mlpack::regression::LogisticRegressionFunction< MatType > Class Template Reference

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

#include <logistic_regression_function.hpp>

Public Member Functions
	LogisticRegressionFunction (const MatType &predictors, const arma::Row< size_t > &responses, const double lambda=0)
	Creates the LogisticRegressionFunction. More...
const double &	Lambda () const
	Return the regularization parameter (lambda).
double &	Lambda ()
	Modify the regularization parameter (lambda).
const MatType &	Predictors () const
	Return the matrix of predictors.
const arma::Row< size_t > &	Responses () const
	Return the vector of responses.
void	Shuffle ()
	Shuffle the order of function visitation. More...
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 begin, const size_t batchSize=1) const
	Evaluate the logistic regression log-likelihood function with the given parameters using the given batch size from the given point index. 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...
template<typename GradType >
void	Gradient (const arma::mat &parameters, const size_t begin, GradType &gradient, const size_t batchSize=1) const
	Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, for the given batch size from a given point in the dataset. More...
void	PartialGradient (const arma::mat &parameters, const size_t j, arma::sp_mat &gradient) const
	Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one feature in the dataset. More...
template<typename GradType >
double	EvaluateWithGradient (const arma::mat &parameters, GradType &gradient) const
	Evaluate the objective function and gradient of the logistic regression log-likelihood function simultaneously with the given parameters.
template<typename GradType >
double	EvaluateWithGradient (const arma::mat &parameters, const size_t begin, GradType &gradient, const size_t batchSize=1) const
	Evaluate the objective function and gradient of the logistic regression log-likelihood function simultaneously with the given parameters, for the given batch size from a given point in the dataset.
size_t	NumFunctions () const
	Return the number of separable functions (the number of predictor points).
size_t	NumFeatures () const
	Return the number of features(add 1 for the intercept term).

Detailed Description

template<typename MatType = arma::mat>
class mlpack::regression::LogisticRegressionFunction< MatType >

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

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

Constructor & Destructor Documentation

LogisticRegressionFunction()

template<typename MatType >

mlpack::regression::LogisticRegressionFunction< MatType >::LogisticRegressionFunction	(	const MatType &	predictors,
		const arma::Row< size_t > &	responses,
		const double	lambda = 0
	)

Creates the LogisticRegressionFunction.

Parameters

predictors	The matrix of data points.
responses	The measured data for each point in predictors.
lambda	Regularization constant for ridge regression.

Member Function Documentation

Evaluate() [1/2]

template<typename MatType >

double mlpack::regression::LogisticRegressionFunction< MatType >::Evaluate

(

const arma::mat &

parameters

)

const

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

Evaluate the logistic regression objective function given the estimated 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

parameters

Vector of logistic regression parameters.

Evaluate() [2/2]

template<typename MatType >

double mlpack::regression::LogisticRegressionFunction< MatType >::Evaluate	(	const arma::mat &	parameters,
		const size_t	begin,
		const size_t	batchSize = 1
	)		const

Evaluate the logistic regression log-likelihood function with the given parameters using the given batch size from the given point index.

Evaluate the logistic regression objective function given the estimated parameters for a given batch from a given point.

This is useful for optimizers such as SGD, which require a separable objective function. Note that if the points have 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 points are classified correctly with very high probability.

Parameters

parameters	Vector of logistic regression parameters.
begin	Index of the starting point to use for objective function evaluation.
batchSize	Number of points to be passed at a time to use for objective function evaluation.

Gradient() [1/2]

template<typename MatType >

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

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

Evaluate the gradient of the logistic regression objective function.

Parameters

parameters	Vector of logistic regression parameters.
gradient	Vector to output gradient into.

Gradient() [2/2]

template<typename MatType >

template<typename GradType >

void mlpack::regression::LogisticRegressionFunction< MatType >::Gradient	(	const arma::mat &	parameters,
		const size_t	begin,
		GradType &	gradient,
		const size_t	batchSize = 1
	)		const

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, for the given batch size from a given point in the dataset.

Evaluate the gradient of the logistic regression objective function for a given batch size.

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

Parameters

parameters	Vector of logistic regression parameters.
begin	Index of the starting point to use for objective function gradient evaluation.
gradient	Vector to output gradient into.
batchSize	Number of points to be processed as a batch for objective function gradient evaluation.

PartialGradient()

template<typename MatType >

void mlpack::regression::LogisticRegressionFunction< MatType >::PartialGradient	(	const arma::mat &	parameters,
		const size_t	j,
		arma::sp_mat &	gradient
	)		const

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

Evaluate the partial gradient of the logistic regression objective function with respect to the individual features in the parameter.

This is useful for optimizers such as SCD, which require partial gradients.

Parameters

parameters	Vector of logistic regression parameters.
j	Index of the feature with respect to which the gradient is to be computed.
gradient	Sparse matrix to output gradient into.

Shuffle()

template<typename MatType >

void mlpack::regression::LogisticRegressionFunction< MatType >::Shuffle

(

)

Shuffle the order of function visitation.

Shuffle the datapoints.

This may be called by the optimizer.

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

• src/mlpack/methods/logistic_regression/logistic_regression_function.hpp
• src/mlpack/methods/logistic_regression/logistic_regression_function_impl.hpp