ML::SVM::SVC< math_t > Class Template Reference

C-Support Vector Classification. More...

#include <svc.hpp>

Collaboration diagram for ML::SVM::SVC< math_t >:

Public Member Functions
	SVC (raft::handle_t &handle, math_t C=1, math_t tol=1.0e-3, ML::matrix::KernelParams kernel_params=ML::matrix::KernelParams{ML::matrix::KernelType::LINEAR, 3, 1, 0}, math_t cache_size=200, int max_iter=-1, int nochange_steps=1000, rapids_logger::level_enum verbosity=rapids_logger::level_enum::info)
	Constructs a support vector classifier. More...
	~SVC ()
void	fit (math_t *input, int n_rows, int n_cols, math_t *labels, const math_t *sample_weight=nullptr)
	Fit a support vector classifier to the training data. More...
void	predict (math_t *input, int n_rows, int n_cols, math_t *preds)
	Predict classes for samples in input. More...
void	decisionFunction (math_t *input, int n_rows, int n_cols, math_t *preds)
	Calculate decision function value for samples in input. More...

Public Attributes
ML::matrix::KernelParams	kernel_params
SvmParameter	param
SvmModel< math_t >	model

Detailed Description

template<typename math_t>
class ML::SVM::SVC< math_t >

C-Support Vector Classification.

This is a Scikit-Learn like wrapper around the stateless C++ functions. See Issue #456 for general discussion about stateful Sklearn like wrappers.

The classifier will be fitted using the SMO algorithm in dual space.

The decision function takes the following form

\[ sign\left( \sum_{i=1}^{N_{support}} y_i \alpha_i K(x_i,x) + b \right), \]

where \(x_i\) are the support vectors, and \( y_i \alpha_i \) are the dual coordinates.

The penalty parameter C limits the values of the dual coefficients

\[ 0 <= \alpha <= C \]

Constructor & Destructor Documentation

SVC()

template<typename math_t >

ML::SVM::SVC< math_t >::SVC	(	raft::handle_t &	handle,
		math_t	C = 1,
		math_t	tol = 1.0e-3,
		ML::matrix::KernelParams	kernel_params = ML::matrix::KernelParams{ML::matrix::KernelType::LINEAR, 3, 1, 0},
		math_t	cache_size = 200,
		int	max_iter = -1,
		int	nochange_steps = 1000,
		rapids_logger::level_enum	verbosity = rapids_logger::level_enum::info
	)

Constructs a support vector classifier.

Parameters

handle	cuML handle
C	penalty term
tol	tolerance to stop fitting
kernel_params	parameters for kernels
cache_size	size of kernel cache in device memory (MiB)
max_iter	maximum number of outer iterations in SmoSolver
nochange_steps	number of steps with no change wrt convergence
verbosity	verbosity level for logging messages during execution

~SVC()

template<typename math_t >

ML::SVM::SVC< math_t >::~SVC

(

)

Member Function Documentation

decisionFunction()

template<typename math_t >

void ML::SVM::SVC< math_t >::decisionFunction	(	math_t *	input,
		int	n_rows,
		int	n_cols,
		math_t *	preds
	)

Calculate decision function value for samples in input.

Parameters

[in]	input	device pointer for the input data in column major format, size [n_rows x n_cols].
[in]	n_rows	number of vectors
[in]	n_cols	number of features
[out]	preds	device pointer to store the decision function value Size [n_rows]. Should be allocated on entry.

fit()

template<typename math_t >

void ML::SVM::SVC< math_t >::fit	(	math_t *	input,
		int	n_rows,
		int	n_cols,
		math_t *	labels,
		const math_t *	sample_weight = nullptr
	)

Fit a support vector classifier to the training data.

Each row of the input data stores a feature vector. We use the SMO method to fit the SVM.

Parameters

	input	device pointer for the input data in column major format. Size n_rows x n_cols.
	n_rows	number of rows
	n_cols	number of columns
	labels	device pointer for the labels. Size n_rows.
[in]	sample_weight	optional sample weights, size [n_rows]

predict()

template<typename math_t >

void ML::SVM::SVC< math_t >::predict	(	math_t *	input,
		int	n_rows,
		int	n_cols,
		math_t *	preds
	)

Predict classes for samples in input.

Parameters

[in]	input	device pointer for the input data in column major format, size [n_rows x n_cols].
[in]	n_rows	number of vectors
[in]	n_cols	number of features
[out]	preds	device pointer to store the predicted class labels. Size [n_rows]. Should be allocated on entry.

Member Data Documentation

kernel_params

template<typename math_t >

ML::matrix::KernelParams ML::SVM::SVC< math_t >::kernel_params

model

template<typename math_t >

SvmModel<math_t> ML::SVM::SVC< math_t >::model

param

template<typename math_t >

SvmParameter ML::SVM::SVC< math_t >::param

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

• include/cuml/svm/svc.hpp