PLearn 0.1
Classes | Namespaces | Typedefs | Functions
RandomVar.h File Reference
#include <plearn/opt/Optimizer.h>
#include "SampleVariable.h"
#include <plearn/vmat/VMat.h>
Include dependency graph for RandomVar.h:
This graph shows which files directly or indirectly include this file:

Go to the source code of this file.

Classes

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...

Namespaces

namespace  PLearn
 

< for swap


Typedefs

typedef RandomVar PLearn::MatRandomVar

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::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 PLearn::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)
Var PLearn::logP (ConditionalExpression conditional_expression, bool clearMarksUponReturn, RVInstanceArray *parameters_to_learn)
Var PLearn::P (ConditionalExpression conditional_expression, bool clearMarksUponReturn)
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)
Var PLearn::Sample (ConditionalExpression conditional_expression)
void PLearn::sample (ConditionalExpression conditional_expression, Mat &samples)
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)

Detailed Description

Definition in file RandomVar.h.

 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines