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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) |
1.7.4