MLPACK  1.0.8
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mlpack::regression::LogisticRegressionFunction Class Reference

The log-likelihood function for the logistic regression objective function. More...

Public Member Functions

 LogisticRegressionFunction (const arma::mat &predictors, const arma::vec &responses, const double lambda=0)
 
 LogisticRegressionFunction (const arma::mat &predictors, const arma::vec &responses, const arma::mat &initialPoint, const double lambda=0)
 
double Evaluate (const arma::mat &parameters) const
 Evaluate the logistic regression log-likelihood function with the given parameters. More...
 
double Evaluate (const arma::mat &parameters, const size_t i) const
 Evaluate the logistic regression log-likelihood function with the given parameters, but using only one data point. More...
 
const arma::mat & GetInitialPoint () const
 Return the initial point for the optimization. More...
 
void Gradient (const arma::mat &parameters, arma::mat &gradient) const
 Evaluate the gradient of the logistic regression log-likelihood function with the given parameters. More...
 
void Gradient (const arma::mat &parameters, const size_t i, arma::mat &gradient) const
 Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one point in the dataset. More...
 
const arma::mat & InitialPoint () const
 Return the initial point for the optimization. More...
 
arma::mat & InitialPoint ()
 Modify the initial point for the optimization. More...
 
const double & Lambda () const
 Return the regularization parameter (lambda). More...
 
double & Lambda ()
 Modify the regularization parameter (lambda). More...
 
size_t NumFunctions () const
 Return the number of separable functions (the number of predictor points). More...
 
const arma::mat & Predictors () const
 Return the matrix of predictors. More...
 
const arma::vec & Responses () const
 Return the vector of responses. More...
 

Private Attributes

arma::mat initialPoint
 The initial point, from which to start the optimization. More...
 
double lambda
 The regularization parameter for L2-regularization. More...
 
const arma::mat & predictors
 The matrix of data points (predictors). More...
 
const arma::vec & responses
 The vector of responses to the input data points. More...
 

Detailed Description

The log-likelihood function for the logistic regression objective function.

This is used by various mlpack optimizers to train a logistic regression model.

Definition at line 37 of file logistic_regression_function.hpp.

Constructor & Destructor Documentation

mlpack::regression::LogisticRegressionFunction::LogisticRegressionFunction ( const arma::mat &  predictors,
const arma::vec &  responses,
const double  lambda = 0 
)
mlpack::regression::LogisticRegressionFunction::LogisticRegressionFunction ( const arma::mat &  predictors,
const arma::vec &  responses,
const arma::mat &  initialPoint,
const double  lambda = 0 
)

Member Function Documentation

double mlpack::regression::LogisticRegressionFunction::Evaluate ( const arma::mat &  parameters) const

Evaluate the logistic regression log-likelihood function with the given parameters.

Note that if a point has 0 probability of being classified directly with the given parameters, then Evaluate() will return nan (this is kind of a corner case and should not happen for reasonable models).

The optimum (minimum) of this function is 0.0, and occurs when each point is classified correctly with very high probability.

Parameters
parametersVector of logistic regression parameters.
double mlpack::regression::LogisticRegressionFunction::Evaluate ( const arma::mat &  parameters,
const size_t  i 
) const

Evaluate the logistic regression log-likelihood function with the given parameters, but using only one data point.

This is useful for optimizers such as SGD, which require a separable objective function. Note that if the point has 0 probability of being classified correctly with the given parameters, then Evaluate() will return nan (this is kind of a corner case and should not happen for reasonable models).

The optimum (minimum) of this function is 0.0, and occurs when the point is classified correctly with very high probability.

Parameters
parametersVector of logistic regression parameters.
iIndex of point to use for objective function evaluation.
const arma::mat& mlpack::regression::LogisticRegressionFunction::GetInitialPoint ( ) const
inline

Return the initial point for the optimization.

Definition at line 117 of file logistic_regression_function.hpp.

References initialPoint.

void mlpack::regression::LogisticRegressionFunction::Gradient ( const arma::mat &  parameters,
arma::mat &  gradient 
) const

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters.

Parameters
parametersVector of logistic regression parameters.
gradientVector to output gradient into.
void mlpack::regression::LogisticRegressionFunction::Gradient ( const arma::mat &  parameters,
const size_t  i,
arma::mat &  gradient 
) const

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one point in the dataset.

This is useful for optimizers such as SGD, which require a separable objective function.

Parameters
parametersVector of logistic regression parameters.
iIndex of points to use for objective function gradient evaluation.
gradientVector to output gradient into.
const arma::mat& mlpack::regression::LogisticRegressionFunction::InitialPoint ( ) const
inline

Return the initial point for the optimization.

Definition at line 50 of file logistic_regression_function.hpp.

References initialPoint.

arma::mat& mlpack::regression::LogisticRegressionFunction::InitialPoint ( )
inline

Modify the initial point for the optimization.

Definition at line 52 of file logistic_regression_function.hpp.

References initialPoint.

const double& mlpack::regression::LogisticRegressionFunction::Lambda ( ) const
inline

Return the regularization parameter (lambda).

Definition at line 55 of file logistic_regression_function.hpp.

References lambda.

double& mlpack::regression::LogisticRegressionFunction::Lambda ( )
inline

Modify the regularization parameter (lambda).

Definition at line 57 of file logistic_regression_function.hpp.

References lambda.

size_t mlpack::regression::LogisticRegressionFunction::NumFunctions ( ) const
inline

Return the number of separable functions (the number of predictor points).

Definition at line 120 of file logistic_regression_function.hpp.

const arma::mat& mlpack::regression::LogisticRegressionFunction::Predictors ( ) const
inline

Return the matrix of predictors.

Definition at line 60 of file logistic_regression_function.hpp.

References predictors.

const arma::vec& mlpack::regression::LogisticRegressionFunction::Responses ( ) const
inline

Return the vector of responses.

Definition at line 62 of file logistic_regression_function.hpp.

References responses.

Member Data Documentation

arma::mat mlpack::regression::LogisticRegressionFunction::initialPoint
private

The initial point, from which to start the optimization.

Definition at line 124 of file logistic_regression_function.hpp.

Referenced by GetInitialPoint(), and InitialPoint().

double mlpack::regression::LogisticRegressionFunction::lambda
private

The regularization parameter for L2-regularization.

Definition at line 130 of file logistic_regression_function.hpp.

Referenced by Lambda().

const arma::mat& mlpack::regression::LogisticRegressionFunction::predictors
private

The matrix of data points (predictors).

Definition at line 126 of file logistic_regression_function.hpp.

Referenced by Predictors().

const arma::vec& mlpack::regression::LogisticRegressionFunction::responses
private

The vector of responses to the input data points.

Definition at line 128 of file logistic_regression_function.hpp.

Referenced by Responses().


The documentation for this class was generated from the following file: