PLearn 0.1
|
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 ¶meters_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) |
Definition in file RandomVar.h.