PLearn 0.1
Namespaces | Functions
RandomVar.cc File Reference
#include <plearn/var/AbsVariable.h>
#include <plearn/var/ConcatColumnsVariable.h>
#include <plearn/var/ConcatRowsVariable.h>
#include <plearn/var/DeterminantVariable.h>
#include <plearn/var/EqualVariable.h>
#include <plearn/var/ExpVariable.h>
#include <plearn/var/ExtendedVariable.h>
#include <plearn/var/LeftPseudoInverseVariable.h>
#include <plearn/var/LogSumVariable.h>
#include <plearn/var/LogVariable.h>
#include <plearn/var/MatrixSumOfVariable.h>
#include <plearn/var/PowVariable.h>
#include <plearn/var/ProductVariable.h>
#include <plearn/var/RightPseudoInverseVariable.h>
#include <plearn/var/SoftmaxVariable.h>
#include <plearn/var/SumOfVariable.h>
#include <plearn/var/SumVariable.h>
#include <plearn/base/general.h>
#include "RandomVar.h"
#include <plearn/math/plapack.h>
#include <plearn/var/Var_operators.h>
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Namespaces

namespace  PLearn
 

< for swap


Functions

RandomVar PLearn::operator* (RandomVar a, RandomVar b)
 global functions
RandomVar PLearn::operator+ (RandomVar a, RandomVar b)
RandomVar PLearn::operator- (RandomVar a, RandomVar b)
 Return a MatRandomVar that is the element-by-element difference of two RandomVar's.
RandomVar PLearn::operator/ (RandomVar a, RandomVar b)
 Return a MatRandomVar that is the element-by-element ratio of two RandomVar's.
RandomVar PLearn::exp (RandomVar x)
 exponential function applied element-by-element
RandomVar PLearn::log (RandomVar x)
 natural logarithm function applied element-by-element
RandomVar PLearn::extend (RandomVar v, real extension_value, int n_extend)
RandomVar PLearn::hconcat (const RVArray &a)
real PLearn::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 PLearn::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 PLearn::P (ConditionalExpression conditional_expression, bool clearMarksUponReturn)
Var PLearn::logP (ConditionalExpression conditional_expression, bool clearMarksUponReturn, RVInstanceArray *parameters_to_learn)
Var PLearn::ElogP (ConditionalExpression conditional_expression, RVInstanceArray &parameters_to_learn, bool clearMarksUponReturn)
RandomVar PLearn::marginalize (const RandomVar &RV, const RandomVar &hiddenRV)
Vec PLearn::sample (ConditionalExpression conditional_expression)
void PLearn::sample (ConditionalExpression conditional_expression, Mat &samples)
Var PLearn::Sample (ConditionalExpression conditional_expression)
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.
RandomVar PLearn::normal (RandomVar mean, RandomVar log_variance, real minimum_standard_deviation)
RandomVar PLearn::mixture (RVArray components, RandomVar log_weights)
RandomVar PLearn::multinomial (RandomVar log_probabilities)
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