The LinearSVM class implements an L2-regularized support vector machine model, and supports training with multiple optimizers and classification. More...

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

template < typename OptimizerType = ens::L_BFGS >
	LinearSVM (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses=2, const double lambda=0.0001, const double delta=1.0, const bool fitIntercept=false, OptimizerType optimizer=OptimizerType())
	Construct the LinearSVM class with the provided data and labels. More...

	LinearSVM (const size_t inputSize, const size_t numClasses=0, const double lambda=0.0001, const double delta=1.0, const bool fitIntercept=false)
	Initialize the Linear SVM without performing training. More...

	LinearSVM (const size_t numClasses=0, const double lambda=0.0001, const double delta=1.0, const bool fitIntercept=false)
	Initialize the Linear SVM without performing training. More...

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

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

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

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

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

double &	Delta ()
	Sets the margin between the correct class and all other classes. More...

double	Delta () const
	Gets the margin between the correct class and all other classes. More...

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

bool &	FitIntercept ()
	Sets the intercept term flag. 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 ()
	Set 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 LinearSVM model. More...

template<typename OptimizerType , typename... CallbackTypes>
double	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, OptimizerType optimizer, CallbackTypes &&... callbacks)
	Train the Linear SVM with the given training data. More...

template < typename OptimizerType = ens::L_BFGS >
double	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses=2, OptimizerType optimizer=OptimizerType())
	Train the Linear SVM with the given training data. More...

Detailed Description

template
<
typename
MatType
=
arma::mat
>

class mlpack::svm::LinearSVM< MatType >

The LinearSVM class implements an L2-regularized support vector machine model, and supports training with multiple optimizers and classification.

The class supports different observation types via the MatType template parameter; for instance, support vector classification can be performed on sparse datasets by specifying arma::sp_mat as the MatType parameter.

Linear SVM can be used for general classification tasks which will work on multiclass classification. More technical details about the model can be found from the following:

@inproceedings{weston1999support,
title        = {Support vector machines for multi-class pattern
                recognition.},
author       = {Weston, Jason and Watkins, Chris},
booktitle    = {Proceedings of the 7th European Symposium on Artifical Neural
                Networks (ESANN '99)},
volume       = {99},
pages        = {219--224},
year         = {1999}
}

@article{cortes1995support,
title        = {Support-vector networks},
author       = {Cortes, Corinna and Vapnik, Vladimir},
journal      = {Machine Learning},
volume       = {20},
number       = {3},
pages        = {273--297},
year         = {1995},
publisher    = {Springer}
}

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

arma::mat train_data; // 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 = 5; // Number of classes.
// Train the model using default options.
LinearSVM<> lsvm(train_data, labels, inputSize, numClasses, lambda,
    delta, L_BFGS());
arma::mat test_data;
arma::Row<size_t> predictions;
lsvm.Classify(test_data, predictions);

Template Parameters

MatType Type of data matrix.

Definition at line 80 of file linear_svm.hpp.

Constructor & Destructor Documentation

◆ LinearSVM() [1/4]

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

Construct the LinearSVM 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 differentiable separable optimizer
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.
delta	Margin of difference between correct class and other classes.
fitIntercept	add intercept term or not.
optimizer	Desired optimizer.
callbacks	Callback functions. See https://www.ensmallen.org/docs.html#callback-documentation.

◆ LinearSVM() [2/4]

LinearSVM	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses = `2`,
		const double	lambda = `0.0001`,
		const double	delta = `1.0`,
		const bool	fitIntercept = `false`,
		OptimizerType	optimizer = `OptimizerType()`
	)

Construct the LinearSVM 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 differentiable separable optimizer

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.
delta	Margin of difference between correct class and other classes.
fitIntercept	add intercept term or not.
optimizer	Desired optimizer.

◆ LinearSVM() [3/4]

LinearSVM	(	const size_t	inputSize,
		const size_t	numClasses = `0`,
		const double	lambda = `0.0001`,
		const double	delta = `1.0`,
		const bool	fitIntercept = `false`
	)

Initialize the Linear SVM 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.
lambda	L2-regularization constant.
delta	Margin of difference between correct class and other classes.
fitIntercept	add intercept term or not.

◆ LinearSVM() [4/4]

LinearSVM	(	const size_t	numClasses = `0`,
		const double	lambda = `0.0001`,
		const double	delta = `1.0`,
		const bool	fitIntercept = `false`
	)

Initialize the Linear SVM 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

numClasses	Number of classes for classification.
lambda	L2-regularization constant.
delta	Margin of difference between correct class and other classes.
fitIntercept	add intercept term or not.

Member Function Documentation

◆ Classify() [1/4]

void Classify	(	const MatType &	data,
		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

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

◆ Classify() [2/4]

void Classify	(	const MatType &	data,
		arma::Row< size_t > &	labels,
		arma::mat &	scores
	)		const

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

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

Parameters

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

◆ Classify() [3/4]

void Classify	(	const MatType &	data,
		arma::mat &	scores
	)		const

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

Parameters

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

◆ Classify() [4/4]

size_t Classify ( const VecType & point ) const

Classify the given point.

The predicted class label is returned. The function calculates the scores 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.

◆ ComputeAccuracy()

double ComputeAccuracy	(	const MatType &	testData,
		const arma::Row< size_t > &	testLabels
	)		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.
testLabels	Vector of labels associated with the data.

Returns: Accuracy of the model.

◆ Delta() [1/2]

double& Delta ( )

inline

Sets the margin between the correct class and all other classes.

Definition at line 273 of file linear_svm.hpp.

◆ Delta() [2/2]

double Delta ( ) const

inline

Gets the margin between the correct class and all other classes.

Definition at line 275 of file linear_svm.hpp.

◆ FeatureSize()

size_t FeatureSize ( ) const

inline

Gets the features size of the training data.

Definition at line 286 of file linear_svm.hpp.

◆ FitIntercept()

bool& FitIntercept ( )

inline

Sets the intercept term flag.

Definition at line 278 of file linear_svm.hpp.

◆ Lambda() [1/2]

double& Lambda ( )

inline

Sets the regularization parameter.

Definition at line 268 of file linear_svm.hpp.

◆ Lambda() [2/2]

double Lambda ( ) const

inline

Gets the regularization parameter.

Definition at line 270 of file linear_svm.hpp.

◆ NumClasses() [1/2]

size_t& NumClasses ( )

inline

Sets the number of classes.

Definition at line 263 of file linear_svm.hpp.

◆ NumClasses() [2/2]

size_t NumClasses ( ) const

inline

Gets the number of classes.

Definition at line 265 of file linear_svm.hpp.

◆ Parameters() [1/2]

arma::mat& Parameters ( )

inline

Set the model parameters.

Definition at line 281 of file linear_svm.hpp.

◆ Parameters() [2/2]

const arma::mat& Parameters ( ) const

inline

Get the model parameters.

Definition at line 283 of file linear_svm.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

inline

Serialize the LinearSVM model.

Definition at line 294 of file linear_svm.hpp.

◆ Train() [1/2]

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

Train the Linear SVM with the given training data.

Template Parameters

OptimizerType	Desired optimizer.
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.
optimizer	Desired optimizer.
callbacks	Callback Functions. See https://www.ensmallen.org/docs.html#callback-documentation.

Returns: Objective value of the final point.

◆ Train() [2/2]

double Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses = `2`,
		OptimizerType	optimizer = `OptimizerType()`
	)

Train the Linear SVM with the given training data.

Template Parameters

OptimizerType Desired optimizer.

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.

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/linear_svm/linear_svm.hpp

Public Member Functions

Detailed Description

template<typenameMatType=arma::mat> class mlpack::svm::LinearSVM< MatType >

Constructor & Destructor Documentation

◆ LinearSVM() [1/4]

◆ LinearSVM() [2/4]

◆ LinearSVM() [3/4]

◆ LinearSVM() [4/4]

Member Function Documentation

◆ Classify() [1/4]

◆ Classify() [2/4]

◆ Classify() [3/4]

◆ Classify() [4/4]

◆ ComputeAccuracy()

◆ Delta() [1/2]

◆ Delta() [2/2]

◆ 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]

template
<
typename
MatType
=
arma::mat
>

class mlpack::svm::LinearSVM< MatType >