PLearn::ConditionalDensityNet Class Reference

#include <ConditionalDensityNet.h>

Inheritance diagram for PLearn::ConditionalDensityNet:

[legend]Collaboration diagram for PLearn::ConditionalDensityNet:

[legend]List of all members.


Public Member Functions
	ConditionalDensityNet ()
virtual void	build ()
	simply calls inherited::build() then build_()
virtual void	makeDeepCopyFromShallowCopy (map< const void , void > &copies)
	Transforms a shallow copy into a deep copy.
	PLEARN_DECLARE_OBJECT (ConditionalDensityNet)
virtual void	setInput (const Vec &input) const
	Set the value for the input part of a conditional probability.
virtual real	log_density (const Vec &x) const
	return log of probability density log(p(x))
virtual real	survival_fn (const Vec &x) const
	return survival fn = P(X>x)
virtual real	cdf (const Vec &x) const
	return survival fn = P(X<=x)
virtual void	expectation (Vec &mu) const
	return E[X]
virtual void	variance (Mat &cov) const
	return Var[X]
virtual void	resetGenerator (long g_seed) const
	Resets the random number generator used by generate using the given seed.
virtual void	generate (Vec &x) const
	return a pseudo-random sample generated from the distribution.
virtual void	forget ()
	Remove this method, if your distribution does not implement it.
void	initializeParams ()
void	initialize_mu (Vec &mu_)
virtual void	train ()
	Remove this method, if your distribution does not implement it.
virtual TVec< string >	getTrainCostNames () const
	Return [ ].
Public Attributes
Vec	paramsvalues
VarArray	y_values
Var	mu
Func	f
Func	test_costf
Func	output_and_target_to_cost
Func	cdf_f
Func	mean_f
Func	density_f
Func	in2distr_f
VarArray	output_and_target
Vec	output_and_target_values
Var	totalcost
Var	mass_cost
Var	pos_y_cost
int	nhidden
int	nhidden2
real	weight_decay
real	bias_decay
real	layer1_weight_decay
real	layer1_bias_decay
real	layer2_weight_decay
real	layer2_bias_decay
real	output_layer_weight_decay
real	output_layer_bias_decay
real	direct_in_to_out_weight_decay
bool	L1_penalty
bool	direct_in_to_out
PP< Optimizer >	optimizer
int	batch_size
real	c_penalization
real	maxY
real	thresholdY
real	log_likelihood_vs_squared_error_balance
bool	separate_mass_point
int	n_output_density_terms
real	generate_precision
string	steps_type
string	centers_initialization
string	curve_positions
real	scale
real	unconditional_p0
bool	mu_is_fixed
real	initial_hardness
Static Protected Member Functions
void	declareOptions (OptionList &ol)
	Declares this class' options.
Protected Attributes
Var	input
Var	target
Var	sampleweight
Var	w1
Var	w2
Var	wout
Var	wdirect
Var	output
Var	outputs
Var	a
Var	pos_a
Var	b
Var	pos_b
Var	c
Var	pos_c
Var	density
Var	cumulative
Var	expected_value
VarArray	costs
VarArray	penalties
Var	training_cost
Var	test_costs
VarArray	invars
VarArray	params
Var	centers
Var	centers_M
Var	steps
Var	steps_M
Var	steps_0
Var	steps_gradient
Var	steps_integral
Var	delta_steps
Var	cum_numerator
Var	cum_denominator
Vec	unconditional_cdf
Var	unconditional_delta_cdf
Var	initial_hardnesses
Var	prev_centers
Var	prev_centers_M
Var	scaled_prev_centers
Var	scaled_prev_centers_M
Var	minus_prev_centers_0
Var	minus_scaled_prev_centers_0
Private Types
typedef PDistribution	inherited
Private Member Functions
void	build_ ()
	This does the actual building.

Member Typedef Documentation

typedef PDistribution PLearn::ConditionalDensityNet::inherited [private]

Reimplemented from PLearn::PDistribution.
Definition at line 58 of file ConditionalDensityNet.h.

Constructor & Destructor Documentation

PLearn::ConditionalDensityNet::ConditionalDensityNet ( )

Definition at line 72 of file ConditionalDensityNet.cc.

Member Function Documentation

void PLearn::ConditionalDensityNet::build ( ) [virtual]

simply calls inherited::build() then build_()

Reimplemented from PLearn::PDistribution.
Definition at line 738 of file ConditionalDensityNet.cc.
References build_().
Referenced by train().

void PLearn::ConditionalDensityNet::build_ ( ) [private]

This does the actual building.

Reimplemented from PLearn::PDistribution.
Definition at line 296 of file ConditionalDensityNet.cc.
References a, PLearn::affine_transform(), PLearn::affine_transform_weight_penalty(), PLearn::TVec< Var >::append(), b, bias_decay, c, c_penalization, cdf_f, centers, centers_M, costs, cum_denominator, cum_numerator, cumulative, curve_positions, PLearn::d_soft_slope(), delta_steps, density, density_f, direct_in_to_out, direct_in_to_out_weight_decay, PLearn::dot(), PLearn::exp(), expected_value, f, PLearn::hconcat(), PLearn::ifThenElse(), in2distr_f, initial_hardness, initial_hardnesses, initialize_mu(), initializeParams(), input, invars, PLearn::isAboveThreshold(), L1_penalty, layer1_bias_decay, layer1_weight_decay, layer2_bias_decay, layer2_weight_decay, PLearn::Var::length(), PLearn::log(), log_likelihood_vs_squared_error_balance, PLearn::VarArray::makeSharedValue(), mass_cost, maxY, mean_f, mu, mu_is_fixed, n_output_density_terms, PLearn::VarArray::nelems(), nhidden, nhidden2, output, output_and_target, output_and_target_values, output_layer_bias_decay, output_layer_weight_decay, outputs, params, paramsvalues, penalties, PLERROR, pos_a, pos_b, pos_c, pos_y_cost, PLearn::positive(), PLearn::TVec< Var >::push_back(), PLearn::TVec< T >::resize(), PLearn::TVec< Var >::resize(), sampleweight, scale, separate_mass_point, PLearn::sigmoid(), PLearn::TVec< T >::size(), PLearn::TVec< Var >::size(), PLearn::soft_slope(), PLearn::soft_slope_integral(), PLearn::soft_slope_limit(), PLearn::softplus(), PLearn::square(), steps, steps_0, steps_gradient, steps_integral, steps_M, steps_type, PLearn::sum(), PLearn::sumabs(), PLearn::sumsquare(), PLearn::tanh(), target, test_costf, test_costs, thresholdY, PLearn::tostring(), training_cost, PLearn::transpose(), PLearn::transposeProduct(), unconditional_cdf, unconditional_delta_cdf, PLearn::var(), PLearn::vconcat(), w1, w2, wdirect, weight_decay, wout, and y_values.
Referenced by build().

real PLearn::ConditionalDensityNet::cdf ( const Vec & x ) const [virtual]

return survival fn = P(X<=x)

Reimplemented from PLearn::PDistribution.
Definition at line 834 of file ConditionalDensityNet.cc.
References cdf_f, output_and_target_values, PLERROR, and target.
Referenced by generate(), survival_fn(), and train().

void PLearn::ConditionalDensityNet::declareOptions ( OptionList & ol ) [static, protected]

Declares this class' options.

Reimplemented from PLearn::PDistribution.
Definition at line 154 of file ConditionalDensityNet.cc.
References PLearn::declareOption(), and PLearn::OptionList.

void PLearn::ConditionalDensityNet::expectation ( Vec & mu ) const [virtual]

return E[X]

Reimplemented from PLearn::PDistribution.
Definition at line 846 of file ConditionalDensityNet.cc.
References mean_f, output, and PLearn::TVec< T >::resize().

void PLearn::ConditionalDensityNet::forget ( ) [virtual]

Remove this method, if your distribution does not implement it.

Reimplemented from PLearn::PDistribution.
Definition at line 986 of file ConditionalDensityNet.cc.
References initializeParams().

void PLearn::ConditionalDensityNet::generate ( Vec & x ) const [virtual]

return a pseudo-random sample generated from the distribution.

Reimplemented from PLearn::PDistribution.
Definition at line 862 of file ConditionalDensityNet.cc.
References cdf(), generate_precision, maxY, pos_a, PLearn::TVec< T >::resize(), and PLearn::uniform_sample().

TVec< string > PLearn::ConditionalDensityNet::getTrainCostNames ( ) const [virtual]

Return [ ].

Reimplemented from PLearn::PDistribution.
Definition at line 712 of file ConditionalDensityNet.cc.
References PLearn::PDistribution::getTestCostNames(), penalties, and PLearn::TVec< Var >::size().

void PLearn::ConditionalDensityNet::initialize_mu ( Vec & mu_ )

Definition at line 967 of file ConditionalDensityNet.cc.
References PLearn::center(), centers_initialization, PLearn::exp(), maxY, n_output_density_terms, PLERROR, and scale.
Referenced by build_(), initializeParams(), and train().

void PLearn::ConditionalDensityNet::initializeParams ( )

Definition at line 897 of file ConditionalDensityNet.cc.
References centers_initialization, PLearn::Var::column(), direct_in_to_out, PLearn::TVec< T >::fill(), PLearn::fill_random_normal(), initialize_mu(), PLearn::inverse_sigmoid(), PLearn::inverse_softplus(), PLearn::manual_seed(), mu, mu_is_fixed, n_output_density_terms, nhidden, nhidden2, optimizer, PLearn::seed(), separate_mass_point, PLearn::TVec< T >::subVec(), unconditional_delta_cdf, unconditional_p0, w1, w2, wdirect, and wout.
Referenced by build_(), and forget().

real PLearn::ConditionalDensityNet::log_density ( const Vec & x ) const [virtual]

return log of probability density log(p(x))

Reimplemented from PLearn::PDistribution.
Definition at line 819 of file ConditionalDensityNet.cc.
References density_f, PLearn::log(), output_and_target_values, PLearn::TVec< T >::resize(), and target.

void PLearn::ConditionalDensityNet::makeDeepCopyFromShallowCopy ( map< const void *, void * > & copies ) [virtual]

Transforms a shallow copy into a deep copy.

Reimplemented from PLearn::PDistribution.
Definition at line 746 of file ConditionalDensityNet.cc.
References a, b, c, cdf_f, centers, centers_M, costs, cum_denominator, cum_numerator, cumulative, PLearn::deepCopyField(), delta_steps, density, density_f, expected_value, f, in2distr_f, initial_hardnesses, input, invars, mass_cost, mean_f, minus_prev_centers_0, minus_scaled_prev_centers_0, mu, optimizer, output, output_and_target, output_and_target_to_cost, output_and_target_values, outputs, params, paramsvalues, penalties, pos_a, pos_b, pos_c, pos_y_cost, prev_centers, prev_centers_M, sampleweight, scaled_prev_centers, scaled_prev_centers_M, steps, steps_0, steps_gradient, steps_integral, steps_M, target, test_costf, test_costs, totalcost, training_cost, unconditional_cdf, unconditional_delta_cdf, PLearn::varDeepCopyField(), w1, w2, wdirect, wout, and y_values.

PLearn::ConditionalDensityNet::PLEARN_DECLARE_OBJECT ( ConditionalDensityNet )

void PLearn::ConditionalDensityNet::resetGenerator ( long g_seed ) const [virtual]

Resets the random number generator used by generate using the given seed.

Reimplemented from PLearn::PDistribution.
Definition at line 857 of file ConditionalDensityNet.cc.
References PLearn::manual_seed().

void PLearn::ConditionalDensityNet::setInput ( const Vec & input ) const [virtual]

Set the value for the input part of a conditional probability.

Reimplemented from PLearn::PDistribution.
Definition at line 810 of file ConditionalDensityNet.cc.
References f, in2distr_f, PLERROR, and pos_a.

real PLearn::ConditionalDensityNet::survival_fn ( const Vec & x ) const [virtual]

return survival fn = P(X>x)

Reimplemented from PLearn::PDistribution.
Definition at line 828 of file ConditionalDensityNet.cc.
References cdf().

void PLearn::ConditionalDensityNet::train ( ) [virtual]

Remove this method, if your distribution does not implement it.
You may remove this method if your distribution does not implement it
Reimplemented from PLearn::PDistribution.
Definition at line 993 of file ConditionalDensityNet.cc.
References batch_size, PLearn::StatsCollector::build(), build(), PLearn::StatsCollector::cdf(), cdf(), centers_initialization, PLearn::TVec< T >::clear(), PLearn::displayFunction(), PLearn::endl(), f, PLearn::TVec< T >::fill(), PLearn::StatsCollector::forget(), PLearn::VMat::getExample(), initial_hardness, initialize_mu(), input, PLearn::PLearner::inputsize(), invars, PLearn::inverse_sigmoid(), PLearn::inverse_softplus(), PLearn::PP< Function >::isNull(), k, PLearn::TMat< T >::length(), PLearn::VMat::length(), PLearn::StatsCollector::maxnvalues, maxY, PLearn::StatsCollector::mean(), PLearn::meanOf(), PLearn::min(), mu, mu_is_fixed, n_output_density_terms, optimizer, outputs, params, PLERROR, separate_mass_point, PLearn::sigmoid(), PLearn::soft_slope(), steps_type, PLearn::TVec< T >::subVec(), target, PLearn::PLearner::targetsize(), test_costf, PLearn::tostring(), totalcost, training_cost, unconditional_cdf, unconditional_delta_cdf, unconditional_p0, PLearn::ProgressBar::update(), PLearn::StatsCollector::update(), and wout.

void PLearn::ConditionalDensityNet::variance ( Mat & cov ) const [virtual]

return Var[X]

Reimplemented from PLearn::PDistribution.
Definition at line 852 of file ConditionalDensityNet.cc.
References PLearn::Mat, and PLERROR.