Namespace containing the C-SVM using the CUDA backend. More...

Namespaces
	detail
	Namespace containing CUDA backend specific implementation details. Should not directly be used by users.

Classes
class	csvm
	A C-SVM implementation using CUDA as backend. More...

class	backend_exception
	Exception type thrown if a problem with the CUDA backend occurs. More...

Functions
template<typename real_type >
__global__ void	device_kernel_w_linear (real_type w_d, const real_type data_d, const real_type data_last_d, const real_type alpha_d, const kernel_index_type num_data_points, const kernel_index_type num_features)
	Calculate the `w` vector to speed up the prediction of the labels for data points using the linear kernel function. More...

template<typename real_type >
__global__ void	device_kernel_predict_polynomial (real_type out_d, const real_type data_d, const real_type data_last_d, const real_type alpha_d, const kernel_index_type num_data_points, const real_type *points, const kernel_index_type num_predict_points, const kernel_index_type num_features, const int degree, const real_type gamma, const real_type coef0)
	Predicts the labels for data points using the polynomial kernel function. More...

template<typename real_type >
__global__ void	device_kernel_predict_rbf (real_type out_d, const real_type data_d, const real_type data_last_d, const real_type alpha_d, const kernel_index_type num_data_points, const real_type *points, const kernel_index_type num_predict_points, const kernel_index_type num_features, const real_type gamma)
	Predicts the labels for data points using the radial basis functions kernel function. More...

template<typename real_type >
__global__ void	device_kernel_q_linear (real_type q, const real_type data_d, const real_type *data_last, const kernel_index_type num_rows, const kernel_index_type feature_range)
	Calculates the `q` vector using the linear C-SVM kernel. More...

template<typename real_type >
__global__ void	device_kernel_q_polynomial (real_type q, const real_type data_d, const real_type *data_last, const kernel_index_type num_rows, const kernel_index_type num_cols, const int degree, const real_type gamma, const real_type coef0)
	Calculates the `q` vector using the polynomial C-SVM kernel. More...

template<typename real_type >
__global__ void	device_kernel_q_rbf (real_type q, const real_type data_d, const real_type *data_last, const kernel_index_type num_rows, const kernel_index_type num_cols, const real_type gamma)
	Calculates the `q` vector using the radial basis functions C-SVM kernel. More...

template<typename real_type >
__global__ void	device_kernel_linear (const real_type q, real_type ret, const real_type d, const real_type data_d, const real_type QA_cost, const real_type cost, const kernel_index_type num_rows, const kernel_index_type feature_range, const real_type add, const kernel_index_type id)
	Calculates the C-SVM kernel using the linear kernel function. More...

template<typename real_type >
__global__ void	device_kernel_polynomial (const real_type q, real_type ret, const real_type d, const real_type data_d, const real_type QA_cost, const real_type cost, const kernel_index_type num_rows, const kernel_index_type num_cols, const real_type add, const int degree, const real_type gamma, const real_type coef0)
	Calculates the C-SVM kernel using the polynomial kernel function. More...

template<typename real_type >
__global__ void	device_kernel_rbf (const real_type q, real_type ret, const real_type d, const real_type data_d, const real_type QA_cost, const real_type cost, const kernel_index_type num_rows, const kernel_index_type num_cols, const real_type add, const real_type gamma)
	Calculates the C-SVM kernel using the radial basis function kernel function. More...

Detailed Description

Namespace containing the C-SVM using the CUDA backend.

Function Documentation

◆ device_kernel_w_linear()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_w_linear	(	real_type *	w_d,
		const real_type *	data_d,
		const real_type *	data_last_d,
		const real_type *	alpha_d,
		const kernel_index_type	num_data_points,
		const kernel_index_type	num_features
	)

Calculate the w vector to speed up the prediction of the labels for data points using the linear kernel function.

Supports multi-GPU execution.

Template Parameters

real_type the type of the data

Parameters

[out]	w_d	the `w` vector to assemble
[in]	data_d	the one-dimension support vector matrix
[in]	data_last_d	the last row of the support vector matrix
[in]	alpha_d	the previously calculated weight for each data point
[in]	num_data_points	the total number of support vectors
[in]	num_features	the number of features per support vector

◆ device_kernel_predict_polynomial()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_predict_polynomial	(	real_type *	out_d,
		const real_type *	data_d,
		const real_type *	data_last_d,
		const real_type *	alpha_d,
		const kernel_index_type	num_data_points,
		const real_type *	points,
		const kernel_index_type	num_predict_points,
		const kernel_index_type	num_features,
		const int	degree,
		const real_type	gamma,
		const real_type	coef0
	)

Predicts the labels for data points using the polynomial kernel function.

Currently only single GPU execution is supported.

Template Parameters

real_type the type of the data

Parameters

[out]	out_d	the calculated predictions
[in]	data_d	the one-dimension support vector matrix
[in]	data_last_d	the last row of the support vector matrix
[in]	alpha_d	the previously calculated weight for each data point
[in]	num_data_points	the total number of support vectors
[in]	points	the data points to predict
[in]	num_predict_points	the total number of data points to predict
[in]	num_features	the number of features per support vector and point to predict
[in]	degree	the degree parameter used in the polynomial kernel function
[in]	gamma	the gamma parameter used in the polynomial kernel function
[in]	coef0	the coef0 parameter used in the polynomial kernel function

◆ device_kernel_predict_rbf()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_predict_rbf	(	real_type *	out_d,
		const real_type *	data_d,
		const real_type *	data_last_d,
		const real_type *	alpha_d,
		const kernel_index_type	num_data_points,
		const real_type *	points,
		const kernel_index_type	num_predict_points,
		const kernel_index_type	num_features,
		const real_type	gamma
	)

Predicts the labels for data points using the radial basis functions kernel function.

Currently only single GPU execution is supported.

Template Parameters

real_type the type of the data

Parameters

[out]	out_d	the calculated predictions
[in]	data_d	the one-dimension support vector matrix
[in]	data_last_d	the last row of the support vector matrix
[in]	alpha_d	the previously calculated weight for each data point
[in]	num_data_points	the total number of support vectors
[in]	points	the data points to predict
[in]	num_predict_points	the total number of data points to predict
[in]	num_features	the number of features per support vector and point to predict
[in]	gamma	the gamma parameter used in the rbf kernel function

◆ device_kernel_q_linear()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_q_linear	(	real_type *	q,
		const real_type *	data_d,
		const real_type *	data_last,
		const kernel_index_type	num_rows,
		const kernel_index_type	feature_range
	)

Calculates the q vector using the linear C-SVM kernel.

Supports multi-GPU execution.

Template Parameters

real_type the type of the data

Parameters

[out]	q	the calculated `q` vector
[in]	data_d	the one-dimensional data matrix
[in]	data_last	the last row in the data matrix
[in]	num_rows	the number of rows in the data matrix
[in]	feature_range	number of features used for the calculation

◆ device_kernel_q_polynomial()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_q_polynomial	(	real_type *	q,
		const real_type *	data_d,
		const real_type *	data_last,
		const kernel_index_type	num_rows,
		const kernel_index_type	num_cols,
		const int	degree,
		const real_type	gamma,
		const real_type	coef0
	)

Calculates the q vector using the polynomial C-SVM kernel.

Currently only single GPU execution is supported.

Template Parameters

real_type the type of the data

Parameters

[out]	q	the calculated `q` vector
[in]	data_d	the one-dimensional data matrix
[in]	data_last	the last row in the data matrix
[in]	num_rows	the number of rows in the data matrix
[in]	num_cols	the number of columns in the data matrix
[in]	degree	the degree parameter used in the polynomial kernel function
[in]	gamma	the gamma parameter used in the polynomial kernel function
[in]	coef0	the coef0 parameter used in the polynomial kernel function

◆ device_kernel_q_rbf()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_q_rbf	(	real_type *	q,
		const real_type *	data_d,
		const real_type *	data_last,
		const kernel_index_type	num_rows,
		const kernel_index_type	num_cols,
		const real_type	gamma
	)

Calculates the q vector using the radial basis functions C-SVM kernel.

Currently only single GPU execution is supported.

Template Parameters

real_type the type of the data

Parameters

[out]	q	the calculated `q` vector
[in]	data_d	the one-dimensional data matrix
[in]	data_last	the last row in the data matrix
[in]	num_rows	the number of rows in the data matrix
[in]	num_cols	the number of columns in the data matrix
[in]	gamma	the gamma parameter used in the rbf kernel function

◆ device_kernel_linear()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_linear	(	const real_type *	q,
		real_type *	ret,
		const real_type *	d,
		const real_type *	data_d,
		const real_type	QA_cost,
		const real_type	cost,
		const kernel_index_type	num_rows,
		const kernel_index_type	feature_range,
		const real_type	add,
		const kernel_index_type	id
	)

Calculates the C-SVM kernel using the linear kernel function.

Supports multi-GPU execution.

Template Parameters

real_type the type of the data

Parameters

[in]	q	the `q` vector
[out]	ret	the result vector
[in]	d	the right-hand side of the equation
[in]	data_d	the one-dimension data matrix
[in]	QA_cost	the bottom right matrix entry multiplied by cost
[in]	cost	1 / the cost parameter in the C-SVM
[in]	num_rows	the number of columns in the data matrix
[in]	feature_range	number of features used for the calculation on the device `id`
[in]	add	denotes whether the values are added or subtracted from the result vector
[in]	id	the id of the current device

◆ device_kernel_polynomial()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_polynomial	(	const real_type *	q,
		real_type *	ret,
		const real_type *	d,
		const real_type *	data_d,
		const real_type	QA_cost,
		const real_type	cost,
		const kernel_index_type	num_rows,
		const kernel_index_type	num_cols,
		const real_type	add,
		const int	degree,
		const real_type	gamma,
		const real_type	coef0
	)

Calculates the C-SVM kernel using the polynomial kernel function.

Currently only single GPU execution is supported.

Template Parameters

real_type the type of the data

Parameters

[in]	q	the `q` vector
[out]	ret	the result vector
[in]	d	the right-hand side of the equation
[in]	data_d	the one-dimension data matrix
[in]	QA_cost	he bottom right matrix entry multiplied by cost
[in]	cost	1 / the cost parameter in the C-SVM
[in]	num_rows	the number of columns in the data matrix
[in]	num_cols	the number of rows in the data matrix
[in]	add	denotes whether the values are added or subtracted from the result vector
[in]	degree	the degree parameter used in the polynomial kernel function
[in]	gamma	the gamma parameter used in the polynomial kernel function
[in]	coef0	the coef0 parameter used in the polynomial kernel function

◆ device_kernel_rbf()

template<typename real_type >

__global__ void plssvm::cuda::device_kernel_rbf	(	const real_type *	q,
		real_type *	ret,
		const real_type *	d,
		const real_type *	data_d,
		const real_type	QA_cost,
		const real_type	cost,
		const kernel_index_type	num_rows,
		const kernel_index_type	num_cols,
		const real_type	add,
		const real_type	gamma
	)

Calculates the C-SVM kernel using the radial basis function kernel function.

Currently only single GPU execution is supported.

Template Parameters

real_type the type of the data

Parameters

[in]	q	the `q` vector
[out]	ret	the result vector
[in]	d	the right-hand side of the equation
[in]	data_d	the one-dimension data matrix
[in]	QA_cost	he bottom right matrix entry multiplied by cost
[in]	cost	1 / the cost parameter in the C-SVM
[in]	num_rows	the number of columns in the data matrix
[in]	num_cols	the number of rows in the data matrix
[in]	add	denotes whether the values are added or subtracted from the result vector
[in]	gamma	the gamma parameter used in the rbf kernel function

Namespaces

Classes

Functions

Detailed Description

Function Documentation

◆ device_kernel_w_linear()

◆ device_kernel_predict_polynomial()

◆ device_kernel_predict_rbf()

◆ device_kernel_q_linear()

◆ device_kernel_q_polynomial()

◆ device_kernel_q_rbf()

◆ device_kernel_linear()

◆ device_kernel_polynomial()

◆ device_kernel_rbf()