PLearn 0.1
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#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>
Go to the source code of this file.
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 ¶meters_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) |