| _classname_() | PLearn::Object | [static] |
| _getOptionList_() | PLearn::Object | [static] |
| _isa_(Object *o) | PLearn::Object | [static] |
| _new_instance_for_typemap_() | PLearn::Object | [static] |
| _static_initialize_() | PLearn::Object | [static] |
| _static_initializer_ | PLearn::Object | [static] |
| addDataForKernelMatrix(const Vec &newRow) | PLearn::Kernel | [virtual] |
| apply(VMat m1, VMat m2, Mat &result) const | PLearn::Kernel | |
| apply(VMat m1, VMat m2) const | PLearn::Kernel | |
| apply(VMat m, const Vec &x, Vec &result) const | PLearn::Kernel | |
| apply(Vec x, VMat m, Vec &result) const | PLearn::Kernel | |
| build() | PLearn::Kernel | [virtual] |
| call(const string &methodname, int nargs, PStream &in_parameters, PStream &out_results) | PLearn::Object | [virtual] |
| changeOption(const string &optionname, const string &value) | PLearn::Object | |
| changeOptions(const map< string, string > &name_value) | PLearn::Object | [virtual] |
| classname() const | PLearn::Object | [virtual] |
| computeGramMatrix(Mat K) const | PLearn::Kernel | [virtual] |
| computeKNNeighbourMatrixFromDistanceMatrix(const Mat &D, int knn, bool insure_self_first_neighbour=true, bool report_progress=false) | PLearn::Kernel | [static] |
| computeNearestNeighbors(const Vec &x, Mat &k_xi_x_sorted, int knn) const | PLearn::Kernel | |
| computeNeighbourMatrixFromDistanceMatrix(const Mat &D, bool insure_self_first_neighbour=true, bool report_progress=false) | PLearn::Kernel | [static] |
| data | PLearn::Kernel | [protected] |
| data_inputsize | PLearn::Kernel | [protected] |
| dataInputsize() | PLearn::Kernel | [inline, virtual] |
| declareOptions(OptionList &ol) | PLearn::LogOfGaussianDensityKernel | [protected, static] |
| deepCopy(CopiesMap &copies) const | PLearn::Object | [virtual] |
| estimateHistograms(VMat d, real sameness_threshold, real minval, real maxval, int nbins) const | PLearn::Kernel | |
| estimateHistograms(Mat input_and_class, real minval, real maxval, int nbins) const | PLearn::Kernel | |
| evaluate(const Vec &x1, const Vec &x2) const | PLearn::LogOfGaussianDensityKernel | [virtual] |
| evaluate_all_i_x(const Vec &x, Vec &k_xi_x, real squared_norm_of_x=-1, int istart=0) const | PLearn::Kernel | |
| evaluate_all_x_i(const Vec &x, Vec &k_x_xi, real squared_norm_of_x=-1, int istart=0) const | PLearn::Kernel | |
| evaluate_i_j(int i, int j) const | PLearn::Kernel | [virtual] |
| evaluate_i_x(int i, const Vec &x, real squared_norm_of_x=-1) const | PLearn::Kernel | [virtual] |
| evaluate_i_x_again(int i, const Vec &x, real squared_norm_of_x=-1, bool first_time=false) const | PLearn::Kernel | [virtual] |
| evaluate_x_i(const Vec &x, int i, real squared_norm_of_x=-1) const | PLearn::Kernel | [virtual] |
| evaluate_x_i_again(const Vec &x, int i, real squared_norm_of_x=-1, bool first_time=false) const | PLearn::Kernel | [virtual] |
| getData() | PLearn::Kernel | [inline] |
| getOption(const string &optionname) const | PLearn::Object | |
| getOptionList() const | PLearn::Object | [virtual] |
| getOptionsToSave() const | PLearn::Object | [virtual] |
| getParameters() const | PLearn::Kernel | [virtual] |
| hasData() | PLearn::Kernel | |
| info() const | PLearn::Object | [virtual] |
| inherited typedef | PLearn::LogOfGaussianDensityKernel | [private] |
| is_symmetric | PLearn::Kernel | |
| isInData(const Vec &x, int *i=0) const | PLearn::Kernel | |
| Kernel(bool is__symmetric=true) | PLearn::Kernel | |
| load(const string &filename) | PLearn::Object | [virtual] |
| LogOfGaussianDensityKernel() | PLearn::LogOfGaussianDensityKernel | [inline] |
| LogOfGaussianDensityKernel(real the_sigma) | PLearn::LogOfGaussianDensityKernel | [inline] |
| makeDeepCopyFromShallowCopy(CopiesMap &copies) | PLearn::Kernel | [virtual] |
| n_examples | PLearn::Kernel | [protected] |
| newread(PStream &in) | PLearn::Object | |
| newwrite(PStream &out) const | PLearn::Object | |
| nExamples() | PLearn::Kernel | [inline, virtual] |
| Object() | PLearn::Object | |
| oldread(istream &in) | PLearn::Object | [virtual] |
| operator()(const Vec &x1, const Vec &x2) const | PLearn::Kernel | [inline] |
| PLEARN_DECLARE_ABSTRACT_OBJECT(Kernel) | PLearn::Kernel | |
| PLEARN_DECLARE_OBJECT(LogOfGaussianDensityKernel) | PLearn::LogOfGaussianDensityKernel | |
| PPointable() | PLearn::PPointable | [inline] |
| PPointable(const PPointable &other) | PLearn::PPointable | [inline] |
| prepareToSendResults(PStream &out, int nres) | PLearn::Object | [inline, protected] |
| print(ostream &out) const | PLearn::Object | [virtual] |
| read(istream &in) | PLearn::Object | [virtual] |
| readOptionVal(PStream &in, const string &optionname) | PLearn::Object | |
| ref() const | PLearn::PPointable | [inline] |
| report_progress | PLearn::Kernel | |
| run() | PLearn::Object | [virtual] |
| save(const string &filename) const | PLearn::Object | [virtual] |
| setDataForKernelMatrix(VMat the_data) | PLearn::Kernel | [virtual] |
| setOption(const string &optionname, const string &value) | PLearn::Object | |
| setParameters(Vec paramvec) | PLearn::Kernel | [virtual] |
| sigma | PLearn::LogOfGaussianDensityKernel | [protected] |
| specify_dataset | PLearn::Kernel | |
| test(VMat d, real threshold, real sameness_below_threshold, real sameness_above_threshold) const | PLearn::Kernel | |
| unref() const | PLearn::PPointable | [inline] |
| usage() const | PLearn::PPointable | [inline] |
| write(ostream &out) const | PLearn::Object | [virtual] |
| writeOptionVal(PStream &out, const string &optionname) const | PLearn::Object | |
| ~Kernel() | PLearn::Kernel | [virtual] |
| ~Object() | PLearn::Object | [virtual] |
| ~PPointable() | PLearn::PPointable | [inline, virtual] |
