Data converter between libtorch tensors and SVM library formats. More...

#include <data_converter.hpp>

Public Member Functions
	DataConverter ()
	Default constructor.

	~DataConverter ()
	Destructor - cleans up allocated memory.

std::unique_ptr< svm_problem >	to_svm_problem (const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
	Convert PyTorch tensors to libsvm format.

std::unique_ptr< problem >	to_linear_problem (const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
	Convert PyTorch tensors to liblinear format.

svm_node *	to_svm_node (const torch::Tensor &sample)
	Convert single sample to libsvm format.

feature_node *	to_feature_node (const torch::Tensor &sample)
	Convert single sample to liblinear format.

torch::Tensor	from_predictions (const std::vector< double > &predictions)
	Convert predictions back to PyTorch tensor.

torch::Tensor	from_probabilities (const std::vector< std::vector< double > > &probabilities)
	Convert probabilities back to PyTorch tensor.

torch::Tensor	from_decision_values (const std::vector< std::vector< double > > &decision_values)
	Convert decision values back to PyTorch tensor.

void	validate_tensors (const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
	Validate input tensors.

int	get_n_features () const
	Get number of features from last conversion.

int	get_n_samples () const
	Get number of samples from last conversion.

void	cleanup ()
	Clean up all allocated memory.

void	set_sparse_threshold (double threshold)
	Set sparse threshold (features with absolute value below this are ignored)

double	get_sparse_threshold () const
	Get sparse threshold.

	DataConverter ()
	Default constructor.

	~DataConverter ()
	Destructor - cleans up allocated memory.

std::unique_ptr< svm_problem >	to_svm_problem (const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
	Convert PyTorch tensors to libsvm format.

std::unique_ptr< problem >	to_linear_problem (const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
	Convert PyTorch tensors to liblinear format.

svm_node *	to_svm_node (const torch::Tensor &sample)
	Convert single sample to libsvm format.

feature_node *	to_feature_node (const torch::Tensor &sample)
	Convert single sample to liblinear format.

torch::Tensor	from_predictions (const std::vector< double > &predictions)
	Convert predictions back to PyTorch tensor.

torch::Tensor	from_probabilities (const std::vector< std::vector< double > > &probabilities)
	Convert probabilities back to PyTorch tensor.

torch::Tensor	from_decision_values (const std::vector< std::vector< double > > &decision_values)
	Convert decision values back to PyTorch tensor.

void	validate_tensors (const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
	Validate input tensors.

int	get_n_features () const
	Get number of features from last conversion.

int	get_n_samples () const
	Get number of samples from last conversion.

void	cleanup ()
	Clean up all allocated memory.

void	set_sparse_threshold (double threshold)
	Set sparse threshold (features with absolute value below this are ignored)

double	get_sparse_threshold () const
	Get sparse threshold.

Detailed Description

Data converter between libtorch tensors and SVM library formats.

This class handles the conversion between PyTorch tensors and the data structures required by libsvm and liblinear libraries. It manages memory allocation and provides efficient conversion methods.

Definition at line 23 of file data_converter.hpp.

Constructor & Destructor Documentation

◆ DataConverter()

svm_classifier::DataConverter::DataConverter ( )

Default constructor.

Definition at line 10 of file data_converter.cpp.

◆ ~DataConverter()

svm_classifier::DataConverter::~DataConverter ( )

Destructor - cleans up allocated memory.

Definition at line 17 of file data_converter.cpp.

Member Function Documentation

◆ cleanup()

void svm_classifier::DataConverter::cleanup ( )

Clean up all allocated memory.

Definition at line 198 of file data_converter.cpp.

◆ from_decision_values() [1/2]

torch::Tensor svm_classifier::DataConverter::from_decision_values ( const std::vector< std::vector< double > > & decision_values )

Convert decision values back to PyTorch tensor.

Parameters

decision_values 2D vector of decision function values

Returns: PyTorch tensor with decision values

Definition at line 152 of file data_converter.cpp.

◆ from_decision_values() [2/2]

torch::Tensor svm_classifier::DataConverter::from_decision_values ( const std::vector< std::vector< double > > & decision_values )

Convert decision values back to PyTorch tensor.

Parameters

decision_values 2D vector of decision function values

Returns: PyTorch tensor with decision values

◆ from_predictions() [1/2]

torch::Tensor svm_classifier::DataConverter::from_predictions ( const std::vector< double > & predictions )

Convert predictions back to PyTorch tensor.

Parameters

predictions Vector of predictions

Returns: PyTorch tensor with predictions

Definition at line 120 of file data_converter.cpp.

◆ from_predictions() [2/2]

torch::Tensor svm_classifier::DataConverter::from_predictions ( const std::vector< double > & predictions )

Convert predictions back to PyTorch tensor.

Parameters

predictions Vector of predictions

Returns: PyTorch tensor with predictions

◆ from_probabilities() [1/2]

torch::Tensor svm_classifier::DataConverter::from_probabilities ( const std::vector< std::vector< double > > & probabilities )

Convert probabilities back to PyTorch tensor.

Parameters

probabilities 2D vector of class probabilities

Returns: PyTorch tensor with probabilities of shape (n_samples, n_classes)

Definition at line 132 of file data_converter.cpp.

◆ from_probabilities() [2/2]

torch::Tensor svm_classifier::DataConverter::from_probabilities ( const std::vector< std::vector< double > > & probabilities )

Convert probabilities back to PyTorch tensor.

Parameters

probabilities 2D vector of class probabilities

Returns: PyTorch tensor with probabilities of shape (n_samples, n_classes)

◆ get_n_features() [1/2]

int svm_classifier::DataConverter::get_n_features ( ) const

inline

Get number of features from last conversion.

Returns: Number of features

Definition at line 100 of file data_converter.hpp.

◆ get_n_features() [2/2]

int svm_classifier::DataConverter::get_n_features ( ) const

inline

Get number of features from last conversion.

Returns: Number of features

Definition at line 100 of file kernel_parameters.hpp.

◆ get_n_samples() [1/2]

int svm_classifier::DataConverter::get_n_samples ( ) const

inline

Get number of samples from last conversion.

Returns: Number of samples

Definition at line 106 of file data_converter.hpp.

◆ get_n_samples() [2/2]

int svm_classifier::DataConverter::get_n_samples ( ) const

inline

Get number of samples from last conversion.

Returns: Number of samples

Definition at line 106 of file kernel_parameters.hpp.

◆ get_sparse_threshold() [1/2]

double svm_classifier::DataConverter::get_sparse_threshold ( ) const

inline

Get sparse threshold.

Returns: Current sparse threshold

Definition at line 123 of file data_converter.hpp.

◆ get_sparse_threshold() [2/2]

double svm_classifier::DataConverter::get_sparse_threshold ( ) const

inline

Get sparse threshold.

Returns: Current sparse threshold

Definition at line 123 of file kernel_parameters.hpp.

◆ set_sparse_threshold() [1/2]

void svm_classifier::DataConverter::set_sparse_threshold ( double threshold )

inline

Set sparse threshold (features with absolute value below this are ignored)

Parameters

threshold Sparse threshold (default: 1e-8)

Definition at line 117 of file data_converter.hpp.

◆ set_sparse_threshold() [2/2]

void svm_classifier::DataConverter::set_sparse_threshold ( double threshold )

inline

Set sparse threshold (features with absolute value below this are ignored)

Parameters

threshold Sparse threshold (default: 1e-8)

Definition at line 117 of file kernel_parameters.hpp.

◆ to_feature_node() [1/2]

feature_node * svm_classifier::DataConverter::to_feature_node ( const torch::Tensor & sample )

Convert single sample to liblinear format.

Parameters

sample Feature tensor of shape (n_features,)

Returns: Pointer to feature_node array

Definition at line 110 of file data_converter.cpp.

◆ to_feature_node() [2/2]

feature_node * svm_classifier::DataConverter::to_feature_node ( const torch::Tensor & sample )

Convert single sample to liblinear format.

Parameters

sample Feature tensor of shape (n_features,)

Returns: Pointer to feature_node array

◆ to_linear_problem() [1/2]

std::unique_ptr< problem > svm_classifier::DataConverter::to_linear_problem	(	const torch::Tensor &	X,
		const torch::Tensor &	y = `torch::Tensor()`
	)

Convert PyTorch tensors to liblinear format.

Parameters

X	Feature tensor of shape (n_samples, n_features)
y	Target tensor of shape (n_samples,) - optional for prediction

Returns: Pointer to problem structure

Definition at line 60 of file data_converter.cpp.

◆ to_linear_problem() [2/2]

std::unique_ptr< problem > svm_classifier::DataConverter::to_linear_problem	(	const torch::Tensor &	X,
		const torch::Tensor &	y = `torch::Tensor()`
	)

Convert PyTorch tensors to liblinear format.

Parameters

X	Feature tensor of shape (n_samples, n_features)
y	Target tensor of shape (n_samples,) - optional for prediction

Returns: Pointer to problem structure

◆ to_svm_node() [1/2]

svm_node * svm_classifier::DataConverter::to_svm_node ( const torch::Tensor & sample )

Convert single sample to libsvm format.

Parameters

sample Feature tensor of shape (n_features,)

Returns: Pointer to svm_node array

Definition at line 100 of file data_converter.cpp.

◆ to_svm_node() [2/2]

svm_node * svm_classifier::DataConverter::to_svm_node ( const torch::Tensor & sample )

Convert single sample to libsvm format.

Parameters

sample Feature tensor of shape (n_features,)

Returns: Pointer to svm_node array

◆ to_svm_problem() [1/2]

std::unique_ptr< svm_problem > svm_classifier::DataConverter::to_svm_problem	(	const torch::Tensor &	X,
		const torch::Tensor &	y = `torch::Tensor()`
	)

Convert PyTorch tensors to libsvm format.

Parameters

X	Feature tensor of shape (n_samples, n_features)
y	Target tensor of shape (n_samples,) - optional for prediction

Returns: Pointer to svm_problem structure

Definition at line 22 of file data_converter.cpp.

◆ to_svm_problem() [2/2]

std::unique_ptr< svm_problem > svm_classifier::DataConverter::to_svm_problem	(	const torch::Tensor &	X,
		const torch::Tensor &	y = `torch::Tensor()`
	)

Convert PyTorch tensors to libsvm format.

Parameters

X	Feature tensor of shape (n_samples, n_features)
y	Target tensor of shape (n_samples,) - optional for prediction

Returns: Pointer to svm_problem structure

◆ validate_tensors() [1/2]

void svm_classifier::DataConverter::validate_tensors	(	const torch::Tensor &	X,
		const torch::Tensor &	y = `torch::Tensor()`
	)

Validate input tensors.

Parameters

X	Feature tensor
y	Target tensor (optional)

Exceptions

std::invalid_argument if tensors are invalid

Definition at line 172 of file data_converter.cpp.

◆ validate_tensors() [2/2]

void svm_classifier::DataConverter::validate_tensors	(	const torch::Tensor &	X,
		const torch::Tensor &	y = `torch::Tensor()`
	)

Validate input tensors.

Parameters

X	Feature tensor
y	Target tensor (optional)

Exceptions

std::invalid_argument if tensors are invalid

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

include/svm_classifier/data_converter.hpp
include/svm_classifier/kernel_parameters.hpp
src/data_converter.cpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ DataConverter()

◆ ~DataConverter()

Member Function Documentation

◆ cleanup()

◆ from_decision_values() [1/2]

◆ from_decision_values() [2/2]

◆ from_predictions() [1/2]

◆ from_predictions() [2/2]

◆ from_probabilities() [1/2]

◆ from_probabilities() [2/2]

◆ get_n_features() [1/2]

◆ get_n_features() [2/2]

◆ get_n_samples() [1/2]

◆ get_n_samples() [2/2]

◆ get_sparse_threshold() [1/2]

◆ get_sparse_threshold() [2/2]

◆ set_sparse_threshold() [1/2]

◆ set_sparse_threshold() [2/2]

◆ to_feature_node() [1/2]

◆ to_feature_node() [2/2]

◆ to_linear_problem() [1/2]

◆ to_linear_problem() [2/2]

◆ to_svm_node() [1/2]

◆ to_svm_node() [2/2]

◆ to_svm_problem() [1/2]

◆ to_svm_problem() [2/2]

◆ validate_tensors() [1/2]

◆ validate_tensors() [2/2]