file SigmoidVariable.cc. References sigmoid().

template<class T> TVec<T> softmax (  const TVec< T > &  x  )

Definition at line 1613 of file TMat_maths_impl.h. References softmax(), and x.

template<class T> void softmax (  const TVec< T > &  x, const TVec< T > &  y )

y = softmax(x) Definition at line 76 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), max(), PLERROR, safeexp(), and x. Referenced by PLearn::LogSumVariable::bprop(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MixtureRandomVariable::ElogP(), PLearn::SoftmaxVariable::fprop(), PLearn::MultinomialRandomVariable::logP(), PLearn::MixtureRandomVariable::logP(), PLearn::MultinomialRandomVariable::setValueFromParentsValue(), PLearn::MixtureRandomVariable::setValueFromParentsValue(), softmax(), and PLearn::LogSumVariable::symbolicBprop().

Var softplus (  Var  v  )  [inline]

Definition at line 72 of file SoftplusVariable.h.

real softplus (  real  x  )  [inline]

numerically stable computation of log(1+exp(x)) return 0.5*x + LOG_2 - log(1./cosh(0.5*x)); Definition at line 293 of file pl_math.h. References exp(), and x. Referenced by PLearn::SoftSlopeIntegralVariable::bprop(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::GaussianContinuum::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::SoftplusVariable::fprop(), soft_slope(), and tabulated_softplus().

Var softplus_primitive (  Var  v  )  [inline]

Definition at line 78 of file DilogarithmVariable.h. References dilogarithm(), and exp().

real softplus_primitive (  real  x  )  [inline]

Definition at line 409 of file pl_math.h. References dilogarithm(), exp(), and x. Referenced by soft_slope_integral(), and tabulated_softplus_primitive().

Vec PLearn::solveLinearSystem (  const Mat &  A, const Vec &  b )

Returns solution x of Ax = b (same as above, except b and x are vectors). Definition at line 350 of file plapack.cc. References PLearn::TVec< T >::length(), solveLinearSystem(), and PLearn::TVec< T >::toMat().

Mat PLearn::solveLinearSystem (  const Mat &  A, const Mat &  B )

Returns the solution X of AX = B A and B are left intact, and the solution is returned. This call does memory allocations/deallocations and transposed copies of matrices (contrary to the lower level lapackSolveLinearSystem call that you may consider using if efficiency is a concern). Definition at line 337 of file plapack.cc. References lapackSolveLinearSystem(), PLearn::TMat< T >::length(), PLERROR, and transpose().

void PLearn::solveLinearSystem (  const Mat &  A, const Mat &  Y, Mat &  X )

for matrices A such that A.length() <= A.width(), find X s.t. A X = Y Definition at line 325 of file plapack.cc. References PLERROR. Referenced by constrainedLinearRegression(), PLearn::ProductRandomVariable::EMBprop(), linearRegression(), PLearn::ReconstructionWeightsKernel::reconstruct(), smartInitialization(), solveLinearSystem(), and weightedLinearRegression().

template<class T> void solveLinearSystemByCholesky (  const TMat< T > &  A, const TMat< T > &  B, TMat< T > &  X, TMat< T > *  pL = 0, TVec< T > *  py = 0 )

Definition at line 4732 of file TMat_maths_impl.h. References choleskyDecomposition(), choleskySolve(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductRandomVariable::EMUpdate(), linearRegression(), linearRegressionNoBias(), and weightedLinearRegression().

void PLearn::solveTransposeLinearSystem (  const Mat &  A, const Mat &  Y, Mat &  X )

for matrices A such that A.length() >= A.width(), find X s.t. X A = Y Definition at line 332 of file plapack.cc. References PLERROR. Referenced by PLearn::ProductRandomVariable::EMBprop().

template<class T> void solveTransposeLinearSystemByCholesky (  const TMat< T > &  A, const TMat< T > &  B, TMat< T > &  X, TMat< T > *  pL = 0, TVec< T > *  py = 0 )

Definition at line 4760 of file TMat_maths_impl.h. References choleskyDecomposition(), choleskySolve(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductRandomVariable::EMUpdate().

template<class T> void sortColumns (  const TMat< T > &  mat, int  rownum )

Definition at line 239 of file TMat_sort.h. References PLearn::TMat< T >::length(), min(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void sortElements (  const TVec< T > &  vec  )  [inline]

Sorts the elements of vec in place. Definition at line 113 of file TMat_sort.h. References PLearn::TVec< T >::begin(), and PLearn::TVec< T >::end(). Referenced by bootstrap(), PLearn::TrainValidTestSplitter::build_(), PLearn::RemoveRowsVMatrix::build_(), PLearn::BootstrapVMatrix::build_(), displayHistogram(), PLearn::QuantilesStatsIterator::finish(), max_cdf_diff(), PLearn::Gnuplot::plotcdf(), and PLearn::Gnuplot::plotdensity().

int sortIdComparator (  const void *  i1, const void *  i2 )

Definition at line 83 of file StatsCollector.cc. References PairRealSCCType. Referenced by PLearn::StatsCollector::sortIds().

template<class T> void sortRows (  const TMat< T > &  mat, int  col = 0, bool  increasing_order = true )

This implementation should be very efficient, but it does two memory allocation: a first one of mat.length()*(sizeof(real)+sizeof(int)) and a second one of mat.length()*sizeof(int). (Note: due to the implementation of the column sorting, this function always performs a STABLE SORT (in the sense of the STL stable_sort function). There is no need to explicitly call stable_sort to achieve the effect, iff increasing_order=true) Definition at line 194 of file TMat_sort.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::swapRows().

template<class T> void sortRows (  TMat< T > &  mat, const TVec< int > &  key_columns, bool  increasing_order = true )

Definition at line 104 of file TMat_sort.h. References PLearn::TMat< T >::rows_as_arrays_begin(), and PLearn::TMat< T >::rows_as_arrays_end(). Referenced by PLearn::SortRowsVMatrix::build_(), PLearn::Kernel::computeNeighbourMatrixFromDistanceMatrix(), computeRanks(), PLearn::ReconstructionWeightsKernel::computeWeights(), findClosestPairsOfDifferentClass(), newIndexedMatArray(), PLearn::Grapher::plot_1D_regression(), PLearn::Gnuplot::plotClasses(), rebalanceNClasses(), PLearn::TestDependenciesCommand::run(), and PLearn::SortRowsVMatrix::sortRows().

string PLearn::space_to_underscore (  string  str  )

replaces all characters <= ' ' (i.e. newline, tab, space, etc...) by '_' Definition at line 335 of file stringutils.cc. Referenced by PLearn::AsciiVMatrix::AsciiVMatrix(), PLearn::Learner::costNames(), PLearn::SDBVMFieldRemapStrings::getDiscreteValue(), PLearn::VMatrix::saveAMAT(), saveAscii(), PLearn::Learner::testResultsNames(), PLearn::RowIterator::toString(), and PLearn::FieldValue::toString().

void PLearn::SpearmanRankCorrelation (  const VMat &  x, const VMat &  y, Mat &  r )

Compute the Spearman Rank correlation statistic. It measures how much of a monotonic dependency there is between two variables x and y (column matrices). The statistic is computed as follows: r = 1 - 6 (sum_{i=1}^n (rx_i - ry_i)^2) / (n(n^2-1)) If x and y are column matrices than r is a 1x1 matrix. If x and y have width wx and wy respectively than the statistic is computed for each pair of column (the first taken from x and the second from y) and r will be a symmetric matrix size wx by wy upon return. N.B. If x holds in memory than copying to a matrix before calling this function will speed up computation significantly. Definition at line 62 of file stats_utils.cc. References PLearn::TMat< T >::clear(), computeRanks(), endl(), k, PLearn::VMat::length(), PLERROR, PLWARNING, PLearn::TMat< T >::resize(), PLearn::VMat::toMat(), PLearn::VMat::width(), and x. Referenced by testSpearmanRankCorrelation().

void PLearn::split (  VMat  d, real  validation_fraction, real  test_fraction, VMat &  train, VMat &  valid, VMat &  test, bool  do_shuffle = false )

Splits the dataset d into 3 subsets. Definition at line 115 of file Splitter.cc. References endl(), PLearn::VMat::length(), PLearn::VMat::rows(), shuffleElements(), and PLearn::TVec< T >::subVec().

void PLearn::split (  VMat  d, real  test_fraction, VMat &  train, VMat &  test, int  i = 0, bool  use_all = false )

Splits the dataset d into a train and a test subset If test_fraction is <1.0 then the size of the test subset is set to be ntest = int(test_fraction*d.length()) If test_fraction is >=1.0 then ntest = int(test_fraction) Last argument i allows to get the ith split of a K-fold cross validation i = 0 corresponds to having the last ntest samples used as the test set i = 1 means having the ntest samples before those as the test set, etc... If the bool 'use_all' is true, then the test set may contain more examples so that if we ask all splits, all the examples are contained in one test split. Definition at line 63 of file Splitter.cc. References PLearn::VMat::length(), PLearn::VMat::subMatRows(), and vconcat().

vector< string > PLearn::split (  const string &  s, const string &  delimiters = " \t\n\r", bool  keepdelimiters = false )

splits a string into a list of substrings (using any sequence of the given delimiters as split point) if keepdelimiters is true the delimitersequences are appended to the list otherwise (the default) they are removed. Definition at line 399 of file stringutils.cc.

vector< string > PLearn::split (  const string &  s, char  delimiter )

splits a string along occurences of the delimiters. Definition at line 380 of file stringutils.cc. Referenced by PLearn::SDBVMatrix::appendField(), PLearn::MultiInstanceVMatrix::build_(), PLearn::GraphicalBiText::build_(), PLearn::AsciiVMatrix::build_(), PLearn::VVMatrix::createPreproVMat(), cross_valid(), PLearn::VVMatrix::extractSourceMatrix(), PLearn::VMatLanguage::generateCode(), PLearn::VVMatrix::generateVMatIndex(), getDataSet(), PLearn::VVMatrix::getDateOfVMat(), PLearn::Dictionary::getId(), PLearn::EntropyContrast::getInfo(), getList(), getNonBlankLines(), PLearn::KFoldSplitter::getSplit(), PLearn::ShellProgressBar::getTime(), interactiveDisplayCDF(), PLearn::WordNetOntology::load(), loadAscii(), loadAsciiSingleBinaryDescriptor(), PLearn::GraphicalBiText::loadBitext(), loadClassificationDataset(), PLearn::VMatrix::loadFieldInfos(), PLearn::GraphicalBiText::loadSensemap(), main(), PLearn::GhostScript::multilineShow(), PLearn::Object::newwrite(), parseSizeFromRemainingLines(), PLearn::StatSpec::parseStatname(), plotVMats(), PLearn::VMatLanguage::preprocess(), PLearn::VVMatrix::processJoinSection(), randomSplit(), and PLearn::StringTable::StringTable().

pair< string, string > PLearn::split_on_first (  const string &  s, const string &  delimiters = " \t\n\r" )

Split the string on the first occurence of a delimiter; return a pair with the two split parts. If the splitting character is not found, the original string is returned in the first part of the pair, and "" is in the second part Definition at line 475 of file stringutils.cc. References left(), right(), and split_on_first().

void PLearn::split_on_first (  const string &  s, const string &  delimiters, string &  left, string &  right )

Split the string on the first occurence of a delimiter and returns what was left of the delimitor and what was right of it. If no delimitor character is found, the original string is returned as left, and "" is returned in right Definition at line 459 of file stringutils.cc. References left(), and right(). Referenced by PLearn::StatSpec::parseStatname(), PLearn::RunCommand::run(), and split_on_first().

void PLearn::splitTrainValidTest (  VMat &  data_set, VMat &  train_set, VMat &  valid_set, real  valid_fraction, VMat &  test_set, real  test_fraction, bool  normalize = true )

Definition at line 130 of file databases.cc. References PLearn::VMat::length(), normalize(), normalizeDataSets(), PLearn::VMat::subMatColumns(), PLearn::VMat::subMatRows(), and PLearn::VMat::width().

Var sqrt (  Var  v  )  [inline]

Definition at line 80 of file PowVariable.h. References pow().

template<class T> TMat<T> sqrt (  const TMat< T > &  m  )

Definition at line 5544 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), sqrt(), and PLearn::TMat< T >::width().

template<class T> TVec<T> sqrt (  const TVec< T > &  src  )  [inline]

Definition at line 894 of file TMat_maths_impl.h. References compute_sqrt(), and PLearn::TVec< T >::length(). Referenced by affineNormalization(), autocorrelation_function(), bnldev(), PLearn::SquareRootVariable::bprop(), PLearn::ErfVariable::bprop(), choleskyDecomposition(), compute_sqrt(), computeBasicStats(), computeColumnsMeanAndStddev(), computeConditionalMeans(), computeMeanAndStddev(), PLearn::GaussMix::computeMeansAndCovariances(), PLearn::PCA::computeOutput(), PLearn::KernelProjection::computeOutput(), PLearn::GaussianContinuum::computeOutput(), correlation(), correlations(), dist(), PLearn::GhostScript::drawCross(), PLearn::DivisiveNormalizationKernel::evaluate(), PLearn::DivisiveNormalizationKernel::evaluate_i_j(), PLearn::DistanceKernel::evaluate_i_j(), PLearn::DivisiveNormalizationKernel::evaluate_i_x(), PLearn::DivisiveNormalizationKernel::evaluate_i_x_again(), PLearn::DivisiveNormalizationKernel::evaluate_x_i(), PLearn::DivisiveNormalizationKernel::evaluate_x_i_again(), PLearn::FieldStat::finalize(), PLearn::SharpeRatioStatsIterator::finish(), PLearn::StderrStatsIterator::finish(), PLearn::StddevStatsIterator::finish(), PLearn::SquareRootVariable::fprop(), gamdev(), gaussian_01(), PLearn::GaussianDistribution::generate(), PLearn::GaussMix::generateFromGaussian(), PLearn::JoinVMatrix::getNewRow(), PLearn::TangentLearner::initializeParams(), PLearn::NNet::initializeParams(), PLearn::GaussianContinuum::initializeParams(), KS_test(), PLearn::GaussianContinuum::make_random_walk(), PLearn::ConjGradientOptimizer::minCubic(), multivariate_normal(), norm(), PLearn::ScaledGradientOptimizer::optimize(), p_value(), paired_t_test(), PLearn::SequentialModelSelector::paired_t_test(), poidev(), PLearn::PCA::reconstruct(), PLearn::VMatLanguage::run(), PLearn::GaussianProcessRegressor::setInput_const(), PLearn::DiagonalNormalRandomVariable::setValueFromParentsValue(), sqrt(), squareroot(), PLearn::VMFieldStat::stddev(), PLearn::StatsCollector::stddev(), PLearn::StatsCollector::stderror(), PLearn::ErfVariable::symbolicBprop(), testNoCorrelationAsymptotically(), PLearn::GaussianProcessRegressor::train(), PLearn::EntropyContrast::train(), PLearn::AdaBoost::train(), and weighted_distance().

Var square (  Var  v  )  [inline]

Definition at line 74 of file SquareVariable.h.

template<class T> TMat<T> square (  const TMat< T > &  m  )

Definition at line 5534 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), square(), and PLearn::TMat< T >::width().

template<class T> TVec<T> square (  const TVec< T > &  vec  )

Definition at line 1334 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> T square (  const T &  x  )  [inline]

Definition at line 125 of file pl_math.h. References x. Referenced by autocorrelation_function(), PLearn::TanhVariable::bprop(), PLearn::GaussianKernel::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::EntropyContrast::compute_df_dx(), computeBasicStats(), computeConditionalMeans(), PLearn::MovingAverage::computeCostsFromOutputs(), PLearn::GaussMix::computeLogLikelihood(), PLearn::PDistribution::computeOutput(), PLearn::SquaredErrorCostFunction::evaluate(), PLearn::ScaledGaussianKernel::evaluate(), PLearn::GeodesicDistanceKernel::evaluate(), PLearn::GeodesicDistanceKernel::evaluate_i_j(), PLearn::GeodesicDistanceKernel::evaluate_i_x_again(), PLearn::GeodesicDistanceKernel::evaluate_i_x_from_distances(), PLearn::FieldStat::finalize(), PLearn::OneHotSquaredLoss::fprop(), PLearn::MatrixOneHotSquaredLoss::fprop(), logOfCompactGaussian(), PLearn::DiagonalNormalRandomVariable::logP(), norm(), pownorm(), PLearn::TanhVariable::rfprop(), PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost(), square(), squareElements(), sumsquare(), PLearn::TanhVariable::symbolicBprop(), PLearn::ErfVariable::symbolicBprop(), PLearn::DivVariable::symbolicBprop(), PLearn::EntropyContrast::train(), PLearn::ConditionalStatsCollector::update(), PLearn::EntropyContrast::update_mu_sigma_f(), PLearn::GaussMix::updateFromConditionalSorting(), PLearn::VMFieldStat::variance(), and PLearn::StatsCollector::variance().

real PLearn::square_f (  real  x  )

Definition at line 131 of file pl_math.cc. References x. Referenced by PLearn::DiagonalNormalRandomVariable::EMBprop().

template<class T> void squareAcc (  const TVec< T > &  vec, const TVec< T > &  x )

TVec[i] += x[i]*x[i];. Definition at line 2141 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::ScaledGradientOptimizer::optimize().

CostFunc squared_classification_error (  real  hot_value = 0.8, real  cold_value = 0.2 )  [inline]

Definition at line 88 of file SquaredErrorCostFunction.h.

CostFunc PLearn::squared_error (  int  singleoutputindex = -1  )  [inline]

Definition at line 92 of file SquaredErrorCostFunction.cc. Referenced by PLearn::LinearRegressor::train().

template<class T> void squareElements (  const TMat< T > &  m  )

squares the elements of m in place Definition at line 156 of file TMat_maths_impl.h. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::compact_begin(), PLearn::TMat< T >::compact_end(), PLearn::TMat< T >::end(), PLearn::TMat< T >::isCompact(), PLearn::TMat< T >::size(), and square().

template<class T> void squareElements (  const TVec< T > &  x  )

squares the elements of x in place Definition at line 143 of file TMat_maths_impl.h. References x.

template<class T> void squareMultiplyAcc (  const TMat< T > &  mat, const TMat< T > &  x, T  scale )

Definition at line 3548 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLERROR, PLearn::TMat< T >::width(), and x.

template<class T> void squareMultiplyAcc (  const TVec< T > &  vec, const TVec< T > &  x, T  scale )

TVec[i] += x[i]*x[i]*scale;. Definition at line 2125 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::DiagonalNormalRandomVariable::EMBprop(), and PLearn::ScaledGradientOptimizer::optimize().

template<class T> void squareProductAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 2948 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::TransposeProductVariable::bbprop().

template<class T> void squareProductTranspose (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3106 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void squareProductTransposeAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3223 of file TMat_maths_impl.h. References k, PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::TransposeProductVariable::bbprop().

Var squareroot (  Var  v  )  [inline]

Definition at line 73 of file SquareRootVariable.h.

template<class T> TVec<T> squareroot (  const TVec< T > &  vec  )

Definition at line 1346 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and sqrt().

template<class T> void squareSubtract (  const TVec< T > &  vec, const TVec< T > &  x )

Tvec[i] -= x[i]*x[i];. Definition at line 2157 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and x. Referenced by PLearn::SharpeRatioStatsIterator::finish(), and PLearn::StderrStatsIterator::finish().

int ssaupd_ (  long int *  , const char *  , long int *  , const char *  , long int *  , float *  , float *  , long int *  , float *  , long int *  , long int *  , long int *  , float *  , float *  , long int *  , long int *  , short  , short  )

Referenced by eigenSparseSymmMat().

int sseupd_ (  long int *  , const char *  , long int *  , float *  , float *  , long int *  , float *  , const char *  , long int *  , const char *  , long int *  , float *  , float *  , long int *  , float *  , long int *  , long int *  , long int *  , float *  , float *  , long int *  , long int *  , short  , short  , short  )

Referenced by eigenSparseSymmMat().

void ssyev_ (  char *  JOBZ, char *  UPLO, int *  N, float *  A, int *  LDA, float *  W, float *  WORK, int *  LWORK, int *  INFO )

Referenced by eigen_SymmMat().

void ssyevr_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, float *  A, int *  LDA, float *  VL, float *  VU, int *  IL, int *  IU, float *  ABSTOL, int *  M, float *  W, float *  Z, int *  LDZ, int *  ISUPPZ, float *  WORK, int *  LWORK, int *  IWORK, int *  LIWORK, int *  INFO )

Referenced by lapack_Xsyevr_().

void ssyevx_ (  char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, float *  A, int *  LDA, float *  VL, float *  VU, int *  IL, int *  IU, float *  ABSTOL, int *  M, float *  W, float *  Z, int *  LDZ, float *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )

Referenced by lapack_Xsyevx_().

void ssygvx_ (  int *  ITYPE, char *  JOBZ, char *  RANGE, char *  UPLO, int *  N, float *  A, int *  LDA, float *  B, int *  LDB, float *  VL, float *  VU, int *  IL, int *  IU, float *  ABSTOL, int *  M, float *  W, float *  Z, int *  LDZ, float *  WORK, int *  LWORK, int *  IWORK, int *  IFAIL, int *  INFO )

Referenced by lapack_Xsygvx_().

Var stable_cross_entropy (  Var  linear_output, Var  target )  [inline]

Definition at line 83 of file NegCrossEntropySigmoidVariable.h. Referenced by PLearn::NNet::build_(), and PLearn::NeighborhoodSmoothnessNNet::build_().

bool startsWith (  string &  base, string  s )

Definition at line 2899 of file WordNetOntology.cc. Referenced by extractFiles(), PLearn::WordNetOntology::load(), and main().

StatsIt stddev_stats (   )  [inline]

Definition at line 409 of file StatsIterator.h.

StatsIt stderr_stats (   )  [inline]

Definition at line 410 of file StatsIterator.h. Referenced by PLearn::Learner::Learner().

string PLearn::stemWord (  string &  word, int  wn_pos )

Definition at line 2869 of file WordNetOntology.cc. References cstr().

string PLearn::stemWord (  string &  word  )

Definition at line 2842 of file WordNetOntology.cc. References cstr(). Referenced by PLearn::WordNetOntology::extractWord(), PLearn::WordNetOntology::isInWordNet(), main(), and PLearn::TextSenseSequenceVMatrix::permute().

char * PLearn::strcopy (  char *  s  )

make a copy of a C string and return it Definition at line 87 of file general.cc. References strlen().

char* stringPos (  const char *  s, const char *  strings[] )

Definition at line 93 of file TypesNumeriques.cc. Referenced by compactRepresentationTranslate().

vector< string > PLearn::stringvector (  int  argc, char **  argv )

makes a C++ style vector of strings from a C style vectr of strings Note: this may be useful in conjunction with get_option. Definition at line 564 of file stringutils.cc. Referenced by old_plearn_main(), and plearn_main().

int strlen (  char *  s  )  [static]

Definition at line 68 of file FileVMatrix.cc. Referenced by PLearn::FieldValue::FieldValue(), PLearn::VVMatrix::getDateOfVMat(), hashval(), loadLetters(), loadSTATLOG(), loadUCIMLDB(), PLearn::PStream::operator<<(), savePMat(), savePVec(), strcopy(), PLearn::FileVMatrix::updateHeader(), viewVMat(), word_sequences2files_int_stream(), PLearn::PStream::write(), and PLearn::PStream::writeAsciiNum().

real PLearn::student_t_cdf (  real  t, int  nb_degrees_of_freedom )

Student-t cumulative distribution function Definition at line 158 of file random.cc. References incomplete_beta(), and PLERROR.

Var subMat (  Var  v, int  i, int  j, int  l, int  w )  [inline]

Definition at line 89 of file SubMatVariable.h. Referenced by PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::Var::column(), PLearn::Variable::column(), PLearn::Var::operator()(), PLearn::Var::row(), PLearn::Variable::row(), PLearn::Variable::subVec(), and PLearn::Var::subVec().

Var subsample (  Var  input, int  subsample_factor )  [inline]

Definition at line 81 of file SubsampleVariable.h.

template<class T> void subsample (  TMat< T >  m, int  thesubsamplefactor, TMat< T >  result )

Definition at line 5600 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), norm(), sum(), and PLearn::TMat< T >::width(). Referenced by PLearn::SubsampleVariable::fprop().

template<class T> void substract (  const TMat< T > &  m1, const TMat< T > &  m2, TMat< T > &  destination )

Definition at line 4574 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void substract (  const TVec< T > &  source1, const TVec< T > &  source2, TVec< T > &  destination )

Definition at line 1501 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, and PLearn::TVec< T >::resize().

template<class T> void substract (  const TVec< T > &  source1, T  source2, TVec< T > &  destination )  [inline]

Definition at line 1496 of file TMat_maths_impl.h. References add(). Referenced by PLearn::ProjectionErrorVariable::bprop(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), computeMeanAndVariance(), computeWeightedMeanAndCovar(), PLearn::DiagonalNormalRandomVariable::EMBprop(), PLearn::MinusRandomVariable::EMBprop(), PLearn::PlusRandomVariable::EMBprop(), PLearn::DiagonalNormalRandomVariable::EMUpdate(), PLearn::ProjectionErrorVariable::fprop(), PLearn::NllSemisphericalGaussianVariable::fprop(), PLearn::GaussianContinuum::get_image_matrix(), PLearn::LocalNeighborsDifferencesVMatrix::getNewRow(), logOfNormal(), operator-(), and PLearn::GaussianContinuum::train().

template<class T> void substractFromColumns (  const TMat< T > &  mat, const TVec< T >  col, bool  ignored )

Definition at line 3746 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> void substractFromRows (  const TMat< T > &  mat, const TVec< T >  row, bool  ignored )

Definition at line 3733 of file TMat_maths_impl.h. References PLearn::TMat< T >::length().

Var sum (  Var  v  )  [inline]

Definition at line 75 of file SumVariable.h.

template<class T> T sum (  const TMat< T > &  mat, bool  ignore_missing = false )

Definition at line 3767 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), is_missing(), PLearn::TMat< T >::length(), MISSING_VALUE, PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width().

template<class T> T sum (  const TVec< T > &  vec, bool  ignore_missing = false )

Definition at line 245 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), and MISSING_VALUE. Referenced by PLearn::LogSoftmaxVariable::bprop(), PLearn::TextSenseSequenceVMatrix::build_(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::KNNVMatrix::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::HistogramDistribution::calc_density_from_survival(), PLearn::GraphicalBiText::check_consitency(), PLearn::ComplementedProbSparseMatrix::checkCondProbIntegrity(), PLearn::SmoothedProbSparseMatrix::checkCondProbIntegrity(), PLearn::ProbSparseMatrix::checkCondProbIntegrity(), choleskyDecomposition(), choleskyInvert(), choleskySolve(), PLearn::GraphicalBiText::compute_likelihood(), PLearn::DivisiveNormalizationKernel::computeAverage(), PLearn::AdditiveNormalizationKernel::computeAverage(), computeConditionalMeans(), PLearn::MultiInstanceNNet::computeCostsFromOutputs(), PLearn::LiftStatsCollector::computeLift(), PLearn::LiftStatsCollector::computeLiftMax(), PLearn::KPCATangentLearner::computeOutput(), computeWeightedMean(), convolve(), PLearn::MixtureRandomVariable::ElogP(), PLearn::MixtureRandomVariable::EMBprop(), PLearn::MultinomialRandomVariable::EMUpdate(), PLearn::MixtureRandomVariable::EMUpdate(), entropy(), PLearn::Kernel::estimateHistograms(), PLearn::RowMapSparseValueMatrix< T >::euclidianDistance(), PLearn::RowMapSparseMatrix< real >::euclidianDistance(), PLearn::NegLogProbCostFunction::evaluate(), PLearn::ReconstructionWeightsKernel::evaluate_sum_k_i_k_j(), PLearn::HistogramDistribution::expectation(), PLearn::LiftStatsIterator::finish(), PLearn::SumVariable::fprop(), PLearn::SoftmaxLossVariable::fprop(), PLearn::ProjectionErrorVariable::fprop(), PLearn::MatrixSoftmaxLossVariable::fprop(), PLearn::DotProductVariable::fprop(), PLearn::GeneralizedOneHotVMatrix::GeneralizedOneHotVMatrix(), PLearn::JoinVMatrix::getNewRow(), loadATT800(), logadd(), PLearn::DiagonalNormalRandomVariable::logP(), makeRowsSumTo1(), mean(), PLearn::RowMapSparseMatrix< real >::multiplyVecs(), norm(), normalizeColumns(), normalizeRows(), PLearn::TextSenseSequenceVMatrix::permute(), pl_gser(), pownorm(), PLearn::ReconstructionWeightsKernel::reconstruct(), PLearn::SumVariable::rfprop(), PLearn::SoftmaxLossVariable::rfprop(), PLearn::OneHotSquaredLoss::rfprop(), PLearn::DotProductVariable::rfprop(), rowSum(), PLearn::EntropyContrast::set_NNcontinuous_gradient(), PLearn::AdditiveNormalizationKernel::setDataForKernelMatrix(), softmax(), subsample(), PLearn::DoubleAccessSparseMatrix< T >::sumCol(), PLearn::ProbabilitySparseMatrix::sumOfElements(), PLearn::DoubleAccessSparseMatrix< T >::sumOfElements(), PLearn::DoubleAccessSparseMatrix< T >::sumRow(), PLearn::PlusScalarVariable::symbolicBprop(), PLearn::MinusScalarVariable::symbolicBprop(), PLearn::DuplicateScalarVariable::symbolicBprop(), PLearn::HistogramDistribution::variance(), vmatmain(), and weighted_variance().

template<class T> T sum_of_log (  const TVec< T > &  vec  )

returns the sum of the log of the elements (this is also the log of the product of the elements but is more stable if you have very small elements). Definition at line 261 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and log().

template<class T> T sum_of_squares (  const TMat< T > &  mat  )

Definition at line 3794 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), and PLearn::TMat< T >::width(). Referenced by columnSumOfSquares().

Var sumabs (  Var  v  )  [inline]

Definition at line 73 of file SumAbsVariable.h.

template<class T> T sumabs (  const TMat< T > &  m  )

returns the sum of absolute value of the elements Definition at line 202 of file TMat_maths_impl.h. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::compact_begin(), PLearn::TMat< T >::compact_end(), PLearn::TMat< T >::end(), PLearn::TMat< T >::isCompact(), and PLearn::TMat< T >::size().

template<class T> T sumabs (  const TVec< T > &  x  )

returns the sum of absolute values of elements Definition at line 131 of file TMat_maths_impl.h. References x. Referenced by PLearn::NNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::ConditionalDensityNet::build_(), and PLearn::AffineTransformWeightPenalty::fprop().

Var sumOf (  Var  output, const VarArray &  inputs, VMat  distr, int  nsamples, VarArray  parameters = VarArray() )  [inline]

deprecated old version do not use! Definition at line 99 of file SumOfVariable.h. References sumOf().

Var sumOf (  VMat  distr, Func  f, int  nsamples )  [inline]

sumOf Definition at line 95 of file SumOfVariable.h.

Var sumOf (  VMat  distr, Func  f, int  nsamples, int  input_size )  [inline]

Definition at line 96 of file MatrixSumOfVariable.h. Referenced by sumOf().

Var sumOverBags (  VMat  vmat, Func  f, int  max_bag_size, int  nsamples, bool  average = false, bool  transpose = false )  [inline]

sumOf Definition at line 122 of file SumOverBagsVariable.h. References transpose(). Referenced by PLearn::NeighborhoodSmoothnessNNet::train(), and PLearn::MultiInstanceNNet::train().

Var sumsquare (  Var  v  )  [inline]

Definition at line 73 of file SumSquareVariable.h.

template<class T> T sumsquare (  const TMat< T > &  m  )

returns the sum of squared elements Definition at line 174 of file TMat_maths_impl.h. References PLearn::TMat< T >::begin(), PLearn::TMat< T >::compact_begin(), PLearn::TMat< T >::compact_end(), PLearn::TMat< T >::end(), PLearn::TMat< T >::isCompact(), PLearn::TMat< T >::size(), and square().

template<class T> T sumsquare (  const TVec< T > &  x  )

returns the sum of squared elements Definition at line 119 of file TMat_maths_impl.h. References square(), and x. Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::ConditionalDensityNet::build_(), computeConditionalMeans(), PLearn::ProjectionErrorVariable::fprop(), PLearn::AffineTransformWeightPenalty::fprop(), PLearn::JoinVMatrix::getNewRow(), paired_t_test(), PLearn::SequentialModelSelector::paired_t_test(), and vmatmain().

template<class num_t> void SVD (  const TMat< num_t > &  A, TMat< num_t > &  U, TVec< num_t > &  S, TMat< num_t > &  Vt, char  JOBZ = 'A', real  safeguard = 1 )  [inline]

Performs the SVD decomposition A = U.S.Vt Where U and Vt are orthonormal matrices. A is an MxN matrix whose content is destroyed by the call. S in the above formula is also an MxN matrix, with only its first min(M,N) diagonal elements are non-zero. The call fills a vector S with those elements: the singular values, in decreasing order. JOBZ has the following meaning: 'A': all M columns of U and all N rows of Vt are returned in the arrays U and VT; 'S': the first min(M,N) columns of U and the first min(M,N) rows of Vt are returned in the arrays U and Vt; 'O': If M >= N, the first N columns of U are overwritten on the array A and all rows of Vt are returned in the array VT; otherwise, all columns of U are returned in the array U and the first M rows of Vt are overwritten in the array VT; = 'N': no columns of U or rows of Vt are computed. 'N': compute only the singular values (U and V are not computed) The optional value 'safeguard' may be used with a value > 1 if there is a crash in the SVD (typically, saying that parameter 12 has an illegal value). Relationships between SVD(A) and eigendecomposition of At.A and A.At -> square(singular values) = eigenvalues -> columns of V (rows of Vt) are the eigenvectors of At.A -> columns of U are the eigenvectors of A.At Definition at line 501 of file plapack.h. References lapackSVD(). Referenced by PLearn::PLS::train().

template<class T> void swap (  TVec< T > &  a, TVec< T > &  b )

Definition at line 101 of file TVec_impl.h. References PLearn::TVec< T >::begin(), and PLearn::TVec< T >::end().

template<class T> void swap (  TMat< T > &  a, TMat< T > &  b )

Definition at line 628 of file TMat_impl.h. References PLearn::TMat< T >::begin(), and PLearn::TMat< T >::end().

template<class T> void swap (  T &  a, T &  b )  [inline]

Definition at line 239 of file general.h.

template<class T> void swap (  Array< T > &  a1, Array< T > &  a2 )

Definition at line 56 of file Array_impl.h. References PLearn::TVec< T >::data(), PLERROR, and PLearn::TVec< T >::size(). Referenced by PLearn::SmallVector< T, SizeBits, Allocator >::operator=(), PLearn::FieldValue::operator=(), selectAndOrder(), PLearn::TinyVector< T, N, TTrait >::swap(), PLearn::ArrayAllocatorTrivial< T, SizeBits >::swap(), PLearn::ArrayAllocator< T, SizeBits >::swap(), and PLearn::TMat< pair< real, real > >::transpose().

real tabulated_soft_slope (  real  x, real  smoothness = 1, real  left = 0, real  right = 1 )  [inline]

Definition at line 358 of file pl_math.h. References hard_slope(), left(), right(), tabulated_softplus(), and x. Referenced by PLearn::SoftSlopeVariable::bprop(), and PLearn::SoftSlopeVariable::fprop().

real PLearn::tabulated_soft_slope_integral (  real  smoothness = 1, real  left = 0, real  right = 1, real  a = 0, real  b = 1 )

Definition at line 254 of file pl_math.cc. References hard_slope_integral(), left(), right(), and tabulated_softplus_primitive(). Referenced by PLearn::SoftSlopeIntegralVariable::fprop().

real tabulated_softplus (  real  x  )  [inline]

Definition at line 304 of file pl_math.h. References softplus(), and x. Referenced by PLearn::SoftSlopeIntegralVariable::bprop(), and tabulated_soft_slope().

real tabulated_softplus_primitive (  real  x  )  [inline]

Definition at line 413 of file pl_math.h. References softplus_primitive(), and x. Referenced by tabulated_soft_slope_integral().

Var tanh (  Var  v  )  [inline]

Definition at line 75 of file TanhVariable.h.

template<class T> void tanh (  const TVec< T > &  x, TVec< T > &  y )

Definition at line 1621 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, tanh(), and x.

template<class T> TVec<T> tanh (  const TVec< T > &  src  )  [inline]

Definition at line 958 of file TMat_maths_impl.h. References compute_tanh(), and PLearn::TVec< T >::length(). Referenced by PLearn::TangentLearner::build_(), PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::GaussianContinuum::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::EntropyContrast::compute_df_dx(), compute_tanh(), PLearn::TanhVariable::fprop(), PLearn::NNet::NNet(), PLearn::PLMathInitializer::PLMathInitializer(), PLearn::EntropyContrast::set_NNcontinuous_gradient(), PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost(), sigmoid(), tanh(), and PLearn::EntropyContrast::train().

VMat PLearn::temporalThreshold (  VMat  distr, int  threshold_date, bool  is_before, int  yyyy_col, int  mm_col, int  dd_col )

Definition at line 1208 of file VMat_maths.cc. References PLearn::VMat::length(), PLearn::TVec< T >::resize(), and PLearn::VMat::rows().

VMat PLearn::temporalThreshold (  VMat  distr, int  threshold_date, bool  is_before, int  yyyymmdd_col )

This VMat is a SelectRowsVMatrix which, given a threshold date, keep only the rows earlier (or later) than this date. The thresdold date is given as a YYYYMMDD date, and the date on the original VMatrix are kept on 1 column (YYYYMMDD) or 3 (YYYY, MM and DD). Definition at line 1192 of file VMat_maths.cc. References PLearn::VMat::length(), PLearn::TVec< T >::resize(), and PLearn::VMat::rows().

real PLearn::testNoCorrelationAsymptotically (  real  r, int  n )

Return P(|R|>|r|) two-sided p-value for the null-hypothesis that there is no monotonic dependency, with r the observed correlation between two paired samples of length n. The p-value is computed by taking advantage of the fact that under the null hypothesis r*sqrt(n-1) converges to a Normal(0,1), if n is LARGE ENOUGH (approx. > 30). Definition at line 105 of file stats_utils.cc. References gauss_01_cum(), and sqrt(). Referenced by correlations(), and testSpearmanRankCorrelation().

void PLearn::testSpearmanRankCorrelation (  const VMat &  x, const VMat &  y, Mat &  r, Mat &  pvalues )

same as above but return also in r the rank correlations Definition at line 128 of file stats_utils.cc. References PLearn::TMat< T >::length(), PLearn::TMat< T >::resize(), SpearmanRankCorrelation(), testNoCorrelationAsymptotically(), PLearn::TMat< T >::width(), and x. Referenced by PLearn::TestDependencyCommand::run(), PLearn::TestDependenciesCommand::run(), and testSpearmanRankCorrelationPValues().

void PLearn::testSpearmanRankCorrelationPValues (  const VMat &  x, const VMat &  y, Mat &  pvalues )

Compute P(|R|>|r|) two-sided p-value for the null-hypothesis that there is no monotonic dependency, with r the observed Spearman Rank correlation between two paired samples x and y of length n (column matrices). The p-value is computed by taking advantage of the fact that under the null hypothesis r*sqrt(n-1) is Normal(0,1). If x and y have width wx and wy respectively than the statistic is computed for each pair of column (the first taken from x and the second from y) and pvalues will be a symmetric matrix size wx by wy upon return. N.B. If x holds in memory than copying it to a matrix (toMat()) before calling this function will speed up computation significantly. Definition at line 122 of file stats_utils.cc. References testSpearmanRankCorrelation(), and x.

VMat thresholdVMat (  VMat  d, real  threshold, real  cold_value = 0.0, real  hot_value = 1.0, bool  gt_threshold = true )  [inline]

Definition at line 82 of file ThresholdVMatrix.h.

Var times (  Var  v, Var  w )  [inline]

Definition at line 77 of file TimesVariable.h. Referenced by PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::Profiler::end(), PLearn::AutoRunCommand::run(), and PLearn::Profiler::start().

Var timesScalar (  Var  v, Var  scalar )  [inline]

Definition at line 78 of file TimesScalarVariable.h. Referenced by PLearn::NeighborhoodSmoothnessNNet::build_().

bool PLearn::tobool (  const string &  s  )

Definition at line 119 of file stringutils.cc. References PLERROR. Referenced by PLearn::WordNetOntology::load(), and main().

double todouble (  const Row::iterator &  it  )

Generic conversions from an iterator.

double todouble (  const RowIterator &  it  )

Definition at line 673 of file SimpleDB.cc. References PLearn::RowIterator::toDouble().

double PLearn::todouble (  const string &  s  )

Definition at line 127 of file stringutils.cc. References MISSING_VALUE. Referenced by PLearn::SDBWithStats::computeStats(), PLearn::SDBVMFieldRemapReals::SDBVMFieldRemapReals(), and tofloat().

float tofloat (  const string &  s  )  [inline]

FLOAT. Definition at line 95 of file stringutils.h. References todouble(). Referenced by PLearn::VMatLanguage::generateCode(), and PLearn::SDBWithStats::loadStats().

template<class T> Object* toIndexedObjectPtr (  const T &  , int  )

Definition at line 577 of file Object.h. References PLERROR.

template<class T> Object* toIndexedObjectPtr (  const TVec< T > &  x, int  i )

Definition at line 574 of file Object.h. References toObjectPtr(), and x.

template<class T> Object* toIndexedObjectPtr (  const Array< T > &  x, int  i )

Definition at line 571 of file Object.h. References toObjectPtr(), and x. Referenced by PLearn::Option< DeallocatorType, self >::getIndexedObject().

int toint (  const string &  s, int  base = 10 )  [inline]

Definition at line 94 of file stringutils.h. References tolong(). Referenced by PLearn::AsciiVMatrix::build_(), PLearn::VVMatrix::createPreproVMat(), PLearn::MatlabInterface::eigs_r11(), PLearn::VMatLanguage::generateCode(), PLearn::VVMatrix::generateVMatIndex(), PLearn::VMatrix::getFieldIndex(), PLearn::VecStatsCollector::getFieldNum(), getList(), PLearn::ShellProgressBar::getWcAsciiFileLineCount(), interactiveDisplayCDF(), PLearn::WordNetOntology::load(), loadAscii(), loadAsciiSingleBinaryDescriptor(), loadClassificationDataset(), PLearn::VMatrix::loadFieldInfos(), PLearn::WordNetOntology::loadPredominentSyntacticClasses(), PLearn::SDBWithStats::loadStats(), main(), matlabR11eigs(), old_plearn_main(), PLearn::StatSpec::parseStatname(), PLearn::PDate::PDate(), PLearn::PDateTime::PDateTime(), plotVMats(), PLearn::VMatLanguage::preprocess(), readAndMacroProcess(), readHeader(), PLearn::Object::readOptionVal(), PLearn::TestDependencyCommand::run(), PLearn::TestDependenciesCommand::run(), PLearn::KolmogorovSmirnovCommand::run(), PLearn::JulianDateCommand::run(), train_and_test(), viewVMat(), vmatmain(), and PLearn::Object::writeOptionVal().

long PLearn::tolong (  const string &  s, int  base = 10 )

conversions from string to numerical types Definition at line 107 of file stringutils.cc. References PLERROR. Referenced by PLearn::WordNetOntology::load(), and toint().

Object* toObjectPtr (  const SetOption &  o  )  [inline]

Definition at line 113 of file SetOption.h.

Object* toObjectPtr (  const RealMapping &  o  )  [inline]

Definition at line 237 of file RealMapping.h.

template<class T> Object* toObjectPtr (  const PP< T > &  x  )  [inline]

Definition at line 568 of file Object.h. References toObjectPtr(), and x.

template<> Object* toObjectPtr (  const Object *  x  )  [inline]

Definition at line 564 of file Object.h. References x.

template<> Object* toObjectPtr (  const Object &  x  )  [inline]

Definition at line 561 of file Object.h. References x.

template<class T> Object* toObjectPtr (  const T &  x  )  [inline]

Definition at line 557 of file Object.h. References PLERROR.

template<class T> Object* toObjectPtr (  const T *  x  )  [inline]

The toObjectPtr functions attempt to return a pointer to Object (or 0 if the passed argument cannot be considered an Object subclass). Definition at line 554 of file Object.h. References PLERROR. Referenced by PLearn::Option< DeallocatorType, self >::getAsObject(), toIndexedObjectPtr(), and toObjectPtr().

string tostring (  const Row::iterator &  it  )

string tostring (  const RowIterator &  it  )

Definition at line 678 of file SimpleDB.cc. References PLearn::RowIterator::toString().

string tostring (  const char *  s  )  [inline]

specialised version for char* Definition at line 78 of file stringutils.h.

template<class T> string PLearn::tostring (  const T &  x  )

------------------------------------------------------------------ ****************** Implementation * Definition at line 276 of file stringutils.h. References x.

string PLearn::tostring (  const float &  x  )

Definition at line 637 of file stringutils.cc. References x.

string PLearn::tostring (  const double &  x  )

Definition at line 624 of file stringutils.cc. References x. Referenced by PLearn::TextProgressBarPlugin::addProgressBar(), PLearn::DiskVMatrix::appendRow(), PLearn::MovingAverageVMatrix::build_(), PLearn::GraphicalBiText::build_(), PLearn::DiskVMatrix::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::ConcatColumnsVMatrix::build_(), PLearn::SequentialModelSelector::checkModelNames(), PLearn::VMatLanguage::compileStream(), compute_learner_outputs_on_grid(), computeOutputFields(), PLearn::SDBWithStats::computeStats(), cross_valid(), PLearn::ShellProgressBar::draw(), DX_create_grid_outputs_file(), DX_write_2D_fields(), PLearn::MatlabInterface::eigs_r11(), PLearn::VMatrix::getFieldInfos(), PLearn::Variable::getName(), PLearn::VMatrix::getString(), PLearn::PLearner::getTestCostIndex(), PLearn::PLearner::getTrainCostIndex(), PLearn::UnequalConstantVariable::info(), PLearn::TimesConstantVariable::info(), PLearn::QuantilesStatsIterator::info(), PLearn::SelectedOutputCostFunction::info(), PLearn::PowDistanceKernel::info(), PLearn::PlusConstantVariable::info(), PLearn::PDateTime::info(), PLearn::PDate::info(), PLearn::EqualConstantVariable::info(), PLearn::DistanceKernel::info(), loadAscii(), PLearn::GraphicalBiText::loadBitext(), main(), PLearn::GaussianContinuum::make_random_walk(), makeFileNameValid(), matlabR11eigs(), matlabSave(), PLearn::Learner::measure(), PLearn::PTester::perform(), PLearn::Gnuplot::plotClasses(), plotVMats(), PLearn::VMatLanguage::preprocess(), PLearn::WordNetOntology::processUnknownWord(), readAndMacroProcess(), PLearn::SequentialValidation::run(), PLearn::Experiment::run(), PLearn::FinancePreprocVMatrix::setVMFields(), PLearn::Learner::stop_if_wanted(), PLearn::SequentialModelSelector::test(), PLearn::RowIterator::toString(), PLearn::FieldValue::toString(), PLearn::TangentLearner::train(), PLearn::StackedLearner::train(), PLearn::SequentialModelSelector::train(), PLearn::NNet::train(), PLearn::NeighborhoodSmoothnessNNet::train(), PLearn::MultiInstanceNNet::train(), PLearn::GraphicalBiText::train(), PLearn::GaussianContinuum::train(), PLearn::ConditionalDensityNet::train(), PLearn::ClassifierFromDensity::train(), PLearn::AdaBoost::train(), PLearn::SimpleDB< KeyType, QueryResult >::truncateFromRow(), PLearn::VMatrix::unduplicateFieldNames(), PLearn::TextProgressBarPlugin::update(), PLearn::GraphicalBiText::update_WSD_model(), PLearn::FieldStat::updateNumber(), viewVMat(), vmatmain(), while(), and word_sequences2files_int_stream().

void PLearn::touch (  const string &  file  )

trivial unix touch Definition at line 567 of file fileutils.cc.

template<class T> T trace (  const TMat< T > &  mat  )

Definition at line 3422 of file TMat_maths_impl.h. References PLearn::TMat< T >::firstElement(), PLearn::TMat< T >::isSquare(), PLearn::TMat< T >::length(), and PLERROR. Referenced by regularizeMatrix().

void train_and_test (  const string &  modelalias, string  trainalias, vector< string >  testaliases )

Definition at line 127 of file old_plearn_main.cc. References endl(), exitmsg(), getDataSet(), getDatasetAliases(), getModelAliases(), isdir(), isfile(), PLearn::TVec< T >::length(), PLearn::VMat::length(), loadObject(), lsdir(), read(), save(), PLearn::TVec< T >::subVec(), toint(), and PLearn::VMat::width(). Referenced by old_plearn_main().

VMat PLearn::transpose (  VMat  m1  )

returns M1' Definition at line 902 of file VMat_maths.cc. References PLearn::VMat::toMat(), and transpose().

Var transpose (  Var  v  )  [inline]

Definition at line 90 of file SubMatTransposeVariable.h. References PLearn::Var::length(), and PLearn::Var::width().

template<class T> TMat<T> transpose (  const TMat< T > &  src  )

Definition at line 5318 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), transpose(), and PLearn::TMat< T >::width().

template<class T> void transpose (  const TMat< T >  src, TMat< T >  dest )

Definition at line 5297 of file TMat_maths_impl.h. References PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ConditionalDensityNet::build_(), PLearn::Var::column(), PLearn::Variable::column(), PLearn::Var::row(), PLearn::Variable::row(), PLearn::EntropyContrast::set_NNcontinuous_gradient_from_extra_cost(), solveLinearSystem(), PLearn::Variable::subVec(), PLearn::Var::subVec(), sumOverBags(), PLearn::SumOverBagsVariable::SumOverBagsVariable(), PLearn::SubMatTransposeVariable::symbolicBprop(), transpose(), PLearn::RowMapSparseMatrix< real >::transposeProduct(), unfoldedFunc(), PLearn::UnfoldedFuncVariable::UnfoldedFuncVariable(), and vmatmain().

Vec PLearn::transposeProduct (  VMat  m1, Vec  v2 )

computes M1'.V2 Definition at line 841 of file VMat_maths.cc. References PLearn::TVec< T >::clear(), PLearn::TVec< T >::length(), PLearn::VMat::length(), PLERROR, and PLearn::VMat::width().

Mat PLearn::transposeProduct (  VMat  m1, VMat  m2 )

computes M1'.M2 Definition at line 820 of file VMat_maths.cc. References PLearn::VMat::length(), PLERROR, rowmatrix(), transposeProductAcc(), and PLearn::VMat::width().

Mat PLearn::transposeProduct (  VMat  m  )

computes M'.M Definition at line 805 of file VMat_maths.cc. References PLearn::VMat::length(), rowmatrix(), transposeProductAcc(), and PLearn::VMat::width().

Var transposeProduct (  Var &  m1, Var &  m2 )  [inline]

Definition at line 79 of file TransposeProductVariable.h.

template<class T> TMat<T> transposeProduct (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

return m1' x m2 Definition at line 5699 of file TMat_maths_impl.h. References transposeProduct(), and PLearn::TMat< T >::width().

template<class T> TVec<T> transposeProduct (  const TMat< T > &  m, const TVec< T > &  v )  [inline]

return m' x v Definition at line 5689 of file TMat_maths_impl.h. References transposeProduct(), and PLearn::TMat< T >::width().

template<class T> void transposeProduct (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3253 of file TMat_maths_impl.h. References PLearn::TMat< T >::clear(), PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void transposeProduct (  const TVec< T > &  result, const TMat< T > &  m, const TVec< T > &  v )

result[i] = sum_j m[j,i] * v[j] Equivalently: rowvec(result) = rowvec(v) . m Equivalently: columnvec(result) = transpose(m).columnvec(v) Definition at line 2257 of file TMat_maths_impl.h. References PLearn::TVec< T >::clear(), PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::NNet::build_(), PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::MultiInstanceNNet::build_(), PLearn::ConditionalDensityNet::build_(), closestPointOnHyperplane(), PLearn::GaussianContinuum::compute_train_and_validation_costs(), computeCovar(), computeMeanAndCovar(), PLearn::PLS::computeOutput(), PLearn::LinearRegressor::computeOutput(), computePrincipalComponents(), PLearn::ProductRandomVariable::EMBprop(), PLearn::TransposeProductVariable::fprop(), PLearn::ProjectionErrorVariable::fprop(), PLearn::NllSemisphericalGaussianVariable::fprop(), PLearn::GaussianDistribution::generate(), PLearn::GaussianContinuum::get_image_matrix(), linearRegressionNoBias(), PLearn::TransposeProductVariable::rfprop(), PLearn::ProductVariable::symbolicBprop(), PLearn::ProductTransposeVariable::symbolicBprop(), PLearn::PLS::train(), PLearn::PCA::train(), PLearn::GaussianContinuum::train(), transposeProduct(), and PLearn::Function::verifyrfprop().

template<class T> void transposeProduct2 (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3281 of file TMat_maths_impl.h. References PLearn::TMat< T >::clear(), PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void transposeProduct2Acc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3339 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductVariable::bbprop(), and PLearn::ProductTransposeVariable::bbprop().

template<class T> void transposeProductAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3312 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void transposeProductAcc (  const TVec< T > &  result, const TMat< T > &  m, const TVec< T > &  v, T  alpha )

result[i] += alpha * sum_j m[j,i] * v[j] Definition at line 2322 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void transposeProductAcc (  const TVec< T > &  result, const TMat< T > &  m, const TVec< T > &  v )

result[i] += sum_j m[j,i] * v[j] Definition at line 2277 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width(). Referenced by PLearn::ProductVariable::bprop(), PLearn::ProductTransposeVariable::bprop(), PLearn::MatrixAffineTransformVariable::fprop(), PLearn::AffineTransformVariable::fprop(), PLearn::TransposeProductVariable::rfprop(), and transposeProduct().

template<class T> void transposeTransposeProduct (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3369 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> void transposeTransposeProductAcc (  const TMat< T > &  mat, const TMat< T > &  m1, const TMat< T > &  m2 )

Definition at line 3397 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), k, PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

string PLearn::trimWord (  string  word  )

Definition at line 2790 of file WordNetOntology.cc. References isDigit(), isLegalPunct(), isLetter(), and NULL_TAG. Referenced by PLearn::WordNetOntology::extractWord(), and PLearn::WordNetOntology::isInWordNet().

template<class T> T two (  const T &  x  )  [inline]

Definition at line 134 of file pl_math.h. References x. Referenced by PLearn::OneHotSquaredLoss::bprop(), PLearn::MatrixOneHotSquaredLoss::bprop(), PLearn::AffineTransformWeightPenalty::bprop(), and PLearn::TestDependencyCommand::TestDependencyCommand().

real ultrafastsigmoid (  const real &  x  )  [inline]

Definition at line 230 of file pl_math.h. References ultrafasttanh(), and x.

real ultrafasttanh (  const real &  x  )  [inline]

Definition at line 212 of file pl_math.h. References x. Referenced by ultrafastsigmoid().

Var unary_hard_slope (  Var  v, real  l = -1, real  r = 1 )  [inline]

Definition at line 83 of file UnaryHardSlopeVariable.h. Referenced by PLearn::NNet::build_().

void PLearn::uncompress_vec (  char *  comprbuf, double *  data, int  l, bool  double_stored_as_float = false )

Definition at line 494 of file pl_io.cc. References mode, PLERROR, read_compr_mode_and_size_ptr(), and val.

string PLearn::underscore_to_space (  string  str  )

replaces all underscores by a single space character Definition at line 345 of file stringutils.cc. Referenced by PLearn::WordNetOntology::extractWord(), PLearn::WordNetOntology::isInWordNet(), and matlabSave().

Var unfoldedFunc (  Var  input_matrix, Func  f, bool  transpose = false )  [inline]

Definition at line 90 of file UnfoldedFuncVariable.h. References transpose(). Referenced by PLearn::NeighborhoodSmoothnessNNet::build_().

Var unfoldedSumOf (  Var  input_matrix, Var  bag_size, Func  f, int  max_bag_size )  [inline]

Definition at line 91 of file UnfoldedSumOfVariable.h. Referenced by PLearn::MultiInstanceNNet::build_().

int PLearn::uniform_multinomial_sample (  int  N  )

return an integer between 0 and N-1 with equal probabilities Definition at line 553 of file random.cc. References uniform_sample(). Referenced by bootstrap(), bootstrap_rows(), fill_random_discrete(), PLearn::EmpiricalDistribution::generate(), and smartInitialization().

real PLearn::uniform_sample (   )

returns a random number uniformly distributed between 0 and 1 Definition at line 248 of file random.cc. References AM1, IA1, IA2, IM1, IM2, IMM1, IQ1, IQ2, IR1, IR2, k, NDIV1, NTAB, RNMX, and the_seed. Referenced by bnldev(), bounded_uniform(), PLearn::RepeatSplitter::build_(), expdev(), fill_random_uniform(), gamdev(), gaussian_01(), PLearn::ConditionalDensityNet::generate(), PLearn::UniformVMatrix::get(), PLearn::UniformVMatrix::getSubRow(), multinomial_sample(), PLearn::TextSenseSequenceVMatrix::permute(), poidev(), randomShuffleRows(), shuffleElements(), shuffleRows(), PLearn::AdaBoost::train(), and uniform_multinomial_sample().

Mat PLearn::unitmatrix (  int  n  )

Definition at line 148 of file Mat.cc.

void update (  ProbabilitySparseMatrix &  pYX, ProbabilitySparseMatrix &  nYX )  [inline]

Definition at line 497 of file ProbabilitySparseMatrix.h. References PLearn::Set::begin(), PLearn::ProbabilitySparseMatrix::clear(), PLearn::ProbabilitySparseMatrix::computeX(), PLearn::ProbabilitySparseMatrix::computeY(), PLearn::Set::end(), PLearn::ProbabilitySparseMatrix::set(), SetIterator, PLearn::ProbabilitySparseMatrix::sumPYx(), x, PLearn::ProbabilitySparseMatrix::X, and PLearn::ProbabilitySparseMatrix::Y. Referenced by PLearn::GraphicalBiText::compute_BN_likelihood(), PLearn::GraphicalBiText::compute_likelihood(), PLearn::ShellProgressBar::done(), PLearn::SequentialModelSelector::test(), PLearn::SequentialModelSelector::train(), PLearn::VecStatsCollector::update(), PLearn::StatsIterator::update(), and PLearn::StatsItArray::update().

void updateAndClearCounts (  ProbabilitySparseMatrix &  pYX, ProbabilitySparseMatrix &  nYX )  [inline]

Definition at line 521 of file ProbabilitySparseMatrix.h. References PLearn::Set::begin(), PLearn::ProbabilitySparseMatrix::clear(), PLearn::ProbabilitySparseMatrix::computeX(), PLearn::ProbabilitySparseMatrix::computeY(), PLearn::Set::end(), PLearn::ProbabilitySparseMatrix::set(), SetIterator, PLearn::ProbabilitySparseMatrix::sumPYx(), x, PLearn::ProbabilitySparseMatrix::X, and PLearn::ProbabilitySparseMatrix::Y.

string PLearn::upperstring (  const string &  s  )

convert a string to all uppercase Definition at line 278 of file stringutils.cc. Referenced by PLearn::RealMapping::read().

void usage (   )

Definition at line 460 of file old_plearn_main.cc. References endl(). Referenced by PLearn::FieldConvertCommand::FieldConvertCommand(), and old_plearn_main().

void use (  const string &  modelfile, const string &  datasetalias )

Definition at line 407 of file old_plearn_main.cc. References endl(), exitmsg(), getDataSet(), getDatasetAliases(), PLearn::VMat::length(), loadObject(), remove_extension(), PLearn::TVec< T >::subVec(), and PLearn::VMat::width(). Referenced by PLearn::Learner::apply(), PLearn::Learner::computeLeaveOneOutCosts(), PLearn::Learner::computeOutput(), PLearn::FieldConvertCommand::FieldConvertCommand(), PLearn::KolmogorovSmirnovCommand::KolmogorovSmirnovCommand(), PLearn::LearnerCommand::LearnerCommand(), old_plearn_main(), PLearn::Learner::use(), PLearn::Learner::useAndCost(), and PLearn::PLearner::useOnTrain().

Var var (  real  init_value  )  [inline]

Definition at line 69 of file Var.h. Referenced by PLearn::NeuralNet::build_(), PLearn::NeighborhoodSmoothnessNNet::build_(), PLearn::ConditionalDensityNet::build_(), PLearn::AddCostToLearner::build_(), PLearn::GaussianProcessRegressor::computeCostsFromOutputs(), computeInputMeanAndVariance(), d_hard_slope(), gauss_density_var(), gauss_log_density_var(), PLearn::CompactVMatrix::perturb(), and PLearn::GaussianProcessRegressor::variance().

void PLearn::varDeepCopyField (  Var &  field, CopiesMap &  copies )

To use varDeepCopyField. Definition at line 59 of file Var.cc. Referenced by PLearn::Variable::makeDeepCopyFromShallowCopy(), PLearn::VarArray::makeDeepCopyFromShallowCopy(), PLearn::UnfoldedSumOfVariable::makeDeepCopyFromShallowCopy(), PLearn::UnaryVariable::makeDeepCopyFromShallowCopy(), PLearn::TangentLearner::makeDeepCopyFromShallowCopy(), PLearn::Optimizer::makeDeepCopyFromShallowCopy(), PLearn::NNet::makeDeepCopyFromShallowCopy(), PLearn::MultiInstanceNNet::makeDeepCopyFromShallowCopy(), PLearn::MatrixElementsVariable::makeDeepCopyFromShallowCopy(), PLearn::GaussianContinuum::makeDeepCopyFromShallowCopy(), PLearn::ConditionalDensityNet::makeDeepCopyFromShallowCopy(), PLearn::BinaryVariable::makeDeepCopyFromShallowCopy(), and PLearn::AddCostToLearner::makeDeepCopyFromShallowCopy().

template<class T> T variance (  const TMat< T > &  mat, T  meanval )

Definition at line 3844 of file TMat_maths_impl.h. References PLearn::TMat< T >::data(), PLearn::TMat< T >::length(), PLearn::TMat< T >::mod(), PLERROR, and PLearn::TMat< T >::width().

template<class T> T variance (  const TVec< T > &  vec, T  meanval )

Definition at line 408 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), and PLERROR. Referenced by columnVariance(), computeConditionalMeans(), PLearn::GaussMix::computeMeansAndCovariances(), PLearn::PDistribution::computeOutput(), PLearn::PConditionalDistribution::computeOutput(), PLearn::HistogramDistribution::computeOutput(), PLearn::DiagonalNormalRandomVariable::logP(), normal(), rowVariance(), PLearn::SequentialModelSelector::sequenceCost(), and vmatmain().

VMat vconcat (  Array< VMat >  ds  )  [inline]

Definition at line 140 of file ConcatRowsVMatrix.h. References PLearn::TVec< T >::size().

VMat vconcat (  VMat  d1, VMat  d2 )  [inline]

Definition at line 137 of file ConcatRowsVMatrix.h.

Var vconcat (  const VarArray &  varray  )  [inline]

Definition at line 79 of file ConcatRowsVariable.h.

template<class T> TMat<T> vconcat (  const TMat< T > &  m1, const TMat< T > &  m2 )  [inline]

Definition at line 211 of file Array_impl.h. References vconcat().

template<class T> TMat<T> vconcat (  const Array< TMat< T > > &  ar  )

Definition at line 169 of file Array_impl.h. References PLERROR, and PLearn::TMat< T >::subMatRows(). Referenced by PLearn::ConditionalDensityNet::build_(), PLearn::ConditionalExpression::ConditionalExpression(), PLearn::VVMatrix::createPreproVMat(), PLearn::MixtureRandomVariable::ElogP(), getDataSet(), PLearn::TrainValidTestSplitter::getSplit(), PLearn::TestInTrainSplitter::getSplit(), loadUCI(), PLearn::MixtureRandomVariable::logP(), PLearn::RVArrayRandomElementRandomVariable::logP(), PLearn::GaussianContinuum::make_random_walk(), PLearn::RVInstanceArray::operator|(), removeRow(), PLearn::JointRandomVariable::setValueFromParentsValue(), split(), PLearn::InterValuesVariable::symbolicBprop(), and vconcat().

template<class T> int vec_counts (  const TVec< T > &  src, T  value )

Definition at line 1787 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length().

template<class T> int vec_find (  const TVec< T > &  src, T  f )

Definition at line 1800 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), and PLearn::TVec< T >::length(). Referenced by PLearn::UniformizeVMatrix::getNewRow(), and PLearn::GeneralizedOneHotVMatrix::getNewRow().

void PLearn::viewVMat (  const VMat &  vm  )

Definition at line 379 of file vmatmain.cc. References PLearn::TVec< T >::begin(), PLearn::VMat::columns(), PLearn::TVec< T >::end(), endl(), PLearn::VMat::fieldName(), fname, getDataSet(), PLearn::VMat::getFieldIndex(), getList(), is_missing(), PLearn::VMat::length(), max(), min(), pl_isnumber(), PLERROR, removeblanks(), PLearn::TVec< T >::resize(), strlen(), toint(), tostring(), val, PLearn::VMat::width(), and x. Referenced by vmatmain().

int PLearn::vmatmain (  int  argc, char **  argv )

Definition at line 1279 of file vmatmain.cc. References PLearn::TVec< T >::append(), c_str(), PLearn::VMatLanguage::compileString(), displayBasicStats(), endl(), extract_extension(), PLearn::VMat::fieldName(), flush(), getDataSet(), getDataSetDate(), getDataSetHelp(), PLearn::VMatrix::getMtime(), PLearn::VVMatrix::getPrecomputedDataName(), interactiveDisplayCDF(), PLearn::VVMatrix::isPrecomputedAndUpToDate(), PLearn::VMat::length(), max(), mean(), min(), PLERROR, plotVMats(), print_diff(), printDistanceStatistics(), rm(), PLearn::VMatLanguage::run(), PLearn::TVec< T >::size(), sum(), sumsquare(), toint(), tostring(), transpose(), variance(), viewVMat(), and PLearn::VMat::width().

VMat vrange (  real  start, real  end, real  step = 1.0 )  [inline]

Definition at line 81 of file RangeVMatrix.h.

void PLearn::warningmsg (  const char *  msg,   ... )

Definition at line 92 of file plerror.cc. References endl(), ERROR_MSG_SIZE, and error_stream.

CostFunc weighted_costfunc (  CostFunc  costfunc  )  [inline]

reweighting Definition at line 83 of file WeightedCostFunction.h.

template<class T> T weighted_distance (  const TVec< T > &  vec1, const TVec< T > &  vec2, double  n, const TVec< T > &  weights )

Definition at line 824 of file TMat_maths_impl.h. References mypow(), sqrt(), and weighted_powdistance().

template<class T> T weighted_mean (  const TVec< T > &  vec, const TVec< T > &  weights, bool  ignore_missing = false )

Definition at line 383 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), is_missing(), PLearn::TVec< T >::length(), MISSING_VALUE, and PLERROR. Referenced by columnWeightedMean(), and weighted_variance().

template<class T> T weighted_powdistance (  const TVec< T > &  vec1, const TVec< T > &  vec2, double  n, const TVec< T > &  weights )

Definition at line 780 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), mypow(), and PLERROR. Referenced by PLearn::ScaledGaussianKernel::evaluate(), and weighted_distance().

Var weighted_sumsquare (  Var  v, Var  w )  [inline]

Definition at line 80 of file WeightedSumSquareVariable.h. Referenced by PLearn::NeuralNet::build_().

template<class T> T weighted_variance (  const TVec< T > &  vec, const TVec< T > &  weights, T  no_weighted_mean, T  weighted_mean )

Definition at line 448 of file TMat_maths_impl.h. References PLearn::TVec< T >::data(), PLearn::TVec< T >::length(), PLERROR, sum(), and weighted_mean(). Referenced by columnWeightedVariance().

Mat PLearn::weightedLinearRegression (  VMat  inputs, VMat  outputs, VMat  gammas, real  weight_decay )

Version that does all the memory allocations of XtX, XtY and theta_t. Returns theta_t. Definition at line 1049 of file VMat_maths.cc. References weightedLinearRegression(), and PLearn::VMat::width().

real PLearn::weightedLinearRegression (  VMat  inputs, VMat  outputs, VMat  gammas, real  weight_decay, Mat  theta_t, bool  use_precomputed_XtX_XtY, Mat  XtX, Mat  XtY, real &  sum_squared_Y, real &  sum_gammas, bool  return_squared_loss = false, int  verbose_computation_every = 0, bool  cholesky = true )

Linear regression where each input point is given a different importance weight (the gammas); returns weighted average of squared loss. Definition at line 987 of file VMat_maths.cc. References PLearn::TMat< T >::clear(), dot(), externalProductScaleAcc(), PLearn::TMat< T >::length(), PLearn::VMat::length(), PLERROR, product(), solveLinearSystem(), solveLinearSystemByCholesky(), PLearn::TMat< T >::width(), PLearn::VMat::width(), and x. Referenced by PLearn::LinearRegressor::train(), and weightedLinearRegression().

FilesIntStream * PLearn::word_sequences2files_int_stream (  const char *  word_sequences_file  )

Convert <word_sequences> filename into a FilesIntStream stream. This file must contain one line per <word_sequence> filename, and each of these filenames must represent binary integers files that can be associated to an IntStream. Definition at line 247 of file IntStream.cc. References PLERROR, strlen(), and tostring().

const string wordseparators (  " \t\n\  r  )  [static]

List of characters considered to mark a separation between "words"; This is a fairly restricted list, meaning that many things can be part of a "word" in this sense (for ex: "this-is_a+single@wor'd"), this is to insure a smooth transition for the new setOption, which calls readOptionVal ... which may call read(istream&, string&)...

template<class T> void write (  ostream &  out_, const T &  o )  [inline]

Definition at line 1085 of file PStream.h.

void write (  ostream &  out, const RealRange &  range )  [inline]

Definition at line 116 of file RealMapping.h. Referenced by PLearn::CompactVMatrix::append(), PLearn::StatsCollector::oldwrite(), PLearn::PStream::operator<<(), PLearn::pl_fdstreambuf::overflow(), PLearn::PStream::put(), PLearn::RowMapSparseMatrix< real >::save(), PLearn::pl_fdstreambuf::sync(), PLearn::Variable::write(), PLearn::RowMapSparseMatrix< real >::write(), PLearn::PStream::write(), PLearn::PStream::writeAsciiNum(), writeField(), PLearn::OptionBase::writeIntoString(), and PLearn::pl_fdstreambuf::xsputn().

void write_bool (  ostream &  out, const bool *  ptr, int  n, bool  is_file_bigendian )

void write_compr_mode_and_size (  ostream &  out, unsigned char  mode, int  size )  [inline]

Definition at line 76 of file pl_io.cc. References binwrite(), header, mode, and PLERROR. Referenced by binwrite_compressed().

void write_compr_mode_and_size_ptr (  char *&  out, unsigned char  mode, int  size )  [inline]

Definition at line 429 of file pl_io.cc. References header, mode, and PLERROR. Referenced by compress_vec().

void write_double (  ostream &  out, double  value, bool  is_file_bigendian = true )  [inline]

Definition at line 133 of file pl_io_deprecated.h. References write_double().

void PLearn::write_double (  ostream &  out, const double *  ptr, int  n, bool  is_file_bigendian )

Definition at line 338 of file pl_io_deprecated.cc. References reverse_double(). Referenced by write_double().

void write_float (  ostream &  out, float  value, bool  is_file_bigendian = true )  [inline]

Definition at line 131 of file pl_io_deprecated.h. References write_float().

void PLearn::write_float (  ostream &  out, const float *  ptr, int  n, bool  is_file_bigendian )

Definition at line 363 of file pl_io_deprecated.cc. References reverse_float(). Referenced by write_float().

void write_int (  ostream &  out, int  value, bool  is_file_bigendian = true )  [inline]

The following calls write a single value to the file in the specified representation, regardeless of the endianness on the current architecture. Definition at line 127 of file pl_io_deprecated.h. References write_int().

void PLearn::write_int (  ostream &  out, const int *  ptr, int  n, bool  is_file_bigendian )

Writes binary data to the file in the specified representation (little or big endian) regardeless of the endianness used on the current architecture. Definition at line 290 of file pl_io_deprecated.cc. References reverse_int(). Referenced by write_int(), and write_uint().

void write_sbyte (  ostream &  out, signed char  x )  [inline]

Definition at line 139 of file pl_io_deprecated.h. References x. Referenced by PLearn::VecCompressor::writeCompressedVec().

void write_short (  ostream &  out, short  value, bool  is_file_bigendian = true )  [inline]

Definition at line 129 of file pl_io_deprecated.h. References write_short().

void PLearn::write_short (  ostream &  out, const short *  ptr, int  n, bool  is_file_bigendian )

Definition at line 314 of file pl_io_deprecated.cc. References reverse_short(). Referenced by write_short(), and write_ushort().

void write_ubyte (  ostream &  out, unsigned char  x )  [inline]

Definition at line 140 of file pl_io_deprecated.h. References x.

void write_uint (  ostream &  out, unsigned int  value, bool  is_file_bigendian = true )  [inline]

Definition at line 135 of file pl_io_deprecated.h. References write_uint().

void write_uint (  ostream &  out, const unsigned int *  ptr, int  n, bool  is_file_bigendian )  [inline]

Definition at line 119 of file pl_io_deprecated.h. References write_int(). Referenced by write_uint().

void write_ushort (  ostream &  out, unsigned short  value, bool  is_file_bigendian = true )  [inline]

Definition at line 137 of file pl_io_deprecated.h. References write_ushort().

void write_ushort (  ostream &  out, const unsigned short *  ptr, int  n, bool  is_file_bigendian )  [inline]

Definition at line 121 of file pl_io_deprecated.h. References write_short(). Referenced by write_ushort().

template<class T> void writeField (  ostream &  out, const string &  fieldname, const T &  x )

generic field writing and reading Definition at line 216 of file pl_io_deprecated.h. References write(), writeFieldName(), and x. Referenced by PLearn::VMatrix::oldwrite(), PLearn::QuantilesStatsIterator::oldwrite(), PLearn::LiftStatsIterator::oldwrite(), PLearn::SharpeRatioStatsIterator::oldwrite(), PLearn::StderrStatsIterator::oldwrite(), PLearn::StddevStatsIterator::oldwrite(), PLearn::ExpMeanStatsIterator::oldwrite(), PLearn::MeanStatsIterator::oldwrite(), PLearn::StatsIterator::oldwrite(), PLearn::StatsCollector::oldwrite(), PLearn::Optimizer::oldwrite(), PLearn::Learner::oldwrite(), and PLearn::RowMapSparseMatrix< real >::write().

void PLearn::writeFieldName (  ostream &  out, const string &  fieldname )

writes "fieldname: " writes and reads the given fieldname (should be followed by wrtiting or reading of the field's value. The readFieldName method checks the read fieldname against the one passed as argument and issues an error if they do not match Definition at line 92 of file pl_io_deprecated.cc. Referenced by binwriteField(), binwriteField_double(), PLearn::StatsCollector::oldwrite(), and writeField().

void PLearn::writeFooter (  ostream &  out, const string &  classname )

writes "</ClassName>\n" Definition at line 58 of file pl_io_deprecated.cc. Referenced by PLearn::VMatrix::oldwrite(), PLearn::QuantilesStatsIterator::oldwrite(), PLearn::LiftStatsIterator::oldwrite(), PLearn::MaxStatsIterator::oldwrite(), PLearn::MinStatsIterator::oldwrite(), PLearn::SharpeRatioStatsIterator::oldwrite(), PLearn::StderrStatsIterator::oldwrite(), PLearn::StddevStatsIterator::oldwrite(), PLearn::ExpMeanStatsIterator::oldwrite(), PLearn::MeanStatsIterator::oldwrite(), PLearn::StatsIterator::oldwrite(), PLearn::StatsCollector::oldwrite(), PLearn::Optimizer::oldwrite(), PLearn::Learner::oldwrite(), PLearn::RowMapSparseMatrix< real >::write(), and PLearn::RealMapping::write().

void PLearn::writeHeader (  ostream &  out, const string &  classname, int  version = 0 )

writes "<ClassName:version>\n" Definition at line 55 of file pl_io_deprecated.cc. Referenced by PLearn::VMatrix::oldwrite(), PLearn::QuantilesStatsIterator::oldwrite(), PLearn::LiftStatsIterator::oldwrite(), PLearn::MaxStatsIterator::oldwrite(), PLearn::MinStatsIterator::oldwrite(), PLearn::SharpeRatioStatsIterator::oldwrite(), PLearn::StderrStatsIterator::oldwrite(), PLearn::StddevStatsIterator::oldwrite(), PLearn::ExpMeanStatsIterator::oldwrite(), PLearn::MeanStatsIterator::oldwrite(), PLearn::StatsIterator::oldwrite(), PLearn::StatsCollector::oldwrite(), PLearn::Optimizer::oldwrite(), PLearn::Learner::oldwrite(), PLearn::RowMapSparseMatrix< real >::write(), and PLearn::RealMapping::write().

template<class MapT> void writeMap (  PStream &  out, const MapT &  m )

Definition at line 674 of file PStream.h. References PLearn::PStream::put(), and PLearn::PStream::write(). Referenced by operator<<().

void writeNewline (  ostream &  out  )  [inline]

Writes a single newline character. Definition at line 196 of file pl_io_deprecated.h. Referenced by PLearn::StatsCollector::oldwrite().

template<class SequenceType> void writeSequence (  PStream &  out, const SequenceType &  seq )

Definition at line 891 of file PStream.h. References binwrite_(), byte_order(), LITTLE_ENDIAN_ORDER, PLearn::PStream::outmode, PLERROR, PLearn::PStream::put(), and PLearn::PStream::write(). Referenced by operator<<(), and PLearn::TVec< pair< real, real > >::write().

template<class SetT> void writeSet (  PStream &  out, const SetT &  s )

Definition at line 1120 of file PStream.h. References PLearn::PStream::put(), and PLearn::PStream::write(). Referenced by operator<<().

PStream & PLearn::ws (  PStream &  out  )

Definition at line 64 of file PStream.cc. References PLearn::PStream::skipBlanksAndComments(). Referenced by PLearn::SumOverBagsVariable::build_(), PLearn::AsciiVMatrix::build_(), PLearn::WordNetOntology::computeWordSenseUniqueIds(), PLearn::WordNetOntology::extractSenses(), getDatasetAliases(), getModelAliases(), PLearn::WordNetOntology::getSenseKey(), PLearn::WordNetOntology::getWordSenseUniqueId(), PLearn::WordNetOntology::load(), loadAscii(), loadAsciiSingleBinaryDescriptor(), PLearn::RealMapping::read(), PLearn::Object::read(), and PLearn::GaussianDistribution::train().

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Variable Documentation

PLearnInit PLearn::_plearn_init_ [static]

Definition at line 55 of file general.cc.

const char PLearn::ALPHAsymbols = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"

Definition at line 51 of file TypesNumeriques.cc. Referenced by compactRepresentationTranslate().

double PLearn::big = 4.503599627370496e15

Definition at line 710 of file random.cc. Referenced by incbcf(), and incbd().

double PLearn::biginv = 2.22044604925031308085e-16

Definition at line 711 of file random.cc. Referenced by incbcf(), and incbd().

pl_stream_clear_flags PLearn::clear_flags

Definition at line 47 of file PStream_util.cc.

const string PLearn::dbdir_name = "" [static]

Definition at line 179 of file databases.cc. Referenced by loadATT800(), loadBreastCancer(), loadBreastCancerWisconsin(), loadCallxx(), loadCorelDatamat(), loadDiabetes(), loadHousing(), loadIonosphere(), loadLetters(), loadPimaIndians(), loadSonar(), loadUCI(), and loadUSPS().

const char PLearn::DIGITsymbols = "0123456789"

Definition at line 50 of file TypesNumeriques.cc. Referenced by compactRepresentationTranslate(), and looksNumeric().

bool PLearn::displayvg = false [static]

Definition at line 138 of file GradientOptimizer.cc. Referenced by PLearn::GradientOptimizer::optimize().

ostream * PLearn::error_stream = &cerr

Definition at line 59 of file plerror.cc. Referenced by errormsg(), exitmsg(), and warningmsg().

real PLearn::gset [static]

Definition at line 57 of file random.cc. Referenced by gaussian_01().

const void * PLearn::Hash_DELETED_SLOT = (void *)0x00000001

Definition at line 50 of file Hash.cc. Referenced by PLearn::Hash< KeyType, DataType >::add(), PLearn::Hash< KeyType, DataType >::cleanup(), PLearn::Hash< KeyType, DataType >::del(), PLearn::Hash< KeyType, DataType >::element(), PLearn::Hash< KeyType, DataType >::find(), PLearn::Hash< KeyType, DataType >::findGap(), PLearn::Hash< KeyType, DataType >::flush(), PLearn::Hash< KeyType, DataType >::hashAddress(), PLearn::Hash< KeyType, DataType >::operator()(), PLearn::Hash< KeyType, DataType >::operator[](), and PLearn::Hash< KeyType, DataType >::resize().

const unsigned int PLearn::Hash_NOMBRES_MAGIQUES

Definition at line 53 of file Hash.cc. Referenced by PLearn::Hash< KeyType, DataType >::hashKey().

const unsigned int PLearn::Hash_UNUSED_TAG = 0xffffffffu

DataType must have new, delete and copy constructor Keys are unique. KeyType must have a (char *) type cast operator that returns a pointer to its usefull region since the key can be any object (therefore, the usefull part of the object does not necessarily starts a offset_0) const size_t byteLength() const that returns the byte-size of its usefull region, new, delete and a copy constructor ==, != Definition at line 49 of file Hash.cc. Referenced by PLearn::SimpleDB< KeyType, QueryResult >::findEqualIndexed(), PLearn::Hash< KeyType, DataType >::hashAddress(), and PLearn::SimpleDB< KeyType, QueryResult >::indexColumn().

pl_stream_initiate PLearn::initiate

Definition at line 48 of file PStream_util.cc.

int PLearn::iset = 0 [static]

Definition at line 56 of file random.cc. Referenced by gaussian_01(), and manual_seed().

double PLearn::MACHEP = 1.11022302462515654042E-16

Definition at line 706 of file random.cc. Referenced by incbcf(), and incbd().

double PLearn::MAXLOG = 7.09782712893383996732E2

Definition at line 704 of file random.cc.

double PLearn::MINLOG = -7.451332191019412076235E2

Definition at line 705 of file random.cc.

const unsigned char PLearn::MissingCharacter = (unsigned char)SCHAR_MIN

Definition at line 48 of file SimpleDB.cc. Referenced by PLearn::RowIterator::isMissing(), PLearn::FieldValue::isMissing(), PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

const PDate PLearn::MissingDate

Definition at line 54 of file SimpleDB.cc. Referenced by PLearn::RowIterator::isMissing(), PLearn::FieldValue::isMissing(), PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

const double PLearn::MissingDouble = MISSING_VALUE

Definition at line 53 of file SimpleDB.cc. Referenced by PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

const float PLearn::MissingFloat = MISSING_VALUE

Definition at line 52 of file SimpleDB.cc. Referenced by PLearn::RowIterator::setMissing().

const int PLearn::MissingInt = INT_MIN

Definition at line 51 of file SimpleDB.cc. Referenced by PLearn::RowIterator::isMissing(), PLearn::FieldValue::isMissing(), PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

const short PLearn::MissingShort = SHRT_MIN

Definition at line 50 of file SimpleDB.cc. Referenced by PLearn::RowIterator::isMissing(), PLearn::FieldValue::isMissing(), PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

const signed char PLearn::MissingSignedChar = (signed char)SCHAR_MIN

Definition at line 49 of file SimpleDB.cc. Referenced by PLearn::RowIterator::isMissing(), PLearn::FieldValue::isMissing(), PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

const char PLearn::MissingString = '\0'

A few constants for representing missing values. Definition at line 47 of file SimpleDB.cc. Referenced by PLearn::RowIterator::isMissing(), PLearn::FieldValue::isMissing(), PLearn::RowIterator::setMissing(), and PLearn::FieldValue::setMissing().

pl_nullstreambuf PLearn::null_streambuf [static]

Definition at line 51 of file PStream_util.cc.

istream PLearn::nullin

a null instream: reading from it does nothing Definition at line 82 of file PStream_util.h. Referenced by PLearn::PLMPI::init().

iostream PLearn::nullinout

a null iostream: reading/writing from/to it does nothing Definition at line 83 of file PStream_util.h.

ostream PLearn::nullout

a null ostream: writing to it does nothing Definition at line 81 of file PStream_util.h. Referenced by PLearn::PLMPI::init().

const char* PLearn::ORDINALS[] = {"d","nd","th","st",0}

Definition at line 52 of file TypesNumeriques.cc. Referenced by compactRepresentationTranslate().

const int PLearn::pl_dftbuflen = 4096

Definition at line 53 of file pl_fdstream.h. Referenced by PLearn::StdPStreamBuf::attach(), and PLearn::pl_fdstream::attach().

double PLearn::pl_gammln_cof[7] [static]

Initial value: { 1.000000000190015 , 76.18009172947146 , -86.50532032941677 , 24.01409824083091 , -1.231739572450155 , 0.1208650973866179e-2, -0.5395239384953e-5 } Definition at line 50 of file pl_erf.cc. Referenced by pl_dgammlndz(), and pl_gammln().

const size_t PLearn::PL_HASH_NOMBRES_MAGIQUES

Definition at line 42 of file pl_hash_fun.cc. Referenced by hashbytes().

PLMathInitializer PLearn::pl_math_initializer

Definition at line 73 of file pl_math.cc.

_plearn_nan_type PLearn::plearn_nan

Definition at line 72 of file pl_math.h.

pl_stream_raw PLearn::raw

Definition at line 46 of file PStream_util.cc. Referenced by PLearn::Row::begin(), PLearn::Row::bind(), PLearn::RowIterator::copyFrom(), PLearn::Row::end(), and PLearn::Row::Row().

const tRule PLearn::rules[]

Initial value: { {"#an", NT_CARDINAL + NT_PREFIXED }, {"#n", NT_CARDINAL }, {"#na", NT_CARDINAL + NT_SUFFIXED }, {"#ar", NT_RANGE + NT_PREFIXED }, {"#r", NT_RANGE }, {"#ra", NT_RANGE + NT_SUFFIXED }, {"#n'a", NT_ORDINAL + NT_SUFFIXED }, {"#ao", NT_ORDINAL + NT_PREFIXED }, {"#o", NT_ORDINAL }, {"#oa", NT_ORDINAL + NT_SUFFIXED }, {"#o'a", NT_ORDINAL + NT_SUFFIXED }, {"#$n", NT_CURRENCY }, {"#$na", NT_CURRENCY + NT_SUFFIXED }, {"#$r", NT_CURRENCY + NT_RANGE }, {"#$ra", NT_CURRENCY + NT_RANGE + NT_SUFFIXED }, {"#n:n", NT_TIME }, {"#n:n:n", NT_TIME }, {"#r:n", NT_CODE }, {"#n:r", NT_CODE }, {"", NT_UNKNOWN_NUMERIC_TYPE} } Definition at line 89 of file TypesNumeriques.h. Referenced by numericType().

const int PLearn::STORAGE_UNUSED_HANDLE = -1

Definition at line 48 of file MemoryMap.h. Referenced by PLearn::Storage< pair< real, real > >::pointTo(), PLearn::Storage< pair< real, real > >::Storage(), and PLearn::Storage< pair< real, real > >::~Storage().

float PLearn::tanhtable

Definition at line 60 of file pl_math.cc. Referenced by fasttanh(), and PLearn::PLMathInitializer::PLMathInitializer().

long PLearn::the_seed = 0 [static]

Definition at line 55 of file random.cc. Referenced by gaussian_01(), get_seed(), manual_seed(), and uniform_sample().

string PLearn::vmat_view_dataset [static]

The specification of the dataset viewed by the vmat program. Definition at line 45 of file vmatmain.h.

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