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...

double	Evaluate (const arma::mat &parameters, const size_t start, const size_t batchSize=1) const
	Evaluate the objective function of the softmax regression model for a subset of the data points using the given parameters. 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 size_t start, const size_t batchSize) 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...

void	Gradient (const arma::mat &parameters, const size_t start, arma::mat &gradient, const size_t batchSize=1) const
	Evaluate the gradient of the objective function given the current set of parameters, on a subset of the data. 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...

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

size_t	NumFunctions () const
	Return the number of separable functions (the number of predictor points). More...

void	PartialGradient (const arma::mat &parameters, size_t j, arma::sp_mat &gradient) const
	Evaluates the gradient values of the objective function given the current set of parameters for a single feature indexed by j. More...

void	Shuffle ()
	Shuffle the dataset. 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...

Detailed Description

Definition at line 21 of file softmax_regression_function.hpp.

Constructor & Destructor Documentation

◆ 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.
numClasses	Number of classes for classification.
lambda	L2-regularization constant.
fitIntercept	Intercept term flag.

Member Function Documentation

◆ Evaluate() [1/2]

double 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.

◆ Evaluate() [2/2]

double Evaluate	(	const arma::mat &	parameters,
		const size_t	start,
		const size_t	batchSize = `1`
	)		const

Evaluate the objective function of the softmax regression model for a subset of the data points 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.
start	First index of the data points to use.
batchSize	Number of data points to evaluate objective for.

◆ FitIntercept()

bool FitIntercept ( ) const

inline

Gets the intercept flag.

Definition at line 188 of file softmax_regression_function.hpp.

◆ GetGroundTruthMatrix()

void 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.

◆ GetInitialPoint()

const arma::mat& GetInitialPoint ( ) const

inline

Return the initial point for the optimization.

Definition at line 167 of file softmax_regression_function.hpp.

◆ GetProbabilitiesMatrix()

void GetProbabilitiesMatrix	(	const arma::mat &	parameters,
		arma::mat &	probabilities,
		const size_t	start,
		const size_t	batchSize
	)		const

Evaluate the probabilities matrix with the passed parameters.

probabilities(i, j) = $exp(\theta_i * data_j) / sum_k(exp(\theta_k * data_j))$ . 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.
start	Index of point to start at.
batchSize	Number of points to calculate probabilities for.

◆ Gradient() [1/2]

void 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.

◆ Gradient() [2/2]

void Gradient	(	const arma::mat &	parameters,
		const size_t	start,
		arma::mat &	gradient,
		const size_t	batchSize = `1`
	)		const

Evaluate the gradient of the objective function given the current set of parameters, on a subset of the data.

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.
start	First index of the data points to use.
gradient	Matrix to store gradient into.
batchSize	Number of data points to evaluate gradient for.

◆ InitializeWeights() [1/3]

const arma::mat InitializeWeights ( )

Initializes the parameters of the model to suitable values.

◆ InitializeWeights() [2/3]

static const arma::mat 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.

◆ InitializeWeights() [3/3]

static void 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.

◆ Lambda() [1/2]

double& Lambda ( )

inline

Sets the regularization parameter.

Definition at line 183 of file softmax_regression_function.hpp.

◆ Lambda() [2/2]

double Lambda ( ) const

inline

Gets the regularization parameter.

Definition at line 185 of file softmax_regression_function.hpp.

◆ NumClasses()

size_t NumClasses ( ) const

inline

Gets the number of classes.

Definition at line 170 of file softmax_regression_function.hpp.

◆ NumFeatures()

size_t NumFeatures ( ) const

inline

Gets the features size of the training data.

Definition at line 173 of file softmax_regression_function.hpp.

◆ NumFunctions()

size_t NumFunctions ( ) const

inline

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

Definition at line 180 of file softmax_regression_function.hpp.

◆ PartialGradient()

void PartialGradient	(	const arma::mat &	parameters,
		size_t	j,
		arma::sp_mat &	gradient
	)		const

Evaluates the gradient values of the objective function given the current set of parameters for a single feature indexed by j.

Parameters

parameters	Current values of the model parameters.
j	The index of the feature with respect to which the partial gradient is to be computed.
gradient	Out param for the gradient value.

◆ Shuffle()

void Shuffle ( )

Shuffle the dataset.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/softmax_regression/softmax_regression_function.hpp

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ SoftmaxRegressionFunction()

Member Function Documentation

◆ Evaluate() [1/2]

◆ Evaluate() [2/2]

◆ FitIntercept()

◆ GetGroundTruthMatrix()

◆ GetInitialPoint()

◆ GetProbabilitiesMatrix()

◆ Gradient() [1/2]

◆ Gradient() [2/2]

◆ InitializeWeights() [1/3]

◆ InitializeWeights() [2/3]

◆ InitializeWeights() [3/3]

◆ Lambda() [1/2]

◆ Lambda() [2/2]

◆ NumClasses()

◆ NumFeatures()

◆ NumFunctions()

◆ PartialGradient()

◆ Shuffle()