Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values. More...

Public Member Functions
	SoftmaxRegression (const size_t inputSize=0, const size_t numClasses=0, const bool fitIntercept=false)
	Initialize the SoftmaxRegression without performing training. More...

template < typename OptimizerType = ens::L_BFGS >
	SoftmaxRegression (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses, const double lambda=0.0001, const bool fitIntercept=false, OptimizerType optimizer=OptimizerType())
	Construct the SoftmaxRegression class with the provided data and labels. More...

template<typename OptimizerType , typename... CallbackTypes>
	SoftmaxRegression (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses, const double lambda, const bool fitIntercept, OptimizerType optimizer, CallbackTypes &&... callbacks)
	Construct the SoftmaxRegression class with the provided data and labels. More...

void	Classify (const arma::mat &dataset, arma::Row< size_t > &labels) const
	Classify the given points, returning the predicted labels for each point. More...

template < typename VecType >
size_t	Classify (const VecType &point) const
	Classify the given point. More...

void	Classify (const arma::mat &dataset, arma::Row< size_t > &labels, arma::mat &probabilities) const
	Classify the given points, returning class probabilities and predicted class label for each point. More...

void	Classify (const arma::mat &dataset, arma::mat &probabilities) const
	Classify the given points, returning class probabilities for each point. More...

double	ComputeAccuracy (const arma::mat &testData, const arma::Row< size_t > &labels) const
	Computes accuracy of the learned model given the feature data and the labels associated with each data point. More...

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

bool	FitIntercept () const
	Gets the intercept term flag. We can't change this after training. More...

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

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

size_t &	NumClasses ()
	Sets the number of classes. More...

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

arma::mat &	Parameters ()
	Get the model parameters. More...

const arma::mat &	Parameters () const
	Get the model parameters. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the SoftmaxRegression model. More...

template < typename OptimizerType = ens::L_BFGS >
double	Train (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses, OptimizerType optimizer=OptimizerType())
	Train the softmax regression with the given training data. More...

template<typename OptimizerType = ens::L_BFGS, typename... CallbackTypes>
double	Train (const arma::mat &data, const arma::Row< size_t > &labels, const size_t numClasses, OptimizerType optimizer, CallbackTypes &&... callbacks)
	Train the softmax regression with the given training data. More...

Detailed Description

Softmax Regression is a classifier which can be used for classification when the data available can take two or more class values.

It is a generalization of Logistic Regression (which is used only for binary classification). The model has a different set of parameters for each class, but can be easily converted into a vectorized implementation as has been done in this module. The model can be used for direct classification of feature data or in conjunction with unsupervised learning methods. More technical details about the model can be found on the following webpage:

http://ufldl.stanford.edu/wiki/index.php/Softmax_Regression

An example on how to use the interface is shown below:

arma::mat trainData; // Training data matrix.
arma::Row<size_t> labels; // Labels associated with the data.
const size_t inputSize = 1000; // Size of input feature vector.
const size_t numClasses = 10; // Number of classes.
const double lambda = 0.0001; // L2-Regularization parameter.
const size_t numBasis = 5; // Parameter required for L-BFGS algorithm.
const size_t numIterations = 100; // Maximum number of iterations.
// Train the model using an instantiated optimizer for the training.
SoftmaxRegression regressor(trainData.n_rows, numClasses);
ens::L_BFGS optimizer(numBasis, numIterations);
regressor.Train(trainData, labels, numClasses, std::move(optimizer));
arma::mat testData; // Test data matrix.
arma::Row<size_t> predictions; // Vectors to store predictions in.
// Obtain predictions from both the learned models.
regressor.Classify(testData, predictions);

Definition at line 59 of file softmax_regression.hpp.

Constructor & Destructor Documentation

◆ SoftmaxRegression() [1/3]

SoftmaxRegression	(	const size_t	inputSize = `0`,
		const size_t	numClasses = `0`,
		const bool	fitIntercept = `false`
	)

Initialize the SoftmaxRegression without performing training.

Default value of lambda is 0.0001. Be sure to use Train() before calling Classify() or ComputeAccuracy(), otherwise the results may be meaningless.

Parameters

inputSize	Size of the input feature vector.
numClasses	Number of classes for classification.
fitIntercept	add intercept term or not.

◆ SoftmaxRegression() [2/3]

SoftmaxRegression	(	const arma::mat &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const double	lambda = `0.0001`,
		const bool	fitIntercept = `false`,
		OptimizerType	optimizer = `OptimizerType()`
	)

Construct the SoftmaxRegression class with the provided data and labels.

This will train the model. Optionally, the parameter 'lambda' can be passed, which controls the amount of L2-regularization in the objective function. By default, the model takes a small value.

Template Parameters

OptimizerType Desired optimizer type.

Parameters

data	Input training features. Each column associate with one sample
labels	Labels associated with the feature data.
numClasses	Number of classes for classification.
optimizer	Desired optimizer.
lambda	L2-regularization constant.
fitIntercept	add intercept term or not.

◆ SoftmaxRegression() [3/3]

SoftmaxRegression	(	const arma::mat &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const double	lambda,
		const bool	fitIntercept,
		OptimizerType	optimizer,
		CallbackTypes &&...	callbacks
	)

Construct the SoftmaxRegression class with the provided data and labels.

This will train the model. Optionally, the parameter 'lambda' can be passed, which controls the amount of L2-regularization in the objective function. By default, the model takes a small value.

Template Parameters

OptimizerType	Desired optimizer type.
CallbackTypes	Types of Callback Functions.

Parameters

data	Input training features. Each column associate with one sample
labels	Labels associated with the feature data.
numClasses	Number of classes for classification.
lambda	L2-regularization constant.
fitIntercept	add intercept term or not.
optimizer	Desired optimizer.
callbacks	Callback function for ensmallen optimizer `OptimizerType`. See https://www.ensmallen.org/docs.html#callback-documentation.

Member Function Documentation

◆ Classify() [1/4]

void Classify	(	const arma::mat &	dataset,
		arma::Row< size_t > &	labels
	)		const

Classify the given points, returning the predicted labels for each point.

The function calculates the probabilities for every class, given a data point. It then chooses the class which has the highest probability among all.

Parameters

dataset	Set of points to classify.
labels	Predicted labels for each point.

◆ Classify() [2/4]

size_t Classify ( const VecType & point ) const

Classify the given point.

The predicted class label is returned. The function calculates the probabilites for every class, given the point. It then chooses the class which has the highest probability among all.

Parameters

point Point to be classified.

Returns: Predicted class label of the point.

◆ Classify() [3/4]

void Classify	(	const arma::mat &	dataset,
		arma::Row< size_t > &	labels,
		arma::mat &	probabilities
	)		const

Classify the given points, returning class probabilities and predicted class label for each point.

The function calculates the probabilities for every class, given a data point. It then chooses the class which has the highest probability among all.

Parameters

dataset	Matrix of data points to be classified.
labels	Predicted labels for each point.
probabilities	Class probabilities for each point.

◆ Classify() [4/4]

void Classify	(	const arma::mat &	dataset,
		arma::mat &	probabilities
	)		const

Classify the given points, returning class probabilities for each point.

Parameters

dataset	Matrix of data points to be classified.
probabilities	Class probabilities for each point.

◆ ComputeAccuracy()

double ComputeAccuracy	(	const arma::mat &	testData,
		const arma::Row< size_t > &	labels
	)		const

Computes accuracy of the learned model given the feature data and the labels associated with each data point.

Predictions are made using the provided data and are compared with the actual labels.

Parameters

testData	Matrix of data points using which predictions are made.
labels	Vector of labels associated with the data.

◆ FeatureSize()

size_t FeatureSize ( ) const

inline

Gets the features size of the training data.

Definition at line 227 of file softmax_regression.hpp.

◆ FitIntercept()

bool FitIntercept ( ) const

inline

Gets the intercept term flag. We can't change this after training.

Definition at line 219 of file softmax_regression.hpp.

◆ Lambda() [1/2]

double& Lambda ( )

inline

Sets the regularization parameter.

Definition at line 214 of file softmax_regression.hpp.

◆ Lambda() [2/2]

double Lambda ( ) const

inline

Gets the regularization parameter.

Definition at line 216 of file softmax_regression.hpp.

◆ NumClasses() [1/2]

size_t& NumClasses ( )

inline

Sets the number of classes.

Definition at line 209 of file softmax_regression.hpp.

◆ NumClasses() [2/2]

size_t NumClasses ( ) const

inline

Gets the number of classes.

Definition at line 211 of file softmax_regression.hpp.

◆ Parameters() [1/2]

arma::mat& Parameters ( )

inline

Get the model parameters.

Definition at line 222 of file softmax_regression.hpp.

◆ Parameters() [2/2]

const arma::mat& Parameters ( ) const

inline

Get the model parameters.

Definition at line 224 of file softmax_regression.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

inline

Serialize the SoftmaxRegression model.

Definition at line 235 of file softmax_regression.hpp.

◆ Train() [1/2]

double Train	(	const arma::mat &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		OptimizerType	optimizer = `OptimizerType()`
	)

Train the softmax regression with the given training data.

Template Parameters

OptimizerType Desired optimizer type.

Parameters

data	Input data with each column as one example.
labels	Labels associated with the feature data.
numClasses	Number of classes for classification.
optimizer	Desired optimizer.

Returns: Objective value of the final point.

◆ Train() [2/2]

double Train	(	const arma::mat &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		OptimizerType	optimizer,
		CallbackTypes &&...	callbacks
	)

Train the softmax regression with the given training data.

Template Parameters

OptimizerType	Desired optimizer type.
CallbackTypes	Types of Callback Functions.

Parameters

data	Input data with each column as one example.
labels	Labels associated with the feature data.
numClasses	Number of classes for classification.
optimizer	Desired optimizer.
callbacks	Callback function for ensmallen optimizer `OptimizerType`. See https://www.ensmallen.org/docs.html#callback-documentation.

Returns: Objective value of the final point.

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.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ SoftmaxRegression() [1/3]

◆ SoftmaxRegression() [2/3]

◆ SoftmaxRegression() [3/3]

Member Function Documentation

◆ Classify() [1/4]

◆ Classify() [2/4]

◆ Classify() [3/4]

◆ Classify() [4/4]

◆ ComputeAccuracy()

◆ FeatureSize()

◆ FitIntercept()

◆ Lambda() [1/2]

◆ Lambda() [2/2]

◆ NumClasses() [1/2]

◆ NumClasses() [2/2]

◆ Parameters() [1/2]

◆ Parameters() [2/2]

◆ serialize()

◆ Train() [1/2]

◆ Train() [2/2]