#include <linear.hpp>

Collaboration diagram for ML::SVM::LinearSVMModel< T >:

Public Member Functions
std::size_t	coefCols () const

Static Public Member Functions
static LinearSVMModel< T >	fit (const raft::handle_t &handle, const LinearSVMParams &params, const T X, const std::size_t nRows, const std::size_t nCols, const T y, const T *sampleWeight)
	Allocate and fit the LinearSVM model. More...

static LinearSVMModel< T >	allocate (const raft::handle_t &handle, const LinearSVMParams &params, const std::size_t nCols, const std::size_t nClasses=0)
	Explicitly allocate the data for the model without training it. More...

static void	free (const raft::handle_t &handle, LinearSVMModel< T > &model)
	Free the allocated memory. The model is not usable after the call of this method. More...

static void	predict (const raft::handle_t &handle, const LinearSVMParams &params, const LinearSVMModel< T > &model, const T X, const std::size_t nRows, const std::size_t nCols, T out)
	Predict using the trained LinearSVM model. More...

static void	decisionFunction (const raft::handle_t &handle, const LinearSVMParams &params, const LinearSVMModel< T > &model, const T X, const std::size_t nRows, const std::size_t nCols, T out)
	Calculate decision function value for samples in input. More...

static void	predictProba (const raft::handle_t &handle, const LinearSVMParams &params, const LinearSVMModel< T > &model, const T X, const std::size_t nRows, const std::size_t nCols, const bool log, T out)
	For SVC, predict the probabilities for each outcome. More...

Public Attributes
T *	w

T *	classes = nullptr

T *	probScale = nullptr

std::size_t	nClasses = 0

std::size_t	coefRows

Member Function Documentation

◆ allocate()

template<typename T >

static LinearSVMModel<T> ML::SVM::LinearSVMModel< T >::allocate	(	const raft::handle_t &	handle,
		const LinearSVMParams &	params,
		const std::size_t	nCols,
		const std::size_t	nClasses = `0`
	)

static

Explicitly allocate the data for the model without training it.

Parameters

[in]	handle	the cuML handle.
[in]	params	the model parameters.
[in]	nCols	the number of feature dimensions.
[in]	nClasses	the number of classes in the dataset (not applicable for regression).

Returns: the trained model (don't forget to call free on it after use).

◆ coefCols()

template<typename T >

std::size_t ML::SVM::LinearSVMModel< T >::coefCols ( ) const

inline

It's 1 for binary classification or regression; nClasses for multiclass.

◆ decisionFunction()

template<typename T >

static void ML::SVM::LinearSVMModel< T >::decisionFunction	(	const raft::handle_t &	handle,
		const LinearSVMParams &	params,
		const LinearSVMModel< T > &	model,
		const T *	X,
		const std::size_t	nRows,
		const std::size_t	nCols,
		T *	out
	)

static

Calculate decision function value for samples in input.

Parameters

[in]	handle	the cuML handle.
[in]	params	the model parameters.
[in]	model	the trained model.
[in]	X	the input data matrix of size (nRows, nCols) in column-major format.
[in]	nRows	number of vectors
[in]	nCols	number of features
[out]	out	the decision function value of size (nRows, n_classes <= 2 ? 1 : n_classes) in row-major format.

◆ fit()

template<typename T >

static LinearSVMModel<T> ML::SVM::LinearSVMModel< T >::fit	(	const raft::handle_t &	handle,
		const LinearSVMParams &	params,
		const T *	X,
		const std::size_t	nRows,
		const std::size_t	nCols,
		const T *	y,
		const T *	sampleWeight
	)

static

Allocate and fit the LinearSVM model.

Parameters

[in]	handle	the cuML handle.
[in]	params	the model parameters.
[in]	X	the input data matrix of size (nRows, nCols) in column-major format.
[in]	nRows	the number of input samples.
[in]	nCols	the number of feature dimensions.
[in]	y	the target - a single vector of either real (regression) or categorical (classification) values (nRows, ).
[in]	sampleWeight	the non-negative weights for the training sample (nRows, ).

Returns: the trained model (don't forget to call free on it after use).

◆ free()

template<typename T >

static void ML::SVM::LinearSVMModel< T >::free	(	const raft::handle_t &	handle,
		LinearSVMModel< T > &	model
	)

static

Free the allocated memory. The model is not usable after the call of this method.

◆ predict()

template<typename T >

static void ML::SVM::LinearSVMModel< T >::predict	(	const raft::handle_t &	handle,
		const LinearSVMParams &	params,
		const LinearSVMModel< T > &	model,
		const T *	X,
		const std::size_t	nRows,
		const std::size_t	nCols,
		T *	out
	)

static

Predict using the trained LinearSVM model.

Parameters

[in]	handle	the cuML handle.
[in]	params	the model parameters.
[in]	model	the trained model.
[in]	X	the input data matrix of size (nRows, nCols) in column-major format.
[in]	nRows	the number of input samples.
[in]	nCols	the number of feature dimensions.
[out]	out	the predictions (nRows, ).

◆ predictProba()

template<typename T >

static void ML::SVM::LinearSVMModel< T >::predictProba	(	const raft::handle_t &	handle,
		const LinearSVMParams &	params,
		const LinearSVMModel< T > &	model,
		const T *	X,
		const std::size_t	nRows,
		const std::size_t	nCols,
		const bool	log,
		T *	out
	)

static

For SVC, predict the probabilities for each outcome.

Parameters

[in]	handle	the cuML handle.
[in]	params	the model parameters.
[in]	model	the trained model.
[in]	X	the input data matrix of size (nRows, nCols) in column-major format.
[in]	nRows	the number of input samples.
[in]	nCols	the number of feature dimensions.
[in]	log	whether to output log-probabilities instead of probabilities.
[out]	out	the estimated probabilities (nRows, nClasses) in row-major format.

Member Data Documentation

◆ classes

template<typename T >

T* ML::SVM::LinearSVMModel< T >::classes = nullptr

Sorted, unique values of input array y.

◆ coefRows

template<typename T >

std::size_t ML::SVM::LinearSVMModel< T >::coefRows

Number of rows of w, which is the number of data features plus maybe bias.

◆ nClasses

template<typename T >

std::size_t ML::SVM::LinearSVMModel< T >::nClasses = 0

Number of classes (not applicable for regression).

◆ probScale

template<typename T >

T* ML::SVM::LinearSVMModel< T >::probScale = nullptr

C-style (row-major) matrix of the probabolistic model calibration coefficients. It's empty if LinearSVMParams.probability == false. Otherwise, it's size is (2, coefCols). where coefCols = nClasses == 2 ? 1 : nClasses

◆ w

template<typename T >

T* ML::SVM::LinearSVMModel< T >::w

C-style (row-major) matrix of coefficients of size (coefRows, coefCols) where coefRows = nCols + (params.fit_intercept ? 1 : 0) coefCols = nClasses == 2 ? 1 : nClasses

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

include/cuml/svm/linear.hpp

Public Member Functions

Static Public Member Functions

Public Attributes

Member Function Documentation

◆ allocate()

◆ coefCols()

◆ decisionFunction()

◆ fit()

◆ free()

◆ predict()

◆ predictProba()

Member Data Documentation

◆ classes

◆ coefRows

◆ nClasses

◆ probScale

◆ w