Base class for all C-SVM backends. More...

#include <csvm.hpp>

Inheritance diagram for plssvm::csvm:

Collaboration diagram for plssvm::csvm:

Public Member Functions
	csvm (parameter params={})
	Construct a C-SVM using the SVM parameter `params`. More...

template<typename... Args>
	csvm (Args &&...args)
	Construct a C-SVM forwarding all parameters `args` to the plssvm::parameter constructor. More...

	csvm (const csvm &)=delete
	Delete copy-constructor since a CSVM is a move-only type.

	csvm (csvm &&) noexcept=default
	Default move-constructor since a virtual destructor has been declared.

csvm &	operator= (const csvm &)=delete
	Delete copy-assignment operator since a CSVM is a move-only type. More...

csvm &	operator= (csvm &&) noexcept=default
	Default move-assignment operator since a virtual destructor has been declared. More...

virtual	~csvm ()=default
	Virtual destructor to enable safe inheritance.

target_platform	get_target_platform () const noexcept
	Return the target platform (i.e, CPU or GPU including the vendor) this SVM runs on. More...

parameter	get_params () const noexcept
	Return the currently used SVM parameter. More...

void	set_params (parameter params) noexcept
	Override the old SVM parameter with the new plssvm::parameter `params`. More...

template<typename... Args, PLSSVM_REQUIRES(detail::has_only_parameter_named_args_v< Args... >) >
void	set_params (Args &&...named_args)
	Override the old SVM parameter with the new ones given as named parameters in `named_args`. More...

template<typename real_type , typename label_type , typename... Args>
model< real_type, label_type >	fit (const data_set< real_type, label_type > &data, Args &&...named_args) const
	Fit a model using the current SVM on the `data`. More...

template<typename real_type , typename label_type >
std::vector< label_type >	predict (const model< real_type, label_type > &model, const data_set< real_type, label_type > &data) const
	Predict the labels for the `data` set using the `model`. More...

template<typename real_type , typename label_type >
real_type	score (const model< real_type, label_type > &model) const
	Calculate the accuracy of the `model`. More...

template<typename real_type , typename label_type >
real_type	score (const model< real_type, label_type > &model, const data_set< real_type, label_type > &data) const
	Calculate the accuracy of the labeled `data` set using the `model`. More...

Protected Member Functions
virtual std::pair< std::vector< float >, float >	solve_system_of_linear_equations (const detail::parameter< float > &params, const std::vector< std::vector< float >> &A, std::vector< float > b, float eps, unsigned long long max_iter) const =0
	Solves the equation \(Ax = b\) using the Conjugated Gradients algorithm. More...

virtual std::pair< std::vector< double >, double >	solve_system_of_linear_equations (const detail::parameter< double > &params, const std::vector< std::vector< double >> &A, std::vector< double > b, double eps, unsigned long long max_iter) const =0
	Solves the equation \(Ax = b\) using the Conjugated Gradients algorithm. More...

virtual std::vector< float >	predict_values (const detail::parameter< float > &params, const std::vector< std::vector< float >> &support_vectors, const std::vector< float > &alpha, float rho, std::vector< float > &w, const std::vector< std::vector< float >> &predict_points) const =0
	Uses the already learned model to predict the class of multiple (new) data points. More...

virtual std::vector< double >	predict_values (const detail::parameter< double > &params, const std::vector< std::vector< double >> &support_vectors, const std::vector< double > &alpha, double rho, std::vector< double > &w, const std::vector< std::vector< double >> &predict_points) const =0
	Uses the already learned model to predict the class of multiple (new) data points. More...

Protected Attributes
target_platform	target_ { plssvm::target_platform::automatic }
	The target platform of this SVM.

Private Member Functions
void	sanity_check_parameter () const
	Perform some sanity checks on the passed SVM parameters. More...

Private Attributes
parameter	params_ {}
	The SVM parameter (e.g., cost, degree, gamma, coef0) currently in use.

Detailed Description

Base class for all C-SVM backends.

This class implements all features shared between all C-SVM backends. It defines the whole public API of a C-SVM.

Constructor & Destructor Documentation

◆ csvm() [1/2]

plssvm::csvm::csvm ( parameter params = {} )

inlineexplicit

Construct a C-SVM using the SVM parameter params.

Uses the default SVM parameter if none are provided.

Parameters

[in] params the SVM parameter

◆ csvm() [2/2]

template<typename... Args>

plssvm::csvm::csvm ( Args &&... args )

explicit

Construct a C-SVM forwarding all parameters args to the plssvm::parameter constructor.

Template Parameters

Args	the type of the (named-)parameters

Parameters

[in] args the parameters used to construct a plssvm::parameter

Member Function Documentation

◆ operator=() [1/2]

csvm& plssvm::csvm::operator= ( const csvm & )

delete

Delete copy-assignment operator since a CSVM is a move-only type.

Returns: *this

◆ operator=() [2/2]

csvm& plssvm::csvm::operator= ( csvm && )

defaultnoexcept

Default move-assignment operator since a virtual destructor has been declared.

Returns: *this

◆ get_target_platform()

target_platform plssvm::csvm::get_target_platform ( ) const

inlinenoexcept

Return the target platform (i.e, CPU or GPU including the vendor) this SVM runs on.

Returns: the target platform ([[nodiscard]])

◆ get_params()

parameter plssvm::csvm::get_params ( ) const

inlinenoexcept

Return the currently used SVM parameter.

Returns: the SVM parameter ([[nodiscard]])

◆ set_params() [1/2]

void plssvm::csvm::set_params ( parameter params )

inlinenoexcept

Override the old SVM parameter with the new plssvm::parameter params.

Parameters

[in] params the new SVM parameter to use

◆ set_params() [2/2]

template<typename... Args, std::enable_if_t< detail::has_only_parameter_named_args_v< Args... >, bool > >

void plssvm::csvm::set_params ( Args &&... named_args )

Override the old SVM parameter with the new ones given as named parameters in named_args.

Template Parameters

Args	the type of the named-parameters

Parameters

[in] named_args the potential named-parameters

◆ fit()

template<typename real_type , typename label_type , typename... Args>

model< real_type, label_type > plssvm::csvm::fit	(	const data_set< real_type, label_type > &	data,
		Args &&...	named_args
	)		const

Fit a model using the current SVM on the data.

Template Parameters

real_type	the type of the data (`float` or `double`)
label_type	the type of the label (an arithmetic type or `std::string`)
Args	the type of the potential additional parameters

Parameters

[in]	data	the data used to train the SVM model
[in]	named_args	the potential additional parameters (`epsilon` and/or `max_iter`)

Exceptions

plssvm::invalid_parameter_exception	if the provided value for `epsilon` is greater or equal than zero
plssvm::invlaid_parameter_exception	if the provided maximum number of iterations is less or equal than zero
plssvm::invalid_parameter_exception	if the training `data` does not include labels
plssvm::exception	any exception thrown in the respective backend's implementation of `plssvm::csvm::solve_system_of_linear_equations`

Returns: the learned model ([[nodiscard]])

◆ predict()

template<typename real_type , typename label_type >

std::vector< label_type > plssvm::csvm::predict	(	const model< real_type, label_type > &	model,
		const data_set< real_type, label_type > &	data
	)		const

Predict the labels for the data set using the model.

Template Parameters

real_type	the type of the data (`float` or `double`)
label_type	the type of the label (an arithmetic type or `std::string`)

Parameters

[in]	model	a previously learned model
[in]	data	the data to predict the labels for

Exceptions

plssvm::invalid_parameter_exception	if the number of features in the `model's` support vectors don't match the number of features in the `data` set
plssvm::exception	any exception thrown in the respective backend's implementation of `plssvm::csvm::predict_values`

Returns: the predicted labels ([[nodiscard]])

Examples: csvm_examples.cpp.

◆ score() [1/2]

template<typename real_type , typename label_type >

real_type plssvm::csvm::score ( const model< real_type, label_type > & model ) const

Calculate the accuracy of the model.

Template Parameters

real_type	the type of the data (`float` or `double`)
label_type	the type of the label (an arithmetic type or `std::string`)

Parameters

[in] model a previously learned model

Exceptions

plssvm::exception any exception thrown in the respective backend's implementation of plssvm::csvm::predict_values

Returns: the accuracy of the model ([[nodiscard]])

◆ score() [2/2]

template<typename real_type , typename label_type >

real_type plssvm::csvm::score	(	const model< real_type, label_type > &	model,
		const data_set< real_type, label_type > &	data
	)		const

Calculate the accuracy of the labeled data set using the model.

Template Parameters

real_type	the type of the data (`float` or `double`)
label_type	the type of the label (an arithmetic type or `std::string`)

Parameters

[in]	model	a previously learned model
[in]	data	the labeled data set to score

Exceptions

plssvm::invalid_parameter_exception	if the `data` to score has no labels
plssvm::invalid_parameter_exception	if the number of features in the `model's` support vectors don't match the number of features in the `data` set
plssvm::exception	any exception thrown in the respective backend's implementation of `plssvm::csvm::predict_values`

Returns: the accuracy of the labeled data ([[nodiscard]])

◆ solve_system_of_linear_equations() [1/2]

virtual std::pair<std::vector<float>, float> plssvm::csvm::solve_system_of_linear_equations	(	const detail::parameter< float > &	params,
		const std::vector< std::vector< float >> &	A,
		std::vector< float >	b,
		float	eps,
		unsigned long long	max_iter
	)		const

protectedpure virtual

Solves the equation \(Ax = b\) using the Conjugated Gradients algorithm.

Uses a slightly modified version of the CG algorithm described by Jonathan Richard Shewchuk:

Parameters

[in]	params	the SVM parameters used in the respective kernel functions
[in]	A	the matrix of the equation \(Ax = b\) (symmetric positive definite)
[in]	b	the right-hand side of the equation \(Ax = b\)
[in]	eps	the error tolerance
[in]	max_iter	the maximum number of CG iterations

Exceptions

plssvm::exception any exception thrown by the backend's implementation

Returns: a pair of [the result vector x, the resulting bias] ([[nodiscard]])

Implemented in plssvm::detail::gpu_csvm< device_ptr_t, queue_t >, plssvm::detail::gpu_csvm< detail::device_ptr, int >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::command_queue >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::queue >, and plssvm::openmp::csvm.

◆ solve_system_of_linear_equations() [2/2]

virtual std::pair<std::vector<double>, double> plssvm::csvm::solve_system_of_linear_equations	(	const detail::parameter< double > &	params,
		const std::vector< std::vector< double >> &	A,
		std::vector< double >	b,
		double	eps,
		unsigned long long	max_iter
	)		const

protectedpure virtual

Solves the equation \(Ax = b\) using the Conjugated Gradients algorithm.

Uses a slightly modified version of the CG algorithm described by Jonathan Richard Shewchuk:

Parameters

[in]	params	the SVM parameters used in the respective kernel functions
[in]	A	the matrix of the equation \(Ax = b\) (symmetric positive definite)
[in]	b	the right-hand side of the equation \(Ax = b\)
[in]	eps	the error tolerance
[in]	max_iter	the maximum number of CG iterations

Exceptions

plssvm::exception any exception thrown by the backend's implementation

Returns: a pair of [the result vector x, the resulting bias] ([[nodiscard]])

Implemented in plssvm::detail::gpu_csvm< device_ptr_t, queue_t >, plssvm::detail::gpu_csvm< detail::device_ptr, int >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::command_queue >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::queue >, and plssvm::openmp::csvm.

◆ predict_values() [1/2]

virtual std::vector<float> plssvm::csvm::predict_values	(	const detail::parameter< float > &	params,
		const std::vector< std::vector< float >> &	support_vectors,
		const std::vector< float > &	alpha,
		float	rho,
		std::vector< float > &	w,
		const std::vector< std::vector< float >> &	predict_points
	)		const

protectedpure virtual

Uses the already learned model to predict the class of multiple (new) data points.

Parameters

[in]	params	the SVM parameters used in the respective kernel functions
[in]	support_vectors	the previously learned support vectors
[in]	alpha	the alpha values (weights) associated with the support vectors
[in]	rho	the rho value determined after training the model
[in,out]	w	the normal vector to speedup prediction in case of the linear kernel function, an empty vector in case of the polynomial or rbf kernel
[in]	predict_points	the points to predict

Exceptions

plssvm::exception any exception thrown by the backend's implementation

Returns: a vector filled with the predictions (not the actual labels!) ([[nodiscard]])

Implemented in plssvm::detail::gpu_csvm< device_ptr_t, queue_t >, plssvm::detail::gpu_csvm< detail::device_ptr, int >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::command_queue >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::queue >, and plssvm::openmp::csvm.

◆ predict_values() [2/2]

virtual std::vector<double> plssvm::csvm::predict_values	(	const detail::parameter< double > &	params,
		const std::vector< std::vector< double >> &	support_vectors,
		const std::vector< double > &	alpha,
		double	rho,
		std::vector< double > &	w,
		const std::vector< std::vector< double >> &	predict_points
	)		const

protectedpure virtual

Uses the already learned model to predict the class of multiple (new) data points.

Parameters

[in]	params	the SVM parameters used in the respective kernel functions
[in]	support_vectors	the previously learned support vectors
[in]	alpha	the alpha values (weights) associated with the support vectors
[in]	rho	the rho value determined after training the model
[in,out]	w	the normal vector to speedup prediction in case of the linear kernel function, an empty vector in case of the polynomial or rbf kernel
[in]	predict_points	the points to predict

Exceptions

plssvm::exception any exception thrown by the backend's implementation

Returns: a vector filled with the predictions (not the actual labels!) ([[nodiscard]])

Implemented in plssvm::detail::gpu_csvm< device_ptr_t, queue_t >, plssvm::detail::gpu_csvm< detail::device_ptr, int >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::command_queue >, plssvm::detail::gpu_csvm< detail::device_ptr, detail::queue >, and plssvm::openmp::csvm.

◆ sanity_check_parameter()

void plssvm::csvm::sanity_check_parameter ( ) const

inlineprivate

Perform some sanity checks on the passed SVM parameters.

Exceptions

plssvm::invalid_parameter_exception	if the kernel function is invalid
plssvm::invalid_parameter_exception	if the gamma value for the polynomial or radial basis function kernel is not greater than zero

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

include/plssvm/csvm.hpp

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ csvm() [1/2]

◆ csvm() [2/2]

Member Function Documentation

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ get_target_platform()

◆ get_params()

◆ set_params() [1/2]

◆ set_params() [2/2]

◆ fit()

◆ predict()

◆ score() [1/2]

◆ score() [2/2]

◆ solve_system_of_linear_equations() [1/2]

◆ solve_system_of_linear_equations() [2/2]

◆ predict_values() [1/2]

◆ predict_values() [2/2]

◆ sanity_check_parameter()