A C-SVM implementation using OpenMP as backend. More...

#include <csvm.hpp>

Inheritance diagram for plssvm::openmp::csvm:

Collaboration diagram for plssvm::openmp::csvm:

Public Member Functions
	csvm (parameter params={})
	Construct a new C-SVM using the OpenMP backend with the parameters given through `params`. More...

	csvm (target_platform target, parameter params={})
	Construct a new C-SVM using the OpenMP backend on the `target` platform with the parameters given through `params`. More...

template<typename... Args, PLSSVM_REQUIRES(detail::has_only_parameter_named_args_v< Args... >) >
	csvm (Args &&...named_args)
	Construct a new C-SVM using the OpenMP backend and the optionally provided `named_args`. More...

template<typename... Args, PLSSVM_REQUIRES(detail::has_only_parameter_named_args_v< Args... >) >
	csvm (const target_platform target, Args &&...named_args)
	Construct a new C-SVM using the OpenMP backend on the `target` platform and the optionally provided `named_args`. More...

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

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

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. noexcept More...

	~csvm () override=default
	Default destructor since the copy and move constructors and copy- and move-assignment operators are defined.

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

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

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

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

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

template<typename real_type >
std::vector< real_type >	predict_values_impl (const detail::parameter< real_type > &params, const std::vector< std::vector< real_type >> &support_vectors, const std::vector< real_type > &alpha, real_type rho, std::vector< real_type > &w, const std::vector< std::vector< real_type >> &predict_points) const
	Uses the already learned model to predict the class of multiple (new) data points. More...

template<typename real_type >
std::vector< real_type >	generate_q (const detail::parameter< real_type > &params, const std::vector< std::vector< real_type >> &data) const
	Calculate the `q` vector used in the dimensional reduction. More...

template<typename real_type >
std::vector< real_type >	calculate_w (const std::vector< std::vector< real_type >> &support_vectors, const std::vector< real_type > &alpha) const
	Precalculate the `w` vector to speedup up the prediction using the linear kernel function. More...

template<typename real_type >
void	run_device_kernel (const detail::parameter< real_type > &params, const std::vector< real_type > &q, std::vector< real_type > &ret, const std::vector< real_type > &d, const std::vector< std::vector< real_type >> &data, real_type QA_cost, real_type add) const
	Select the correct kernel based on the value of plssvm::parameter::kernel_type and run it on the CPU using OpenMP. More...

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

Private Member Functions
void	init (target_platform target)
	Initializes the OpenMP backend and performs some sanity checks. More...

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

A C-SVM implementation using OpenMP as backend.

Constructor & Destructor Documentation

◆ csvm() [1/6]

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

explicit

Construct a new C-SVM using the OpenMP backend with the parameters given through params.

Parameters

[in] params struct encapsulating all possible SVM parameters

Exceptions

plssvm::exception	all exceptions thrown in the base class constructor
plssvm::openmp::backend_exception	if the target platform isn't plssvm::target_platform::automatic or plssvm::target_platform::cpu
plssvm::openmp::backend_exception	if the plssvm::target_platform::cpu target isn't available

◆ csvm() [2/6]

plssvm::openmp::csvm::csvm	(	target_platform	target,
		parameter	params = `{}`
	)

explicit

Construct a new C-SVM using the OpenMP backend on the target platform with the parameters given through params.

Parameters

[in]	target	the target platform used for this C-SVM
[in]	params	struct encapsulating all possible SVM parameters

Exceptions

plssvm::exception	all exceptions thrown in the base class constructor
plssvm::openmp::backend_exception	if the target platform isn't plssvm::target_platform::automatic or plssvm::target_platform::cpu
plssvm::openmp::backend_exception	if the plssvm::target_platform::cpu target isn't available

◆ csvm() [3/6]

template<typename... Args, PLSSVM_REQUIRES(detail::has_only_parameter_named_args_v< Args... >) >

plssvm::openmp::csvm::csvm ( Args &&... named_args )

inlineexplicit

Construct a new C-SVM using the OpenMP backend and the optionally provided named_args.

Parameters

[in] named_args the additional optional named-parameters

Exceptions

plssvm::exception	all exceptions thrown in the base class constructor
plssvm::openmp::backend_exception	if the target platform isn't plssvm::target_platform::automatic or plssvm::target_platform::cpu
plssvm::openmp::backend_exception	if the plssvm::target_platform::cpu target isn't available

◆ csvm() [4/6]

template<typename... Args, PLSSVM_REQUIRES(detail::has_only_parameter_named_args_v< Args... >) >

plssvm::openmp::csvm::csvm	(	const target_platform	target,
		Args &&...	named_args
	)

inlineexplicit

Construct a new C-SVM using the OpenMP backend on the target platform and the optionally provided named_args.

Parameters

[in]	target	the target platform used for this C-SVM
[in]	named_args	the additional optional named-parameters

Exceptions

plssvm::exception	all exceptions thrown in the base class constructor
plssvm::openmp::backend_exception	if the target platform isn't plssvm::target_platform::automatic or plssvm::target_platform::cpu
plssvm::openmp::backend_exception	if the plssvm::target_platform::cpu target isn't available

◆ csvm() [5/6]

plssvm::openmp::csvm::csvm ( const csvm & )

delete

Delete copy-constructor since a CSVM is a move-only type.

◆ csvm() [6/6]

plssvm::openmp::csvm::csvm ( csvm && )

defaultnoexcept

Default move-constructor since a virtual destructor has been declared. noexcept

noexcept

Member Function Documentation

◆ operator=() [1/2]

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

delete

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

Returns: *this

◆ operator=() [2/2]

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

defaultnoexcept

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

Returns: *this noexcept

◆ solve_system_of_linear_equations() [1/2]

std::pair<std::vector<float>, float> plssvm::openmp::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

inlineoverrideprotectedvirtual

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]])

Implements plssvm::csvm.

◆ solve_system_of_linear_equations() [2/2]

std::pair<std::vector<double>, double> plssvm::openmp::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

inlineoverrideprotectedvirtual

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]])

Implements plssvm::csvm.

◆ solve_system_of_linear_equations_impl()

template<typename real_type >

std::pair<std::vector<real_type>, real_type> plssvm::openmp::csvm::solve_system_of_linear_equations_impl	(	const detail::parameter< real_type > &	params,
		const std::vector< std::vector< real_type >> &	A,
		std::vector< real_type >	b,
		real_type	eps,
		unsigned long long	max_iter
	)		const

protected

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]])

◆ predict_values() [1/2]

std::vector<float> plssvm::openmp::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

inlineoverrideprotectedvirtual

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]])

Implements plssvm::csvm.

◆ predict_values() [2/2]

std::vector<double> plssvm::openmp::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

inlineoverrideprotectedvirtual

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]])

Implements plssvm::csvm.

◆ predict_values_impl()

template<typename real_type >

std::vector<real_type> plssvm::openmp::csvm::predict_values_impl	(	const detail::parameter< real_type > &	params,
		const std::vector< std::vector< real_type >> &	support_vectors,
		const std::vector< real_type > &	alpha,
		real_type	rho,
		std::vector< real_type > &	w,
		const std::vector< std::vector< real_type >> &	predict_points
	)		const

protected

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]])

◆ generate_q()

template<typename real_type >

std::vector<real_type> plssvm::openmp::csvm::generate_q	(	const detail::parameter< real_type > &	params,
		const std::vector< std::vector< real_type >> &	data
	)		const

protected

Calculate the q vector used in the dimensional reduction.

The template parameter real_type represents the type of the data points (either float or double).

Parameters

[in]	params	the SVM parameter used to calculate `q` (e.g., kernel_type)
[in]	data	the data points used in the dimensional reduction.

Returns: the q vector ([[nodiscard]])

◆ calculate_w()

template<typename real_type >

std::vector<real_type> plssvm::openmp::csvm::calculate_w	(	const std::vector< std::vector< real_type >> &	support_vectors,
		const std::vector< real_type > &	alpha
	)		const

protected

Precalculate the w vector to speedup up the prediction using the linear kernel function.

The template parameter real_type represents the type of the data points (either float or double).

Parameters

[in]	support_vectors	the previously learned support vectors
[in]	alpha	the previously learned weights

Returns: the w vector ([[nodiscard]])

◆ run_device_kernel()

template<typename real_type >

void plssvm::openmp::csvm::run_device_kernel	(	const detail::parameter< real_type > &	params,
		const std::vector< real_type > &	q,
		std::vector< real_type > &	ret,
		const std::vector< real_type > &	d,
		const std::vector< std::vector< real_type >> &	data,
		real_type	QA_cost,
		real_type	add
	)		const

protected

Select the correct kernel based on the value of plssvm::parameter::kernel_type and run it on the CPU using OpenMP.

The template parameter real_type represents the type of the data points (either float or double).

Parameters

[in]	params	the SVM parameter used to calculate `q` (e.g., kernel_type)
[in]	q	the `q` vector used in the dimensional reduction
[out]	ret	the result vector
[in]	d	the right-hand side of the equation
[in]	data	the data points
[in]	QA_cost	a value used in the dimensional reduction
[in]	add	denotes whether the values are added or subtracted from the result vector

◆ init()

void plssvm::openmp::csvm::init ( target_platform target )

private

Initializes the OpenMP backend and performs some sanity checks.

Parameters

[in] target the target platform to use

Exceptions

plssvm::openmp::backend_exception	if the target platform isn't plssvm::target_platform::automatic or plssvm::target_platform::cpu
plssvm::openmp::backend_exception	if the plssvm::target_platform::cpu target isn't available

◆ get_target_platform()

target_platform plssvm::csvm::get_target_platform ( ) const

inlinenoexceptinherited

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

inlinenoexceptinherited

Return the currently used SVM parameter.

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

◆ set_params() [1/2]

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

inlinenoexceptinherited

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 )

inherited

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

inherited

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

inherited

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

inherited

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

inherited

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]])

◆ sanity_check_parameter()

void plssvm::csvm::sanity_check_parameter ( ) const

inlineprivateinherited

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/backends/OpenMP/csvm.hpp

Public Member Functions

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ csvm() [1/6]

◆ csvm() [2/6]

◆ csvm() [3/6]

◆ csvm() [4/6]

◆ csvm() [5/6]

◆ csvm() [6/6]

Member Function Documentation

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ solve_system_of_linear_equations() [1/2]

◆ solve_system_of_linear_equations() [2/2]

◆ solve_system_of_linear_equations_impl()

◆ predict_values() [1/2]

◆ predict_values() [2/2]

◆ predict_values_impl()

◆ generate_q()

◆ calculate_w()

◆ run_device_kernel()

◆ init()

◆ get_target_platform()

◆ get_params()

◆ set_params() [1/2]

◆ set_params() [2/2]

◆ fit()

◆ predict()

◆ score() [1/2]

◆ score() [2/2]

◆ sanity_check_parameter()