Public Member Functions
	SoftmaxRegressionFunction (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses, const double lambda=0.0001, const bool fitIntercept=false)
	Construct the Softmax Regression objective function with the given parameters. More...

double	Evaluate (const arma::mat &parameters) const
	Evaluates the objective function of the softmax regression model using the given parameters. More...

size_t	FeatureSize () const
	Gets the features size of the training data. More...

bool	FitIntercept () const
	Gets the intercept flag. More...

void	GetGroundTruthMatrix (const arma::Row< size_t > &labels, arma::sp_mat &groundTruth)
	Constructs the ground truth label matrix with the passed labels. More...

const arma::mat &	GetInitialPoint () const
	Return the initial point for the optimization. More...

void	GetProbabilitiesMatrix (const arma::mat &parameters, arma::mat &probabilities) const
	Evaluate the probabilities matrix with the passed parameters. More...

void	Gradient (const arma::mat &parameters, arma::mat &gradient) const
	Evaluates the gradient values of the objective function given the current set of parameters. More...

const arma::mat	InitializeWeights ()
	Initializes the parameters of the model to suitable values. More...

double &	Lambda ()
	Sets the regularization parameter. More...

double	Lambda () const
	Gets the regularization parameter. More...

size_t	NumClasses () const
	Gets the number of classes. More...

Static Public Member Functions
static const arma::mat	InitializeWeights (const size_t featureSize, const size_t numClasses, const bool fitIntercept=false)
	Initialize Softmax Regression weights (trainable parameters) with the given parameters. More...

static void	InitializeWeights (arma::mat &weights, const size_t featureSize, const size_t numClasses, const bool fitIntercept=false)
	Initialize Softmax Regression weights (trainable parameters) with the given parameters. More...

Private Attributes
const arma::mat &	data
	Training data matrix. More...

bool	fitIntercept
	Intercept term flag. More...

arma::sp_mat	groundTruth
	Label matrix for the provided data. More...

arma::mat	initialPoint
	Initial parameter point. More...

double	lambda
	L2-regularization constant. More...

size_t	numClasses
	Number of classes. More...

Detailed Description

Definition at line 21 of file softmax_regression_function.hpp.

Constructor & Destructor Documentation

mlpack::regression::SoftmaxRegressionFunction::SoftmaxRegressionFunction	(	const arma::mat &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const double	lambda = `0.0001`,
		const bool	fitIntercept = `false`
	)

Construct the Softmax Regression objective function with the given parameters.

Parameters

data	Input training data, each column associate with one sample
labels	Labels associated with the feature data.
inputSize	Size of the input feature vector.
numClasses	Number of classes for classification.
lambda	L2-regularization constant.
fitIntercept	Intercept term flag.

Member Function Documentation

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

Evaluates the objective function of the softmax regression model using the given parameters.

The cost function has terms for the log likelihood error and the regularization cost. The objective function takes a low value when the model generalizes well for the given training data, while having small parameter values.

Parameters

parameters Current values of the model parameters.

size_t mlpack::regression::SoftmaxRegressionFunction::FeatureSize ( ) const

inline

Gets the features size of the training data.

Definition at line 121 of file softmax_regression_function.hpp.

References fitIntercept, and initialPoint.

bool mlpack::regression::SoftmaxRegressionFunction::FitIntercept ( ) const

inline

Gets the intercept flag.

Definition at line 133 of file softmax_regression_function.hpp.

References fitIntercept.

void mlpack::regression::SoftmaxRegressionFunction::GetGroundTruthMatrix	(	const arma::Row< size_t > &	labels,
		arma::sp_mat &	groundTruth
	)

Constructs the ground truth label matrix with the passed labels.

Parameters

labels	Labels associated with the training data.
groundTruth	Pointer to arma::mat which stores the computed matrix.

const arma::mat& mlpack::regression::SoftmaxRegressionFunction::GetInitialPoint ( ) const

inline

Return the initial point for the optimization.

Definition at line 115 of file softmax_regression_function.hpp.

References initialPoint.

void mlpack::regression::SoftmaxRegressionFunction::GetProbabilitiesMatrix	(	const arma::mat &	parameters,
		arma::mat &	probabilities
	)		const

Evaluate the probabilities matrix with the passed parameters.

probabilities(i, j) = exp( * data_j) / sum_k(exp( * data_j)). It represents the probability of data_j belongs to class i.

Parameters

parameters	Current values of the model parameters.
probabilities	Pointer to arma::mat which stores the probabilities.

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

Evaluates the gradient values of the objective function given the current set of parameters.

The function calculates the probabilities for each class given the parameters, and computes the gradients based on the difference from the ground truth.

Parameters

parameters	Current values of the model parameters.
gradient	Matrix where gradient values will be stored.

const arma::mat mlpack::regression::SoftmaxRegressionFunction::InitializeWeights ( )

Initializes the parameters of the model to suitable values.

static const arma::mat mlpack::regression::SoftmaxRegressionFunction::InitializeWeights	(	const size_t	featureSize,
		const size_t	numClasses,
		const bool	fitIntercept = `false`
	)

static

Initialize Softmax Regression weights (trainable parameters) with the given parameters.

Parameters

featureSize	The number of features in the training set.
numClasses	Number of classes for classification.
fitIntercept	If true, an intercept is fitted.

Returns: Initialized model weights.

static void mlpack::regression::SoftmaxRegressionFunction::InitializeWeights	(	arma::mat &	weights,
		const size_t	featureSize,
		const size_t	numClasses,
		const bool	fitIntercept = `false`
	)

static

Initialize Softmax Regression weights (trainable parameters) with the given parameters.

Parameters

weights	This will be filled with the initialized model weights.
featureSize	The number of features in the training set.
numClasses	Number of classes for classification.
fitIntercept	Intercept term flag.