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+SVM Classifier C++: Class List + + + + + + + + + +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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Here are the classes, structs, unions and interfaces with brief descriptions:
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[detail level 12]
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 ▼Nsvm_classifier
 CDataConverterData converter between libtorch tensors and SVM library formats
 CEvaluationMetricsModel evaluation metrics
 CMulticlassStrategyBaseAbstract base class for multiclass classification strategies
 COneVsOneStrategyOne-vs-One (OvO) multiclass strategy
 COneVsRestStrategyOne-vs-Rest (OvR) multiclass strategy
 CPredictionResultPrediction result structure
 CSVMClassifierSupport Vector Machine Classifier with scikit-learn compatible API
 CTrainingMetricsTraining metrics structure
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+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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Class Index
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D | E | M | O | P | S | T
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D
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DataConverter (svm_classifier)
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E
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EvaluationMetrics (svm_classifier)
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M
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MulticlassStrategyBase (svm_classifier)
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O
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OneVsOneStrategy (svm_classifier)
OneVsRestStrategy (svm_classifier)
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PredictionResult (svm_classifier)
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SVMClassifier (svm_classifier)
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T
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TrainingMetrics (svm_classifier)
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+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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svm_classifier::DataConverter Member List
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This is the complete list of members for svm_classifier::DataConverter, including all inherited members.

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cleanup()svm_classifier::DataConverter
cleanup()svm_classifier::DataConverter
DataConverter()svm_classifier::DataConverter
DataConverter()svm_classifier::DataConverter
from_decision_values(const std::vector< std::vector< double > > &decision_values)svm_classifier::DataConverter
from_decision_values(const std::vector< std::vector< double > > &decision_values)svm_classifier::DataConverter
from_predictions(const std::vector< double > &predictions)svm_classifier::DataConverter
from_predictions(const std::vector< double > &predictions)svm_classifier::DataConverter
from_probabilities(const std::vector< std::vector< double > > &probabilities)svm_classifier::DataConverter
from_probabilities(const std::vector< std::vector< double > > &probabilities)svm_classifier::DataConverter
get_n_features() constsvm_classifier::DataConverterinline
get_n_features() constsvm_classifier::DataConverterinline
get_n_samples() constsvm_classifier::DataConverterinline
get_n_samples() constsvm_classifier::DataConverterinline
get_sparse_threshold() constsvm_classifier::DataConverterinline
get_sparse_threshold() constsvm_classifier::DataConverterinline
set_sparse_threshold(double threshold)svm_classifier::DataConverterinline
set_sparse_threshold(double threshold)svm_classifier::DataConverterinline
to_feature_node(const torch::Tensor &sample)svm_classifier::DataConverter
to_feature_node(const torch::Tensor &sample)svm_classifier::DataConverter
to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())svm_classifier::DataConverter
to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())svm_classifier::DataConverter
to_svm_node(const torch::Tensor &sample)svm_classifier::DataConverter
to_svm_node(const torch::Tensor &sample)svm_classifier::DataConverter
to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())svm_classifier::DataConverter
to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())svm_classifier::DataConverter
validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())svm_classifier::DataConverter
validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())svm_classifier::DataConverter
~DataConverter()svm_classifier::DataConverter
~DataConverter()svm_classifier::DataConverter
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SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
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+Public Member Functions | +List of all members
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svm_classifier::DataConverter Class Reference
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Data converter between libtorch tensors and SVM library formats. + More...

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#include <data_converter.hpp>

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+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_values2D 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_values2D 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
+ + +
predictionsVector 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
+ + +
predictionsVector 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
+ + +
probabilities2D 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
+ + +
probabilities2D 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
+ + +
thresholdSparse 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
+ + +
thresholdSparse 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
+ + +
sampleFeature 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
+ + +
sampleFeature 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
+ + + +
XFeature tensor of shape (n_samples, n_features)
yTarget 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
+ + + +
XFeature tensor of shape (n_samples, n_features)
yTarget 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
+ + +
sampleFeature 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
+ + +
sampleFeature 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
+ + + +
XFeature tensor of shape (n_samples, n_features)
yTarget 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
+ + + +
XFeature tensor of shape (n_samples, n_features)
yTarget 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
+ + + +
XFeature tensor
yTarget tensor (optional)
+
+
+
Exceptions
+ + +
std::invalid_argumentif 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
+ + + +
XFeature tensor
yTarget tensor (optional)
+
+
+
Exceptions
+ + +
std::invalid_argumentif tensors are invalid
+
+
+ +
+
+
The documentation for this class was generated from the following files: +
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/classsvm__classifier_1_1MulticlassStrategyBase-members.html b/classsvm__classifier_1_1MulticlassStrategyBase-members.html new file mode 100644 index 0000000..2203727 --- /dev/null +++ b/classsvm__classifier_1_1MulticlassStrategyBase-members.html @@ -0,0 +1,100 @@ + + + + + + + +SVM Classifier C++: Member List + + + + + + + + + +
+
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+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
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svm_classifier::MulticlassStrategyBase Member List
+
+
+ +

This is the complete list of members for svm_classifier::MulticlassStrategyBase, including all inherited members.

+ + + + + + + + + + + + +
classes_svm_classifier::MulticlassStrategyBaseprotected
decision_function(const torch::Tensor &X, DataConverter &converter)=0svm_classifier::MulticlassStrategyBasepure virtual
fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter)=0svm_classifier::MulticlassStrategyBasepure virtual
get_classes() const =0svm_classifier::MulticlassStrategyBasepure virtual
get_n_classes() const =0svm_classifier::MulticlassStrategyBasepure virtual
get_strategy_type() const =0svm_classifier::MulticlassStrategyBasepure virtual
is_trained_svm_classifier::MulticlassStrategyBaseprotected
predict(const torch::Tensor &X, DataConverter &converter)=0svm_classifier::MulticlassStrategyBasepure virtual
predict_proba(const torch::Tensor &X, DataConverter &converter)=0svm_classifier::MulticlassStrategyBasepure virtual
supports_probability() const =0svm_classifier::MulticlassStrategyBasepure virtual
~MulticlassStrategyBase()=defaultsvm_classifier::MulticlassStrategyBasevirtual
+ +
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+ + diff --git a/classsvm__classifier_1_1MulticlassStrategyBase.html b/classsvm__classifier_1_1MulticlassStrategyBase.html new file mode 100644 index 0000000..5056f6d --- /dev/null +++ b/classsvm__classifier_1_1MulticlassStrategyBase.html @@ -0,0 +1,535 @@ + + + + + + + +SVM Classifier C++: svm_classifier::MulticlassStrategyBase Class Reference + + + + + + + + + +
+
+ + + + + + +
+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
+
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+Public Member Functions | +Protected Attributes | +List of all members
+
svm_classifier::MulticlassStrategyBase Class Referenceabstract
+
+
+ +

Abstract base class for multiclass classification strategies. + More...

+ +

#include <multiclass_strategy.hpp>

+
+Inheritance diagram for svm_classifier::MulticlassStrategyBase:
+
+
Inheritance graph
+ + + + + + + +
[legend]
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+Public Member Functions

+virtual ~MulticlassStrategyBase ()=default
 Virtual destructor.
 
virtual TrainingMetrics fit (const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter)=0
 Train the multiclass classifier.
 
virtual std::vector< int > predict (const torch::Tensor &X, DataConverter &converter)=0
 Predict class labels.
 
virtual std::vector< std::vector< double > > predict_proba (const torch::Tensor &X, DataConverter &converter)=0
 Predict class probabilities.
 
virtual std::vector< std::vector< double > > decision_function (const torch::Tensor &X, DataConverter &converter)=0
 Get decision function values.
 
virtual std::vector< int > get_classes () const =0
 Get unique class labels.
 
virtual bool supports_probability () const =0
 Check if the model supports probability prediction.
 
virtual int get_n_classes () const =0
 Get number of classes.
 
virtual MulticlassStrategy get_strategy_type () const =0
 Get strategy type.
 
+ + + + + + + +

+Protected Attributes

std::vector< int > classes_
 Unique class labels.
 
bool is_trained_ = false
 Whether the model is trained.
 
+

Detailed Description

+

Abstract base class for multiclass classification strategies.

+ +

Definition at line 20 of file multiclass_strategy.hpp.

+

Member Function Documentation

+ +

◆ decision_function()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual std::vector< std::vector< double > > svm_classifier::MulticlassStrategyBase::decision_function (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+pure virtual
+
+ +

Get decision function values.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Decision function values
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ fit()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
virtual TrainingMetrics svm_classifier::MulticlassStrategyBase::fit (const torch::Tensor & X,
const torch::Tensor & y,
const KernelParameters & params,
DataConverter & converter 
)
+ 		+pure virtual
+
+ +

Train the multiclass classifier.

+
Parameters
+ + + + + +
XFeature tensor of shape (n_samples, n_features)
yTarget tensor of shape (n_samples,)
paramsKernel parameters
converterData converter instance
+
+
+
Returns
Training metrics
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ get_classes()

+ +
+
+ + + + + +
+ + + + + + + +
virtual std::vector< int > svm_classifier::MulticlassStrategyBase::get_classes () const
+ 		+pure virtual
+
+ +

Get unique class labels.

+
Returns
Vector of unique class labels
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ get_n_classes()

+ +
+
+ + + + + +
+ + + + + + + +
virtual int svm_classifier::MulticlassStrategyBase::get_n_classes () const
+ 		+pure virtual
+
+ +

Get number of classes.

+
Returns
Number of classes
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ get_strategy_type()

+ +
+
+ + + + + +
+ + + + + + + +
virtual MulticlassStrategy svm_classifier::MulticlassStrategyBase::get_strategy_type () const
+ 		+pure virtual
+
+ +

Get strategy type.

+
Returns
Multiclass strategy type
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ predict()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual std::vector< int > svm_classifier::MulticlassStrategyBase::predict (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+pure virtual
+
+ +

Predict class labels.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Predicted class labels
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ predict_proba()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
virtual std::vector< std::vector< double > > svm_classifier::MulticlassStrategyBase::predict_proba (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+pure virtual
+
+ +

Predict class probabilities.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Class probabilities for each sample
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+ +

◆ supports_probability()

+ +
+
+ + + + + +
+ + + + + + + +
virtual bool svm_classifier::MulticlassStrategyBase::supports_probability () const
+ 		+pure virtual
+
+ +

Check if the model supports probability prediction.

+
Returns
True if probabilities are supported
+ +

Implemented in svm_classifier::OneVsRestStrategy, and svm_classifier::OneVsOneStrategy.

+ +
+
+

Member Data Documentation

+ +

◆ classes_

+ +
+
+ + + + + +
+ + + + +
std::vector<int> svm_classifier::MulticlassStrategyBase::classes_
+ 		+protected
+
+ +

Unique class labels.

+ +

Definition at line 92 of file multiclass_strategy.hpp.

+ +
+
+ +

◆ is_trained_

+ +
+
+ + + + + +
+ + + + +
bool svm_classifier::MulticlassStrategyBase::is_trained_ = false
+ 		+protected
+
+ +

Whether the model is trained.

+ +

Definition at line 93 of file multiclass_strategy.hpp.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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svm_classifier::OneVsOneStrategy Member List
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This is the complete list of members for svm_classifier::OneVsOneStrategy, including all inherited members.

+ + + + + + + + + + + + + + +
classes_svm_classifier::MulticlassStrategyBaseprotected
decision_function(const torch::Tensor &X, DataConverter &converter) overridesvm_classifier::OneVsOneStrategyvirtual
fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) overridesvm_classifier::OneVsOneStrategyvirtual
get_classes() const overridesvm_classifier::OneVsOneStrategyinlinevirtual
get_n_classes() const overridesvm_classifier::OneVsOneStrategyinlinevirtual
get_strategy_type() const overridesvm_classifier::OneVsOneStrategyinlinevirtual
is_trained_svm_classifier::MulticlassStrategyBaseprotected
OneVsOneStrategy()svm_classifier::OneVsOneStrategy
predict(const torch::Tensor &X, DataConverter &converter) overridesvm_classifier::OneVsOneStrategyvirtual
predict_proba(const torch::Tensor &X, DataConverter &converter) overridesvm_classifier::OneVsOneStrategyvirtual
supports_probability() const overridesvm_classifier::OneVsOneStrategyvirtual
~MulticlassStrategyBase()=defaultsvm_classifier::MulticlassStrategyBasevirtual
~OneVsOneStrategy() overridesvm_classifier::OneVsOneStrategy
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/classsvm__classifier_1_1OneVsOneStrategy.html b/classsvm__classifier_1_1OneVsOneStrategy.html new file mode 100644 index 0000000..1d5babf --- /dev/null +++ b/classsvm__classifier_1_1OneVsOneStrategy.html @@ -0,0 +1,505 @@ + + + + + + + +SVM Classifier C++: svm_classifier::OneVsOneStrategy Class Reference + + + + + + + + + +
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+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
+
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+Public Member Functions | +List of all members
+
svm_classifier::OneVsOneStrategy Class Reference
+
+
+ +

One-vs-One (OvO) multiclass strategy. + More...

+ +

#include <multiclass_strategy.hpp>

+
+Inheritance diagram for svm_classifier::OneVsOneStrategy:
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Inheritance graph
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[legend]
+
+Collaboration diagram for svm_classifier::OneVsOneStrategy:
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Collaboration graph
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[legend]
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+Public Member Functions

+ OneVsOneStrategy ()
 Constructor.
 
+ ~OneVsOneStrategy () override
 Destructor.
 
TrainingMetrics fit (const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override
 Train the multiclass classifier.
 
std::vector< int > predict (const torch::Tensor &X, DataConverter &converter) override
 Predict class labels.
 
std::vector< std::vector< double > > predict_proba (const torch::Tensor &X, DataConverter &converter) override
 Predict class probabilities.
 
std::vector< std::vector< double > > decision_function (const torch::Tensor &X, DataConverter &converter) override
 Get decision function values.
 
std::vector< int > get_classes () const override
 Get unique class labels.
 
bool supports_probability () const override
 Check if the model supports probability prediction.
 
int get_n_classes () const override
 Get number of classes.
 
MulticlassStrategy get_strategy_type () const override
 Get strategy type.
 
- Public Member Functions inherited from svm_classifier::MulticlassStrategyBase
+virtual ~MulticlassStrategyBase ()=default
 Virtual destructor.
 
+ + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from svm_classifier::MulticlassStrategyBase
std::vector< int > classes_
 Unique class labels.
 
bool is_trained_ = false
 Whether the model is trained.
 
+

Detailed Description

+

One-vs-One (OvO) multiclass strategy.

+ +

Definition at line 171 of file multiclass_strategy.hpp.

+

Member Function Documentation

+ +

◆ decision_function()

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std::vector< std::vector< double > > svm_classifier::OneVsOneStrategy::decision_function (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Get decision function values.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Decision function values
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ fit()

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TrainingMetrics svm_classifier::OneVsOneStrategy::fit (const torch::Tensor & X,
const torch::Tensor & y,
const KernelParameters & params,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Train the multiclass classifier.

+
Parameters
+ + + + + +
XFeature tensor of shape (n_samples, n_features)
yTarget tensor of shape (n_samples,)
paramsKernel parameters
converterData converter instance
+
+
+
Returns
Training metrics
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ get_classes()

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std::vector< int > svm_classifier::OneVsOneStrategy::get_classes () const
+ 		+inlineoverridevirtual
+
+ +

Get unique class labels.

+
Returns
Vector of unique class labels
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +

Definition at line 197 of file multiclass_strategy.hpp.

+ +
+
+ +

◆ get_n_classes()

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int svm_classifier::OneVsOneStrategy::get_n_classes () const
+ 		+inlineoverridevirtual
+
+ +

Get number of classes.

+
Returns
Number of classes
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +

Definition at line 201 of file multiclass_strategy.hpp.

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+ +

◆ get_strategy_type()

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MulticlassStrategy svm_classifier::OneVsOneStrategy::get_strategy_type () const
+ 		+inlineoverridevirtual
+
+ +

Get strategy type.

+
Returns
Multiclass strategy type
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +

Definition at line 203 of file multiclass_strategy.hpp.

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+ +

◆ predict()

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std::vector< int > svm_classifier::OneVsOneStrategy::predict (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Predict class labels.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Predicted class labels
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ predict_proba()

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+ + + + + +
+ + + + + + + + + + + + + + + + + + +
std::vector< std::vector< double > > svm_classifier::OneVsOneStrategy::predict_proba (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Predict class probabilities.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Class probabilities for each sample
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ supports_probability()

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+ + + + + + + +
bool svm_classifier::OneVsOneStrategy::supports_probability () const
+ 		+overridevirtual
+
+ +

Check if the model supports probability prediction.

+
Returns
True if probabilities are supported
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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svm_classifier::OneVsRestStrategy Member List
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This is the complete list of members for svm_classifier::OneVsRestStrategy, including all inherited members.

+ + + + + + + + + + + + + + +
classes_svm_classifier::MulticlassStrategyBaseprotected
decision_function(const torch::Tensor &X, DataConverter &converter) overridesvm_classifier::OneVsRestStrategyvirtual
fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) overridesvm_classifier::OneVsRestStrategyvirtual
get_classes() const overridesvm_classifier::OneVsRestStrategyinlinevirtual
get_n_classes() const overridesvm_classifier::OneVsRestStrategyinlinevirtual
get_strategy_type() const overridesvm_classifier::OneVsRestStrategyinlinevirtual
is_trained_svm_classifier::MulticlassStrategyBaseprotected
OneVsRestStrategy()svm_classifier::OneVsRestStrategy
predict(const torch::Tensor &X, DataConverter &converter) overridesvm_classifier::OneVsRestStrategyvirtual
predict_proba(const torch::Tensor &X, DataConverter &converter) overridesvm_classifier::OneVsRestStrategyvirtual
supports_probability() const overridesvm_classifier::OneVsRestStrategyvirtual
~MulticlassStrategyBase()=defaultsvm_classifier::MulticlassStrategyBasevirtual
~OneVsRestStrategy() overridesvm_classifier::OneVsRestStrategy
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/classsvm__classifier_1_1OneVsRestStrategy.html b/classsvm__classifier_1_1OneVsRestStrategy.html new file mode 100644 index 0000000..6c0ed03 --- /dev/null +++ b/classsvm__classifier_1_1OneVsRestStrategy.html @@ -0,0 +1,505 @@ + + + + + + + +SVM Classifier C++: svm_classifier::OneVsRestStrategy Class Reference + + + + + + + + + +
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SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
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+Public Member Functions | +List of all members
+
svm_classifier::OneVsRestStrategy Class Reference
+
+
+ +

One-vs-Rest (OvR) multiclass strategy. + More...

+ +

#include <multiclass_strategy.hpp>

+
+Inheritance diagram for svm_classifier::OneVsRestStrategy:
+
+
Inheritance graph
+ + + + + +
[legend]
+
+Collaboration diagram for svm_classifier::OneVsRestStrategy:
+
+
Collaboration graph
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[legend]
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+Public Member Functions

+ OneVsRestStrategy ()
 Constructor.
 
+ ~OneVsRestStrategy () override
 Destructor.
 
TrainingMetrics fit (const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override
 Train the multiclass classifier.
 
std::vector< int > predict (const torch::Tensor &X, DataConverter &converter) override
 Predict class labels.
 
std::vector< std::vector< double > > predict_proba (const torch::Tensor &X, DataConverter &converter) override
 Predict class probabilities.
 
std::vector< std::vector< double > > decision_function (const torch::Tensor &X, DataConverter &converter) override
 Get decision function values.
 
std::vector< int > get_classes () const override
 Get unique class labels.
 
bool supports_probability () const override
 Check if the model supports probability prediction.
 
int get_n_classes () const override
 Get number of classes.
 
MulticlassStrategy get_strategy_type () const override
 Get strategy type.
 
- Public Member Functions inherited from svm_classifier::MulticlassStrategyBase
+virtual ~MulticlassStrategyBase ()=default
 Virtual destructor.
 
+ + + + + + + + +

+Additional Inherited Members

- Protected Attributes inherited from svm_classifier::MulticlassStrategyBase
std::vector< int > classes_
 Unique class labels.
 
bool is_trained_ = false
 Whether the model is trained.
 
+

Detailed Description

+

One-vs-Rest (OvR) multiclass strategy.

+ +

Definition at line 99 of file multiclass_strategy.hpp.

+

Member Function Documentation

+ +

◆ decision_function()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
std::vector< std::vector< double > > svm_classifier::OneVsRestStrategy::decision_function (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Get decision function values.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Decision function values
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ fit()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TrainingMetrics svm_classifier::OneVsRestStrategy::fit (const torch::Tensor & X,
const torch::Tensor & y,
const KernelParameters & params,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Train the multiclass classifier.

+
Parameters
+ + + + + +
XFeature tensor of shape (n_samples, n_features)
yTarget tensor of shape (n_samples,)
paramsKernel parameters
converterData converter instance
+
+
+
Returns
Training metrics
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ get_classes()

+ +
+
+ + + + + +
+ + + + + + + +
std::vector< int > svm_classifier::OneVsRestStrategy::get_classes () const
+ 		+inlineoverridevirtual
+
+ +

Get unique class labels.

+
Returns
Vector of unique class labels
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +

Definition at line 125 of file multiclass_strategy.hpp.

+ +
+
+ +

◆ get_n_classes()

+ +
+
+ + + + + +
+ + + + + + + +
int svm_classifier::OneVsRestStrategy::get_n_classes () const
+ 		+inlineoverridevirtual
+
+ +

Get number of classes.

+
Returns
Number of classes
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +

Definition at line 129 of file multiclass_strategy.hpp.

+ +
+
+ +

◆ get_strategy_type()

+ +
+
+ + + + + +
+ + + + + + + +
MulticlassStrategy svm_classifier::OneVsRestStrategy::get_strategy_type () const
+ 		+inlineoverridevirtual
+
+ +

Get strategy type.

+
Returns
Multiclass strategy type
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +

Definition at line 131 of file multiclass_strategy.hpp.

+ +
+
+ +

◆ predict()

+ +
+
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+ + + + + + + + + + + + + + + + + + +
std::vector< int > svm_classifier::OneVsRestStrategy::predict (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Predict class labels.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Predicted class labels
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ predict_proba()

+ +
+
+ + + + + +
+ + + + + + + + + + + + + + + + + + +
std::vector< std::vector< double > > svm_classifier::OneVsRestStrategy::predict_proba (const torch::Tensor & X,
DataConverter & converter 
)
+ 		+overridevirtual
+
+ +

Predict class probabilities.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
converterData converter instance
+
+
+
Returns
Class probabilities for each sample
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+ +

◆ supports_probability()

+ +
+
+ + + + + +
+ + + + + + + +
bool svm_classifier::OneVsRestStrategy::supports_probability () const
+ 		+overridevirtual
+
+ +

Check if the model supports probability prediction.

+
Returns
True if probabilities are supported
+ +

Implements svm_classifier::MulticlassStrategyBase.

+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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+
svm_classifier::SVMClassifier Member List
+
+
+ +

This is the complete list of members for svm_classifier::SVMClassifier, including all inherited members.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
cross_validate(const torch::Tensor &X, const torch::Tensor &y, int cv=5)svm_classifier::SVMClassifier
decision_function(const torch::Tensor &X)svm_classifier::SVMClassifier
evaluate(const torch::Tensor &X, const torch::Tensor &y_true)svm_classifier::SVMClassifier
fit(const torch::Tensor &X, const torch::Tensor &y)svm_classifier::SVMClassifier
get_classes() constsvm_classifier::SVMClassifier
get_feature_importance() constsvm_classifier::SVMClassifier
get_kernel_type() constsvm_classifier::SVMClassifierinline
get_multiclass_strategy() constsvm_classifier::SVMClassifierinline
get_n_classes() constsvm_classifier::SVMClassifier
get_n_features() constsvm_classifier::SVMClassifierinline
get_parameters() constsvm_classifier::SVMClassifier
get_svm_library() constsvm_classifier::SVMClassifierinline
get_training_metrics() constsvm_classifier::SVMClassifierinline
grid_search(const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)svm_classifier::SVMClassifier
is_fitted() constsvm_classifier::SVMClassifierinline
load_model(const std::string &filename)svm_classifier::SVMClassifier
operator=(const SVMClassifier &)=deletesvm_classifier::SVMClassifier
operator=(SVMClassifier &&) noexceptsvm_classifier::SVMClassifier
predict(const torch::Tensor &X)svm_classifier::SVMClassifier
predict_proba(const torch::Tensor &X)svm_classifier::SVMClassifier
reset()svm_classifier::SVMClassifier
save_model(const std::string &filename) constsvm_classifier::SVMClassifier
score(const torch::Tensor &X, const torch::Tensor &y_true)svm_classifier::SVMClassifier
set_parameters(const nlohmann::json &config)svm_classifier::SVMClassifier
supports_probability() constsvm_classifier::SVMClassifier
SVMClassifier()svm_classifier::SVMClassifier
SVMClassifier(const nlohmann::json &config)svm_classifier::SVMClassifierexplicit
SVMClassifier(KernelType kernel, double C=1.0, MulticlassStrategy multiclass_strategy=MulticlassStrategy::ONE_VS_REST)svm_classifier::SVMClassifier
SVMClassifier(const SVMClassifier &)=deletesvm_classifier::SVMClassifier
SVMClassifier(SVMClassifier &&) noexceptsvm_classifier::SVMClassifier
~SVMClassifier()svm_classifier::SVMClassifier
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/classsvm__classifier_1_1SVMClassifier.html b/classsvm__classifier_1_1SVMClassifier.html new file mode 100644 index 0000000..8e62777 --- /dev/null +++ b/classsvm__classifier_1_1SVMClassifier.html @@ -0,0 +1,991 @@ + + + + + + + +SVM Classifier C++: svm_classifier::SVMClassifier Class Reference + + + + + + + + + +
+
+ + + + + + +
+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
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+Public Member Functions | +List of all members
+
svm_classifier::SVMClassifier Class Reference
+
+
+ +

Support Vector Machine Classifier with scikit-learn compatible API. + More...

+ +

#include <svm_classifier.hpp>

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+Public Member Functions

+ SVMClassifier ()
 Default constructor with default parameters.
 
 SVMClassifier (const nlohmann::json &config)
 Constructor with JSON parameters.
 
 SVMClassifier (KernelType kernel, double C=1.0, MulticlassStrategy multiclass_strategy=MulticlassStrategy::ONE_VS_REST)
 Constructor with explicit parameters.
 
+ ~SVMClassifier ()
 Destructor.
 
+ SVMClassifier (const SVMClassifier &)=delete
 Copy constructor (deleted - models are not copyable)
 
+SVMClassifier & operator= (const SVMClassifier &)=delete
 Copy assignment (deleted - models are not copyable)
 
+ SVMClassifier (SVMClassifier &&) noexcept
 Move constructor.
 
+SVMClassifier & operator= (SVMClassifier &&) noexcept
 Move assignment.
 
TrainingMetrics fit (const torch::Tensor &X, const torch::Tensor &y)
 Train the SVM classifier.
 
torch::Tensor predict (const torch::Tensor &X)
 Predict class labels for samples.
 
torch::Tensor predict_proba (const torch::Tensor &X)
 Predict class probabilities for samples.
 
torch::Tensor decision_function (const torch::Tensor &X)
 Get decision function values.
 
double score (const torch::Tensor &X, const torch::Tensor &y_true)
 Calculate accuracy score on test data.
 
EvaluationMetrics evaluate (const torch::Tensor &X, const torch::Tensor &y_true)
 Calculate detailed evaluation metrics.
 
void set_parameters (const nlohmann::json &config)
 Set parameters from JSON configuration.
 
nlohmann::json get_parameters () const
 Get current parameters as JSON.
 
bool is_fitted () const
 Check if the model is fitted/trained.
 
int get_n_classes () const
 Get the number of classes.
 
std::vector< int > get_classes () const
 Get unique class labels.
 
int get_n_features () const
 Get the number of features.
 
TrainingMetrics get_training_metrics () const
 Get training metrics from last fit.
 
bool supports_probability () const
 Check if the current model supports probability prediction.
 
void save_model (const std::string &filename) const
 Save model to file.
 
void load_model (const std::string &filename)
 Load model from file.
 
KernelType get_kernel_type () const
 Get kernel type.
 
MulticlassStrategy get_multiclass_strategy () const
 Get multiclass strategy.
 
SVMLibrary get_svm_library () const
 Get SVM library being used.
 
std::vector< double > cross_validate (const torch::Tensor &X, const torch::Tensor &y, int cv=5)
 Perform cross-validation.
 
nlohmann::json grid_search (const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)
 Find optimal hyperparameters using grid search.
 
torch::Tensor get_feature_importance () const
 Get feature importance (for linear kernels only)
 
+void reset ()
 Reset the classifier (clear trained model)
 
+

Detailed Description

+

Support Vector Machine Classifier with scikit-learn compatible API.

+

This class provides a unified interface for SVM classification using both liblinear (for linear kernels) and libsvm (for non-linear kernels). It supports multiclass classification through One-vs-Rest and One-vs-One strategies.

+ +

Definition at line 21 of file svm_classifier.hpp.

+

Constructor & Destructor Documentation

+ +

◆ SVMClassifier() [1/2]

+ +
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svm_classifier::SVMClassifier::SVMClassifier (const nlohmann::json & config)
+ 		+explicit
+
+ +

Constructor with JSON parameters.

+
Parameters
+ + +
configJSON configuration object
+
+
+ +
+
+ +

◆ SVMClassifier() [2/2]

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svm_classifier::SVMClassifier::SVMClassifier (KernelType kernel,
double C = 1.0,
MulticlassStrategy multiclass_strategy = MulticlassStrategy::ONE_VS_REST 
)
+
+ +

Constructor with explicit parameters.

+
Parameters
+ + + + +
kernelKernel type
CRegularization parameter
multiclass_strategyMulticlass strategy
+
+
+ +
+
+

Member Function Documentation

+ +

◆ cross_validate()

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std::vector< double > svm_classifier::SVMClassifier::cross_validate (const torch::Tensor & X,
const torch::Tensor & y,
int cv = 5 
)
+
+ +

Perform cross-validation.

+
Parameters
+ + + + +
XFeature tensor
yTarget tensor
cvNumber of folds (default: 5)
+
+
+
Returns
Cross-validation scores for each fold
+ +
+
+ +

◆ decision_function()

+ +
+
+ + + + + + + + +
torch::Tensor svm_classifier::SVMClassifier::decision_function (const torch::Tensor & X)
+
+ +

Get decision function values.

+
Parameters
+ + +
XFeature tensor of shape (n_samples, n_features)
+
+
+
Returns
Tensor with decision function values
+
Exceptions
+ + +
std::runtime_errorif model is not fitted
+
+
+ +
+
+ +

◆ evaluate()

+ +
+
+ + + + + + + + + + + + + + + + + + +
EvaluationMetrics svm_classifier::SVMClassifier::evaluate (const torch::Tensor & X,
const torch::Tensor & y_true 
)
+
+ +

Calculate detailed evaluation metrics.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
y_trueTrue labels tensor of shape (n_samples,)
+
+
+
Returns
Evaluation metrics including precision, recall, F1-score
+ +
+
+ +

◆ fit()

+ +
+
+ + + + + + + + + + + + + + + + + + +
TrainingMetrics svm_classifier::SVMClassifier::fit (const torch::Tensor & X,
const torch::Tensor & y 
)
+
+ +

Train the SVM classifier.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
yTarget tensor of shape (n_samples,) with class labels
+
+
+
Returns
Training metrics
+
Exceptions
+ + + +
std::invalid_argumentif input data is invalid
std::runtime_errorif training fails
+
+
+ +
+
+ +

◆ get_classes()

+ +
+
+ + + + + + + +
std::vector< int > svm_classifier::SVMClassifier::get_classes () const
+
+ +

Get unique class labels.

+
Returns
Vector of unique class labels
+ +
+
+ +

◆ get_feature_importance()

+ +
+
+ + + + + + + +
torch::Tensor svm_classifier::SVMClassifier::get_feature_importance () const
+
+ +

Get feature importance (for linear kernels only)

+
Returns
Tensor with feature weights/importance
+
Exceptions
+ + +
std::runtime_errorif not supported for current kernel
+
+
+ +
+
+ +

◆ get_kernel_type()

+ +
+
+ + + + + +
+ + + + + + + +
KernelType svm_classifier::SVMClassifier::get_kernel_type () const
+ 		+inline
+
+ +

Get kernel type.

+
Returns
Current kernel type
+ +

Definition at line 187 of file svm_classifier.hpp.

+ +
+
+ +

◆ get_multiclass_strategy()

+ +
+
+ + + + + +
+ + + + + + + +
MulticlassStrategy svm_classifier::SVMClassifier::get_multiclass_strategy () const
+ 		+inline
+
+ +

Get multiclass strategy.

+
Returns
Current multiclass strategy
+ +

Definition at line 193 of file svm_classifier.hpp.

+ +
+
+ +

◆ get_n_classes()

+ +
+
+ + + + + + + +
int svm_classifier::SVMClassifier::get_n_classes () const
+
+ +

Get the number of classes.

+
Returns
Number of classes (0 if not fitted)
+ +
+
+ +

◆ get_n_features()

+ +
+
+ + + + + +
+ + + + + + + +
int svm_classifier::SVMClassifier::get_n_features () const
+ 		+inline
+
+ +

Get the number of features.

+
Returns
Number of features (0 if not fitted)
+ +

Definition at line 155 of file svm_classifier.hpp.

+ +
+
+ +

◆ get_parameters()

+ +
+
+ + + + + + + +
nlohmann::json svm_classifier::SVMClassifier::get_parameters () const
+
+ +

Get current parameters as JSON.

+
Returns
JSON object with current parameters
+ +
+
+ +

◆ get_svm_library()

+ +
+
+ + + + + +
+ + + + + + + +
SVMLibrary svm_classifier::SVMClassifier::get_svm_library () const
+ 		+inline
+
+ +

Get SVM library being used.

+
Returns
SVM library type
+ +

Definition at line 199 of file svm_classifier.hpp.

+ +
+
+ +

◆ get_training_metrics()

+ +
+
+ + + + + +
+ + + + + + + +
TrainingMetrics svm_classifier::SVMClassifier::get_training_metrics () const
+ 		+inline
+
+ +

Get training metrics from last fit.

+
Returns
Training metrics
+ +

Definition at line 161 of file svm_classifier.hpp.

+ +
+
+ +

◆ grid_search()

+ +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
nlohmann::json svm_classifier::SVMClassifier::grid_search (const torch::Tensor & X,
const torch::Tensor & y,
const nlohmann::json & param_grid,
int cv = 5 
)
+
+ +

Find optimal hyperparameters using grid search.

+
Parameters
+ + + + + +
XFeature tensor
yTarget tensor
param_gridJSON object with parameter grid
cvNumber of cross-validation folds
+
+
+
Returns
JSON object with best parameters and score
+ +
+
+ +

◆ is_fitted()

+ +
+
+ + + + + +
+ + + + + + + +
bool svm_classifier::SVMClassifier::is_fitted () const
+ 		+inline
+
+ +

Check if the model is fitted/trained.

+
Returns
True if model is fitted
+ +

Definition at line 137 of file svm_classifier.hpp.

+ +
+
+ +

◆ load_model()

+ +
+
+ + + + + + + + +
void svm_classifier::SVMClassifier::load_model (const std::string & filename)
+
+ +

Load model from file.

+
Parameters
+ + +
filenamePath to load the model from
+
+
+
Exceptions
+ + +
std::runtime_errorif loading fails
+
+
+ +
+
+ +

◆ predict()

+ +
+
+ + + + + + + + +
torch::Tensor svm_classifier::SVMClassifier::predict (const torch::Tensor & X)
+
+ +

Predict class labels for samples.

+
Parameters
+ + +
XFeature tensor of shape (n_samples, n_features)
+
+
+
Returns
Tensor of predicted class labels
+
Exceptions
+ + +
std::runtime_errorif model is not fitted
+
+
+ +
+
+ +

◆ predict_proba()

+ +
+
+ + + + + + + + +
torch::Tensor svm_classifier::SVMClassifier::predict_proba (const torch::Tensor & X)
+
+ +

Predict class probabilities for samples.

+
Parameters
+ + +
XFeature tensor of shape (n_samples, n_features)
+
+
+
Returns
Tensor of shape (n_samples, n_classes) with class probabilities
+
Exceptions
+ + +
std::runtime_errorif model is not fitted or doesn't support probabilities
+
+
+ +
+
+ +

◆ save_model()

+ +
+
+ + + + + + + + +
void svm_classifier::SVMClassifier::save_model (const std::string & filename) const
+
+ +

Save model to file.

+
Parameters
+ + +
filenamePath to save the model
+
+
+
Exceptions
+ + +
std::runtime_errorif saving fails
+
+
+ +
+
+ +

◆ score()

+ +
+
+ + + + + + + + + + + + + + + + + + +
double svm_classifier::SVMClassifier::score (const torch::Tensor & X,
const torch::Tensor & y_true 
)
+
+ +

Calculate accuracy score on test data.

+
Parameters
+ + + +
XFeature tensor of shape (n_samples, n_features)
y_trueTrue labels tensor of shape (n_samples,)
+
+
+
Returns
Accuracy score (fraction of correctly predicted samples)
+
Exceptions
+ + +
std::runtime_errorif model is not fitted
+
+
+ +
+
+ +

◆ set_parameters()

+ +
+
+ + + + + + + + +
void svm_classifier::SVMClassifier::set_parameters (const nlohmann::json & config)
+
+ +

Set parameters from JSON configuration.

+
Parameters
+ + +
configJSON configuration object
+
+
+
Exceptions
+ + +
std::invalid_argumentif parameters are invalid
+
+
+ +
+
+ +

◆ supports_probability()

+ +
+
+ + + + + + + +
bool svm_classifier::SVMClassifier::supports_probability () const
+
+ +

Check if the current model supports probability prediction.

+
Returns
True if probabilities are supported
+ +
+
+
The documentation for this class was generated from the following file: +
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/closed.png b/closed.png new file mode 100644 index 0000000000000000000000000000000000000000..98cc2c909da37a6df914fbf67780eebd99c597f5 GIT binary patch literal 132 zcmeAS@N?(olHy`uVBq!ia0vp^oFL4>1|%O$WD@{V-kvUwAr*{o@8{^CZMh(5KoB^r_<4^zF@3)Cp&&t3hdujKf f*?bjBoY!V+E))@{xMcbjXe@)LtDnm{r-UW|*e5JT literal 0 HcmV?d00001 diff --git a/data__converter_8cpp_source.html b/data__converter_8cpp_source.html new file mode 100644 index 0000000..f4369e8 --- /dev/null +++ b/data__converter_8cpp_source.html @@ -0,0 +1,502 @@ + + + + + + + +SVM Classifier C++: src/data_converter.cpp Source File + + + + + + + + + +
+
+ + + + + + +
+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
+
+
+ + + + + + + + + +
+
+ + +
+
+
+
+
+
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+
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+
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+
+
+
+
+ + +
+
+
data_converter.cpp
+
+
+
1#include "svm_classifier/data_converter.hpp"
+
2#include "svm.h" // libsvm
+
3#include "linear.h" // liblinear
+
4#include <stdexcept>
+
5#include <iostream>
+
6#include <cmath>
+
7
+
8namespace svm_classifier {
+
9
+
+
10 DataConverter::DataConverter()
+
11 : n_features_(0)
+
12 , n_samples_(0)
+
13 , sparse_threshold_(1e-8)
+
14 {
+
15 }
+
+
16
+
+
17 DataConverter::~DataConverter()
+
18 {
+
19 cleanup();
+
20 }
+
+
21
+
+
22 std::unique_ptr<svm_problem> DataConverter::to_svm_problem(const torch::Tensor& X,
+
23 const torch::Tensor& y)
+
24 {
+
25 validate_tensors(X, y);
+
26
+
27 auto X_cpu = ensure_cpu_tensor(X);
+
28
+
29 n_samples_ = X_cpu.size(0);
+
30 n_features_ = X_cpu.size(1);
+
31
+
32 // Convert tensor data to svm_node structures
+
33 svm_nodes_storage_ = tensor_to_svm_nodes(X_cpu);
+
34
+
35 // Prepare pointers for svm_problem
+
36 svm_x_space_.clear();
+
37 svm_x_space_.reserve(n_samples_);
+
38
+
39 for (auto& nodes : svm_nodes_storage_) {
+
40 svm_x_space_.push_back(nodes.data());
+
41 }
+
42
+
43 // Extract labels if provided
+
44 if (y.defined() && y.numel() > 0) {
+
45 svm_y_space_ = extract_labels(y);
+
46 } else {
+
47 svm_y_space_.clear();
+
48 svm_y_space_.resize(n_samples_, 0.0); // Dummy labels for prediction
+
49 }
+
50
+
51 // Create svm_problem
+
52 auto problem = std::make_unique<svm_problem>();
+
53 problem->l = n_samples_;
+
54 problem->x = svm_x_space_.data();
+
55 problem->y = svm_y_space_.data();
+
56
+
57 return problem;
+
58 }
+
+
59
+
+
60 std::unique_ptr<problem> DataConverter::to_linear_problem(const torch::Tensor& X,
+
61 const torch::Tensor& y)
+
62 {
+
63 validate_tensors(X, y);
+
64
+
65 auto X_cpu = ensure_cpu_tensor(X);
+
66
+
67 n_samples_ = X_cpu.size(0);
+
68 n_features_ = X_cpu.size(1);
+
69
+
70 // Convert tensor data to feature_node structures
+
71 linear_nodes_storage_ = tensor_to_linear_nodes(X_cpu);
+
72
+
73 // Prepare pointers for problem
+
74 linear_x_space_.clear();
+
75 linear_x_space_.reserve(n_samples_);
+
76
+
77 for (auto& nodes : linear_nodes_storage_) {
+
78 linear_x_space_.push_back(nodes.data());
+
79 }
+
80
+
81 // Extract labels if provided
+
82 if (y.defined() && y.numel() > 0) {
+
83 linear_y_space_ = extract_labels(y);
+
84 } else {
+
85 linear_y_space_.clear();
+
86 linear_y_space_.resize(n_samples_, 0.0); // Dummy labels for prediction
+
87 }
+
88
+
89 // Create problem
+
90 auto linear_problem = std::make_unique<problem>();
+
91 linear_problem->l = n_samples_;
+
92 linear_problem->n = n_features_;
+
93 linear_problem->x = linear_x_space_.data();
+
94 linear_problem->y = linear_y_space_.data();
+
95 linear_problem->bias = -1; // No bias term by default
+
96
+
97 return linear_problem;
+
98 }
+
+
99
+
+
100 svm_node* DataConverter::to_svm_node(const torch::Tensor& sample)
+
101 {
+
102 validate_tensor_properties(sample, 1, "sample");
+
103
+
104 auto sample_cpu = ensure_cpu_tensor(sample);
+
105 single_svm_nodes_ = sample_to_svm_nodes(sample_cpu);
+
106
+
107 return single_svm_nodes_.data();
+
108 }
+
+
109
+
+
110 feature_node* DataConverter::to_feature_node(const torch::Tensor& sample)
+
111 {
+
112 validate_tensor_properties(sample, 1, "sample");
+
113
+
114 auto sample_cpu = ensure_cpu_tensor(sample);
+
115 single_linear_nodes_ = sample_to_linear_nodes(sample_cpu);
+
116
+
117 return single_linear_nodes_.data();
+
118 }
+
+
119
+
+
120 torch::Tensor DataConverter::from_predictions(const std::vector<double>& predictions)
+
121 {
+
122 auto options = torch::TensorOptions().dtype(torch::kInt32);
+
123 auto tensor = torch::zeros({ static_cast<int64_t>(predictions.size()) }, options);
+
124
+
125 for (size_t i = 0; i < predictions.size(); ++i) {
+
126 tensor[i] = static_cast<int>(predictions[i]);
+
127 }
+
128
+
129 return tensor;
+
130 }
+
+
131
+
+
132 torch::Tensor DataConverter::from_probabilities(const std::vector<std::vector<double>>& probabilities)
+
133 {
+
134 if (probabilities.empty()) {
+
135 return torch::empty({ 0, 0 });
+
136 }
+
137
+
138 int n_samples = static_cast<int>(probabilities.size());
+
139 int n_classes = static_cast<int>(probabilities[0].size());
+
140
+
141 auto tensor = torch::zeros({ n_samples, n_classes }, torch::kFloat64);
+
142
+
143 for (int i = 0; i < n_samples; ++i) {
+
144 for (int j = 0; j < n_classes; ++j) {
+
145 tensor[i][j] = probabilities[i][j];
+
146 }
+
147 }
+
148
+
149 return tensor;
+
150 }
+
+
151
+
+
152 torch::Tensor DataConverter::from_decision_values(const std::vector<std::vector<double>>& decision_values)
+
153 {
+
154 if (decision_values.empty()) {
+
155 return torch::empty({ 0, 0 });
+
156 }
+
157
+
158 int n_samples = static_cast<int>(decision_values.size());
+
159 int n_values = static_cast<int>(decision_values[0].size());
+
160
+
161 auto tensor = torch::zeros({ n_samples, n_values }, torch::kFloat64);
+
162
+
163 for (int i = 0; i < n_samples; ++i) {
+
164 for (int j = 0; j < n_values; ++j) {
+
165 tensor[i][j] = decision_values[i][j];
+
166 }
+
167 }
+
168
+
169 return tensor;
+
170 }
+
+
171
+
+
172 void DataConverter::validate_tensors(const torch::Tensor& X, const torch::Tensor& y)
+
173 {
+
174 validate_tensor_properties(X, 2, "X");
+
175
+
176 if (y.defined() && y.numel() > 0) {
+
177 validate_tensor_properties(y, 1, "y");
+
178
+
179 // Check that number of samples match
+
180 if (X.size(0) != y.size(0)) {
+
181 throw std::invalid_argument(
+
182 "Number of samples in X (" + std::to_string(X.size(0)) +
+
183 ") does not match number of labels in y (" + std::to_string(y.size(0)) + ")"
+
184 );
+
185 }
+
186 }
+
187
+
188 // Check for reasonable dimensions
+
189 if (X.size(0) == 0) {
+
190 throw std::invalid_argument("X cannot have 0 samples");
+
191 }
+
192
+
193 if (X.size(1) == 0) {
+
194 throw std::invalid_argument("X cannot have 0 features");
+
195 }
+
196 }
+
+
197
+
+
198 void DataConverter::cleanup()
+
199 {
+
200 svm_nodes_storage_.clear();
+
201 svm_x_space_.clear();
+
202 svm_y_space_.clear();
+
203
+
204 linear_nodes_storage_.clear();
+
205 linear_x_space_.clear();
+
206 linear_y_space_.clear();
+
207
+
208 single_svm_nodes_.clear();
+
209 single_linear_nodes_.clear();
+
210
+
211 n_features_ = 0;
+
212 n_samples_ = 0;
+
213 }
+
+
214
+
215 std::vector<std::vector<svm_node>> DataConverter::tensor_to_svm_nodes(const torch::Tensor& X)
+
216 {
+
217 std::vector<std::vector<svm_node>> nodes_storage;
+
218 nodes_storage.reserve(X.size(0));
+
219
+
220 auto X_acc = X.accessor<float, 2>();
+
221
+
222 for (int i = 0; i < X.size(0); ++i) {
+
223 nodes_storage.push_back(sample_to_svm_nodes(X[i]));
+
224 }
+
225
+
226 return nodes_storage;
+
227 }
+
228
+
229 std::vector<std::vector<feature_node>> DataConverter::tensor_to_linear_nodes(const torch::Tensor& X)
+
230 {
+
231 std::vector<std::vector<feature_node>> nodes_storage;
+
232 nodes_storage.reserve(X.size(0));
+
233
+
234 for (int i = 0; i < X.size(0); ++i) {
+
235 nodes_storage.push_back(sample_to_linear_nodes(X[i]));
+
236 }
+
237
+
238 return nodes_storage;
+
239 }
+
240
+
241 std::vector<svm_node> DataConverter::sample_to_svm_nodes(const torch::Tensor& sample)
+
242 {
+
243 std::vector<svm_node> nodes;
+
244
+
245 auto sample_acc = sample.accessor<float, 1>();
+
246
+
247 // Reserve space (worst case: all features are non-sparse)
+
248 nodes.reserve(sample.size(0) + 1); // +1 for terminator
+
249
+
250 for (int j = 0; j < sample.size(0); ++j) {
+
251 double value = static_cast<double>(sample_acc[j]);
+
252
+
253 // Skip sparse features
+
254 if (std::abs(value) > sparse_threshold_) {
+
255 svm_node node;
+
256 node.index = j + 1; // libsvm uses 1-based indexing
+
257 node.value = value;
+
258 nodes.push_back(node);
+
259 }
+
260 }
+
261
+
262 // Add terminator
+
263 svm_node terminator;
+
264 terminator.index = -1;
+
265 terminator.value = 0;
+
266 nodes.push_back(terminator);
+
267
+
268 return nodes;
+
269 }
+
270
+
271 std::vector<feature_node> DataConverter::sample_to_linear_nodes(const torch::Tensor& sample)
+
272 {
+
273 std::vector<feature_node> nodes;
+
274
+
275 auto sample_acc = sample.accessor<float, 1>();
+
276
+
277 // Reserve space (worst case: all features are non-sparse)
+
278 nodes.reserve(sample.size(0) + 1); // +1 for terminator
+
279
+
280 for (int j = 0; j < sample.size(0); ++j) {
+
281 double value = static_cast<double>(sample_acc[j]);
+
282
+
283 // Skip sparse features
+
284 if (std::abs(value) > sparse_threshold_) {
+
285 feature_node node;
+
286 node.index = j + 1; // liblinear uses 1-based indexing
+
287 node.value = value;
+
288 nodes.push_back(node);
+
289 }
+
290 }
+
291
+
292 // Add terminator
+
293 feature_node terminator;
+
294 terminator.index = -1;
+
295 terminator.value = 0;
+
296 nodes.push_back(terminator);
+
297
+
298 return nodes;
+
299 }
+
300
+
301 std::vector<double> DataConverter::extract_labels(const torch::Tensor& y)
+
302 {
+
303 auto y_cpu = ensure_cpu_tensor(y);
+
304 std::vector<double> labels;
+
305 labels.reserve(y_cpu.size(0));
+
306
+
307 // Handle different tensor types
+
308 if (y_cpu.dtype() == torch::kInt32) {
+
309 auto y_acc = y_cpu.accessor<int32_t, 1>();
+
310 for (int i = 0; i < y_cpu.size(0); ++i) {
+
311 labels.push_back(static_cast<double>(y_acc[i]));
+
312 }
+
313 } else if (y_cpu.dtype() == torch::kInt64) {
+
314 auto y_acc = y_cpu.accessor<int64_t, 1>();
+
315 for (int i = 0; i < y_cpu.size(0); ++i) {
+
316 labels.push_back(static_cast<double>(y_acc[i]));
+
317 }
+
318 } else if (y_cpu.dtype() == torch::kFloat32) {
+
319 auto y_acc = y_cpu.accessor<float, 1>();
+
320 for (int i = 0; i < y_cpu.size(0); ++i) {
+
321 labels.push_back(static_cast<double>(y_acc[i]));
+
322 }
+
323 } else if (y_cpu.dtype() == torch::kFloat64) {
+
324 auto y_acc = y_cpu.accessor<double, 1>();
+
325 for (int i = 0; i < y_cpu.size(0); ++i) {
+
326 labels.push_back(y_acc[i]);
+
327 }
+
328 } else {
+
329 throw std::invalid_argument("Unsupported label tensor dtype");
+
330 }
+
331
+
332 return labels;
+
333 }
+
334
+
335 torch::Tensor DataConverter::ensure_cpu_tensor(const torch::Tensor& tensor)
+
336 {
+
337 if (tensor.device().type() != torch::kCPU) {
+
338 return tensor.to(torch::kCPU);
+
339 }
+
340
+
341 // Convert to float32 if not already
+
342 if (tensor.dtype() != torch::kFloat32) {
+
343 return tensor.to(torch::kFloat32);
+
344 }
+
345
+
346 return tensor;
+
347 }
+
348
+
349 void DataConverter::validate_tensor_properties(const torch::Tensor& tensor,
+
350 int expected_dims,
+
351 const std::string& name)
+
352 {
+
353 if (!tensor.defined()) {
+
354 throw std::invalid_argument(name + " tensor is not defined");
+
355 }
+
356
+
357 if (tensor.dim() != expected_dims) {
+
358 throw std::invalid_argument(
+
359 name + " must have " + std::to_string(expected_dims) +
+
360 " dimensions, got " + std::to_string(tensor.dim())
+
361 );
+
362 }
+
363
+
364 if (tensor.numel() == 0) {
+
365 throw std::invalid_argument(name + " tensor cannot be empty");
+
366 }
+
367
+
368 // Check for NaN or Inf values
+
369 if (torch::any(torch::isnan(tensor)).item<bool>()) {
+
370 throw std::invalid_argument(name + " contains NaN values");
+
371 }
+
372
+
373 if (torch::any(torch::isinf(tensor)).item<bool>()) {
+
374 throw std::invalid_argument(name + " contains infinite values");
+
375 }
+
376 }
+
377
+
378} // namespace svm_classifier
+
svm_classifier::DataConverter::to_svm_node
svm_node * to_svm_node(const torch::Tensor &sample)
Convert single sample to libsvm format.
Definition data_converter.cpp:100
+
svm_classifier::DataConverter::cleanup
void cleanup()
Clean up all allocated memory.
Definition data_converter.cpp:198
+
svm_classifier::DataConverter::from_decision_values
torch::Tensor from_decision_values(const std::vector< std::vector< double > > &decision_values)
Convert decision values back to PyTorch tensor.
Definition data_converter.cpp:152
+
svm_classifier::DataConverter::from_probabilities
torch::Tensor from_probabilities(const std::vector< std::vector< double > > &probabilities)
Convert probabilities back to PyTorch tensor.
Definition data_converter.cpp:132
+
svm_classifier::DataConverter::DataConverter
DataConverter()
Default constructor.
Definition data_converter.cpp:10
+
svm_classifier::DataConverter::to_svm_problem
std::unique_ptr< svm_problem > to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Convert PyTorch tensors to libsvm format.
Definition data_converter.cpp:22
+
svm_classifier::DataConverter::to_feature_node
feature_node * to_feature_node(const torch::Tensor &sample)
Convert single sample to liblinear format.
Definition data_converter.cpp:110
+
svm_classifier::DataConverter::to_linear_problem
std::unique_ptr< problem > to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Convert PyTorch tensors to liblinear format.
Definition data_converter.cpp:60
+
svm_classifier::DataConverter::validate_tensors
void validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Validate input tensors.
Definition data_converter.cpp:172
+
svm_classifier::DataConverter::from_predictions
torch::Tensor from_predictions(const std::vector< double > &predictions)
Convert predictions back to PyTorch tensor.
Definition data_converter.cpp:120
+
svm_classifier::DataConverter::~DataConverter
~DataConverter()
Destructor - cleans up allocated memory.
Definition data_converter.cpp:17
+
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/data__converter_8hpp_source.html b/data__converter_8hpp_source.html new file mode 100644 index 0000000..cadfc1c --- /dev/null +++ b/data__converter_8hpp_source.html @@ -0,0 +1,193 @@ + + + + + + + +SVM Classifier C++: include/svm_classifier/data_converter.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
+
+
+ + + + + + + + + +
+
+ + +
+
+
+
+
+
Loading...
+
Searching...
+
No Matches
+
+
+
+
+ + +
+
+
data_converter.hpp
+
+
+
1#pragma once
+
2
+
3#include "types.hpp"
+
4#include <torch/torch.h>
+
5#include <vector>
+
6#include <memory>
+
7
+
8// Forward declarations for libsvm and liblinear structures
+
9struct svm_node;
+
10struct svm_problem;
+
11struct feature_node;
+
12struct problem;
+
13
+
14namespace svm_classifier {
+
15
+
+
23 class DataConverter {
+
24 public:
+
28 DataConverter();
+
29
+
33 ~DataConverter();
+
34
+
41 std::unique_ptr<svm_problem> to_svm_problem(const torch::Tensor& X,
+
42 const torch::Tensor& y = torch::Tensor());
+
43
+
50 std::unique_ptr<problem> to_linear_problem(const torch::Tensor& X,
+
51 const torch::Tensor& y = torch::Tensor());
+
52
+
58 svm_node* to_svm_node(const torch::Tensor& sample);
+
59
+
65 feature_node* to_feature_node(const torch::Tensor& sample);
+
66
+
72 torch::Tensor from_predictions(const std::vector<double>& predictions);
+
73
+
79 torch::Tensor from_probabilities(const std::vector<std::vector<double>>& probabilities);
+
80
+
86 torch::Tensor from_decision_values(const std::vector<std::vector<double>>& decision_values);
+
87
+
94 void validate_tensors(const torch::Tensor& X, const torch::Tensor& y = torch::Tensor());
+
95
+
100 int get_n_features() const { return n_features_; }
+
101
+
106 int get_n_samples() const { return n_samples_; }
+
107
+
111 void cleanup();
+
112
+
117 void set_sparse_threshold(double threshold) { sparse_threshold_ = threshold; }
+
118
+
123 double get_sparse_threshold() const { return sparse_threshold_; }
+
124
+
125 private:
+
126 int n_features_;
+
127 int n_samples_;
+
128 double sparse_threshold_;
+
129
+
130 // Memory management for libsvm structures
+
131 std::vector<std::vector<svm_node>> svm_nodes_storage_;
+
132 std::vector<svm_node*> svm_x_space_;
+
133 std::vector<double> svm_y_space_;
+
134
+
135 // Memory management for liblinear structures
+
136 std::vector<std::vector<feature_node>> linear_nodes_storage_;
+
137 std::vector<feature_node*> linear_x_space_;
+
138 std::vector<double> linear_y_space_;
+
139
+
140 // Single sample storage (for prediction)
+
141 std::vector<svm_node> single_svm_nodes_;
+
142 std::vector<feature_node> single_linear_nodes_;
+
143
+
149 std::vector<std::vector<svm_node>> tensor_to_svm_nodes(const torch::Tensor& X);
+
150
+
156 std::vector<std::vector<feature_node>> tensor_to_linear_nodes(const torch::Tensor& X);
+
157
+
163 std::vector<svm_node> sample_to_svm_nodes(const torch::Tensor& sample);
+
164
+
170 std::vector<feature_node> sample_to_linear_nodes(const torch::Tensor& sample);
+
171
+
177 std::vector<double> extract_labels(const torch::Tensor& y);
+
178
+
184 torch::Tensor ensure_cpu_tensor(const torch::Tensor& tensor);
+
185
+
192 void validate_tensor_properties(const torch::Tensor& tensor, int expected_dims, const std::string& name);
+
193 };
+
+
194
+
195} // namespace svm_classifier
+
svm_classifier::DataConverter
Data converter between libtorch tensors and SVM library formats.
Definition data_converter.hpp:23
+
svm_classifier::DataConverter::get_n_samples
int get_n_samples() const
Get number of samples from last conversion.
Definition data_converter.hpp:106
+
svm_classifier::DataConverter::to_svm_node
svm_node * to_svm_node(const torch::Tensor &sample)
Convert single sample to libsvm format.
Definition data_converter.cpp:100
+
svm_classifier::DataConverter::get_sparse_threshold
double get_sparse_threshold() const
Get sparse threshold.
Definition data_converter.hpp:123
+
svm_classifier::DataConverter::get_n_features
int get_n_features() const
Get number of features from last conversion.
Definition data_converter.hpp:100
+
svm_classifier::DataConverter::cleanup
void cleanup()
Clean up all allocated memory.
Definition data_converter.cpp:198
+
svm_classifier::DataConverter::from_decision_values
torch::Tensor from_decision_values(const std::vector< std::vector< double > > &decision_values)
Convert decision values back to PyTorch tensor.
Definition data_converter.cpp:152
+
svm_classifier::DataConverter::from_probabilities
torch::Tensor from_probabilities(const std::vector< std::vector< double > > &probabilities)
Convert probabilities back to PyTorch tensor.
Definition data_converter.cpp:132
+
svm_classifier::DataConverter::DataConverter
DataConverter()
Default constructor.
Definition data_converter.cpp:10
+
svm_classifier::DataConverter::to_svm_problem
std::unique_ptr< svm_problem > to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Convert PyTorch tensors to libsvm format.
Definition data_converter.cpp:22
+
svm_classifier::DataConverter::to_feature_node
feature_node * to_feature_node(const torch::Tensor &sample)
Convert single sample to liblinear format.
Definition data_converter.cpp:110
+
svm_classifier::DataConverter::to_linear_problem
std::unique_ptr< problem > to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Convert PyTorch tensors to liblinear format.
Definition data_converter.cpp:60
+
svm_classifier::DataConverter::set_sparse_threshold
void set_sparse_threshold(double threshold)
Set sparse threshold (features with absolute value below this are ignored)
Definition data_converter.hpp:117
+
svm_classifier::DataConverter::validate_tensors
void validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Validate input tensors.
Definition data_converter.cpp:172
+
svm_classifier::DataConverter::from_predictions
torch::Tensor from_predictions(const std::vector< double > &predictions)
Convert predictions back to PyTorch tensor.
Definition data_converter.cpp:120
+
svm_classifier::DataConverter::~DataConverter
~DataConverter()
Destructor - cleans up allocated memory.
Definition data_converter.cpp:17
+
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+--nav-arrow-color: #9CAFD4; +--nav-arrow-selected-color: #9CAFD4; + +/* table of contents */ +--toc-background-color: #F4F6FA; +--toc-border-color: #D8DFEE; +--toc-header-color: #4665A2; +--toc-down-arrow-image: url("data:image/svg+xml;utf8,&%238595;"); + +/** search field */ +--search-background-color: white; +--search-foreground-color: #909090; +--search-magnification-image: url('mag.svg'); +--search-magnification-select-image: url('mag_sel.svg'); +--search-active-color: black; +--search-filter-background-color: #F9FAFC; +--search-filter-foreground-color: black; +--search-filter-border-color: #90A5CE; +--search-filter-highlight-text-color: white; +--search-filter-highlight-bg-color: #3D578C; +--search-results-foreground-color: #425E97; +--search-results-background-color: #EEF1F7; +--search-results-border-color: black; +--search-box-shadow: inset 0.5px 0.5px 3px 0px #555; + +/** code fragments */ +--code-keyword-color: #008000; +--code-type-keyword-color: #604020; +--code-flow-keyword-color: #E08000; +--code-comment-color: #800000; +--code-preprocessor-color: #806020; +--code-string-literal-color: #002080; +--code-char-literal-color: #008080; +--code-xml-cdata-color: black; +--code-vhdl-digit-color: #FF00FF; +--code-vhdl-char-color: #000000; +--code-vhdl-keyword-color: #700070; +--code-vhdl-logic-color: #FF0000; +--code-link-color: #4665A2; +--code-external-link-color: #4665A2; +--fragment-foreground-color: black; +--fragment-background-color: #FBFCFD; +--fragment-border-color: #C4CFE5; +--fragment-lineno-border-color: #00FF00; +--fragment-lineno-background-color: #E8E8E8; +--fragment-lineno-foreground-color: black; +--fragment-lineno-link-fg-color: #4665A2; +--fragment-lineno-link-bg-color: #D8D8D8; +--fragment-lineno-link-hover-fg-color: #4665A2; +--fragment-lineno-link-hover-bg-color: #C8C8C8; +--tooltip-foreground-color: black; +--tooltip-background-color: white; +--tooltip-border-color: gray; +--tooltip-doc-color: grey; +--tooltip-declaration-color: #006318; +--tooltip-link-color: #4665A2; +--tooltip-shadow: 1px 1px 7px gray; +--fold-line-color: #808080; +--fold-minus-image: url('minus.svg'); +--fold-plus-image: url('plus.svg'); +--fold-minus-image-relpath: url('../../minus.svg'); +--fold-plus-image-relpath: url('../../plus.svg'); + +/** font-family */ +--font-family-normal: Roboto,sans-serif; +--font-family-monospace: 'JetBrains Mono',Consolas,Monaco,'Andale Mono','Ubuntu Mono',monospace,fixed; +--font-family-nav: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; +--font-family-title: Tahoma,Arial,sans-serif; +--font-family-toc: Verdana,'DejaVu Sans',Geneva,sans-serif; +--font-family-search: Arial,Verdana,sans-serif; +--font-family-icon: Arial,Helvetica; +--font-family-tooltip: Roboto,sans-serif; + +} + +@media (prefers-color-scheme: dark) { + html:not(.dark-mode) { + color-scheme: dark; + +/* page base colors */ +--page-background-color: black; +--page-foreground-color: #C9D1D9; +--page-link-color: #90A5CE; +--page-visited-link-color: #A3B4D7; + +/* index */ +--index-odd-item-bg-color: #0B101A; +--index-even-item-bg-color: black; +--index-header-color: #C4CFE5; +--index-separator-color: #334975; + +/* header */ +--header-background-color: #070B11; +--header-separator-color: #141C2E; +--header-gradient-image: url('nav_hd.png'); +--group-header-separator-color: #283A5D; +--group-header-color: #90A5CE; +--inherit-header-color: #A0A0A0; + +--footer-foreground-color: #5B7AB7; +--footer-logo-width: 60px; +--citation-label-color: #90A5CE; +--glow-color: cyan; + +--title-background-color: #090D16; +--title-separator-color: #354C79; +--directory-separator-color: #283A5D; +--separator-color: #283A5D; + +--blockquote-background-color: #101826; +--blockquote-border-color: #283A5D; + +--scrollbar-thumb-color: #283A5D; +--scrollbar-background-color: #070B11; + +--icon-background-color: #334975; +--icon-foreground-color: #C4CFE5; +--icon-doc-image: url('docd.svg'); +--icon-folder-open-image: url('folderopend.svg'); +--icon-folder-closed-image: url('folderclosedd.svg'); + +/* brief member declaration list */ +--memdecl-background-color: #0B101A; +--memdecl-separator-color: #2C3F65; +--memdecl-foreground-color: #BBB; +--memdecl-template-color: #7C95C6; + +/* detailed member list */ +--memdef-border-color: #233250; +--memdef-title-background-color: #1B2840; +--memdef-title-gradient-image: url('nav_fd.png'); +--memdef-proto-background-color: #19243A; +--memdef-proto-text-color: #9DB0D4; +--memdef-proto-text-shadow: 0px 1px 1px rgba(0, 0, 0, 0.9); +--memdef-doc-background-color: black; +--memdef-param-name-color: #D28757; +--memdef-template-color: #7C95C6; + +/* tables */ +--table-cell-border-color: #283A5D; +--table-header-background-color: #283A5D; +--table-header-foreground-color: #C4CFE5; + +/* labels */ +--label-background-color: #354C7B; +--label-left-top-border-color: #4665A2; +--label-right-bottom-border-color: #283A5D; +--label-foreground-color: #CCCCCC; + +/** navigation bar/tree/menu */ +--nav-background-color: #101826; +--nav-foreground-color: #364D7C; +--nav-gradient-image: url('tab_bd.png'); +--nav-gradient-hover-image: url('tab_hd.png'); +--nav-gradient-active-image: url('tab_ad.png'); +--nav-gradient-active-image-parent: url("../tab_ad.png"); +--nav-separator-image: url('tab_sd.png'); +--nav-breadcrumb-image: url('bc_sd.png'); +--nav-breadcrumb-border-color: #2A3D61; +--nav-splitbar-image: url('splitbard.png'); +--nav-font-size-level1: 13px; +--nav-font-size-level2: 10px; +--nav-font-size-level3: 9px; +--nav-text-normal-color: #B6C4DF; +--nav-text-hover-color: #DCE2EF; +--nav-text-active-color: #DCE2EF; +--nav-text-normal-shadow: 0px 1px 1px black; +--nav-text-hover-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); +--nav-text-active-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); +--nav-menu-button-color: #B6C4DF; +--nav-menu-background-color: #05070C; +--nav-menu-foreground-color: #BBBBBB; +--nav-menu-toggle-color: rgba(255, 255, 255, 0.2); +--nav-arrow-color: #334975; +--nav-arrow-selected-color: #90A5CE; + +/* table of contents */ +--toc-background-color: #151E30; +--toc-border-color: #202E4A; +--toc-header-color: #A3B4D7; +--toc-down-arrow-image: url("data:image/svg+xml;utf8,&%238595;"); + +/** search field */ +--search-background-color: black; +--search-foreground-color: #C5C5C5; +--search-magnification-image: url('mag_d.svg'); +--search-magnification-select-image: url('mag_seld.svg'); +--search-active-color: #C5C5C5; +--search-filter-background-color: #101826; +--search-filter-foreground-color: #90A5CE; +--search-filter-border-color: #7C95C6; +--search-filter-highlight-text-color: #BCC9E2; +--search-filter-highlight-bg-color: #283A5D; +--search-results-background-color: #101826; +--search-results-foreground-color: #90A5CE; +--search-results-border-color: #7C95C6; +--search-box-shadow: inset 0.5px 0.5px 3px 0px #2F436C; + +/** code fragments */ +--code-keyword-color: #CC99CD; +--code-type-keyword-color: #AB99CD; +--code-flow-keyword-color: #E08000; +--code-comment-color: #717790; +--code-preprocessor-color: #65CABE; +--code-string-literal-color: #7EC699; +--code-char-literal-color: #00E0F0; +--code-xml-cdata-color: #C9D1D9; +--code-vhdl-digit-color: #FF00FF; +--code-vhdl-char-color: #C0C0C0; +--code-vhdl-keyword-color: #CF53C9; +--code-vhdl-logic-color: #FF0000; +--code-link-color: #79C0FF; +--code-external-link-color: #79C0FF; +--fragment-foreground-color: #C9D1D9; +--fragment-background-color: black; +--fragment-border-color: #30363D; +--fragment-lineno-border-color: #30363D; +--fragment-lineno-background-color: black; +--fragment-lineno-foreground-color: #6E7681; +--fragment-lineno-link-fg-color: #6E7681; +--fragment-lineno-link-bg-color: #303030; +--fragment-lineno-link-hover-fg-color: #8E96A1; +--fragment-lineno-link-hover-bg-color: #505050; +--tooltip-foreground-color: #C9D1D9; +--tooltip-background-color: #202020; +--tooltip-border-color: #C9D1D9; +--tooltip-doc-color: #D9E1E9; +--tooltip-declaration-color: #20C348; +--tooltip-link-color: #79C0FF; +--tooltip-shadow: none; +--fold-line-color: #808080; +--fold-minus-image: url('minusd.svg'); +--fold-plus-image: url('plusd.svg'); +--fold-minus-image-relpath: url('../../minusd.svg'); +--fold-plus-image-relpath: url('../../plusd.svg'); + +/** font-family */ +--font-family-normal: Roboto,sans-serif; +--font-family-monospace: 'JetBrains Mono',Consolas,Monaco,'Andale Mono','Ubuntu Mono',monospace,fixed; +--font-family-nav: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; +--font-family-title: Tahoma,Arial,sans-serif; +--font-family-toc: Verdana,'DejaVu Sans',Geneva,sans-serif; +--font-family-search: Arial,Verdana,sans-serif; +--font-family-icon: Arial,Helvetica; +--font-family-tooltip: Roboto,sans-serif; + +}} +body { + background-color: var(--page-background-color); + color: var(--page-foreground-color); +} + +body, table, div, p, dl { + font-weight: 400; + font-size: 14px; + font-family: var(--font-family-normal); + line-height: 22px; +} + +/* @group Heading Levels */ + +.title { + font-weight: 400; + font-size: 14px; + font-family: var(--font-family-normal); + line-height: 28px; + font-size: 150%; + font-weight: bold; + margin: 10px 2px; +} + +h1.groupheader { + font-size: 150%; +} + +h2.groupheader { + border-bottom: 1px solid var(--group-header-separator-color); + color: var(--group-header-color); + font-size: 150%; + font-weight: normal; + margin-top: 1.75em; + padding-top: 8px; + padding-bottom: 4px; + width: 100%; +} + +h3.groupheader { + font-size: 100%; +} + +h1, h2, h3, h4, h5, h6 { + -webkit-transition: text-shadow 0.5s linear; + -moz-transition: text-shadow 0.5s linear; + -ms-transition: text-shadow 0.5s linear; + -o-transition: text-shadow 0.5s linear; + transition: text-shadow 0.5s linear; + margin-right: 15px; +} + +h1.glow, h2.glow, h3.glow, h4.glow, h5.glow, h6.glow { + text-shadow: 0 0 15px var(--glow-color); +} + +dt { + font-weight: bold; +} + +p.startli, p.startdd { + margin-top: 2px; +} + +th p.starttd, th p.intertd, th p.endtd { + font-size: 100%; + font-weight: 700; +} + +p.starttd { + margin-top: 0px; +} + +p.endli { + margin-bottom: 0px; +} + +p.enddd { + margin-bottom: 4px; +} + +p.endtd { + margin-bottom: 2px; +} + +p.interli { +} + +p.interdd { +} + +p.intertd { +} + +/* @end */ + +caption { + font-weight: bold; +} + +span.legend { + font-size: 70%; + text-align: center; +} + +h3.version { + font-size: 90%; + text-align: center; +} + +div.navtab { + padding-right: 15px; + text-align: right; + line-height: 110%; +} + +div.navtab table { + border-spacing: 0; +} + +td.navtab { + padding-right: 6px; + padding-left: 6px; +} + +td.navtabHL { + background-image: var(--nav-gradient-active-image); + background-repeat:repeat-x; + padding-right: 6px; + padding-left: 6px; +} + +td.navtabHL a, td.navtabHL a:visited { + color: var(--nav-text-hover-color); + text-shadow: var(--nav-text-hover-shadow); +} + +a.navtab { + font-weight: bold; +} + +div.qindex{ + text-align: center; + width: 100%; + line-height: 140%; + font-size: 130%; + color: var(--index-separator-color); +} + +#main-menu a:focus { + outline: auto; + z-index: 10; + position: relative; +} + +dt.alphachar{ + font-size: 180%; + font-weight: bold; +} + +.alphachar a{ + color: var(--index-header-color); +} + +.alphachar a:hover, .alphachar a:visited{ + text-decoration: none; +} + +.classindex dl { + padding: 25px; + column-count:1 +} + +.classindex dd { + display:inline-block; + margin-left: 50px; + width: 90%; + line-height: 1.15em; +} + +.classindex dl.even { + background-color: var(--index-even-item-bg-color); +} + +.classindex dl.odd { + background-color: var(--index-odd-item-bg-color); +} + +@media(min-width: 1120px) { + .classindex dl { + column-count:2 + } +} + +@media(min-width: 1320px) { + .classindex dl { + column-count:3 + } +} + + +/* @group Link Styling */ + +a { + color: var(--page-link-color); + font-weight: normal; + text-decoration: none; +} + +.contents a:visited { + color: var(--page-visited-link-color); +} + +a:hover { + text-decoration: underline; +} + +a.el { + font-weight: bold; +} + +a.elRef { +} + +a.code, a.code:visited, a.line, a.line:visited { + color: var(--code-link-color); +} + +a.codeRef, a.codeRef:visited, a.lineRef, a.lineRef:visited { + color: var(--code-external-link-color); +} + +a.code.hl_class { /* style for links to class names in code snippets */ } +a.code.hl_struct { /* style for links to struct names in code snippets */ } +a.code.hl_union { /* style for links to union names in code snippets */ } +a.code.hl_interface { /* style for links to interface names in code snippets */ } +a.code.hl_protocol { /* style for links to protocol names in code snippets */ } +a.code.hl_category { /* style for links to category names in code snippets */ } +a.code.hl_exception { /* style for links to exception names in code snippets */ } +a.code.hl_service { /* style for links to service names in code snippets */ } +a.code.hl_singleton { /* style for links to singleton names in code snippets */ } +a.code.hl_concept { /* style for links to concept names in code snippets */ } +a.code.hl_namespace { /* style for links to namespace names in code snippets */ } +a.code.hl_package { /* style for links to package names in code snippets */ } +a.code.hl_define { /* style for links to macro names in code snippets */ } +a.code.hl_function { /* style for links to function names in code snippets */ } +a.code.hl_variable { /* style for links to variable names in code snippets */ } +a.code.hl_typedef { /* style for links to typedef names in code snippets */ } +a.code.hl_enumvalue { /* style for links to enum value names in code snippets */ } +a.code.hl_enumeration { /* style for links to enumeration names in code snippets */ } +a.code.hl_signal { /* style for links to Qt signal names in code snippets */ } +a.code.hl_slot { /* style for links to Qt slot names in code snippets */ } +a.code.hl_friend { /* style for links to friend names in code snippets */ } +a.code.hl_dcop { /* style for links to KDE3 DCOP names in code snippets */ } +a.code.hl_property { /* style for links to property names in code snippets */ } +a.code.hl_event { /* style for links to event names in code snippets */ } +a.code.hl_sequence { /* style for links to sequence names in code snippets */ } +a.code.hl_dictionary { /* style for links to dictionary names in code snippets */ } + +/* @end */ + +dl.el { + margin-left: -1cm; +} + +ul { + overflow: visible; +} + +ul.multicol { + -moz-column-gap: 1em; + -webkit-column-gap: 1em; + column-gap: 1em; + -moz-column-count: 3; + -webkit-column-count: 3; + column-count: 3; + list-style-type: none; +} + +#side-nav ul { + overflow: visible; /* reset ul rule for scroll bar in GENERATE_TREEVIEW window */ +} + +#main-nav ul { + overflow: visible; /* reset ul rule for the navigation bar drop down lists */ +} + +.fragment { + text-align: left; + direction: ltr; + overflow-x: auto; /*Fixed: fragment lines overlap floating elements*/ + overflow-y: hidden; +} + +pre.fragment { + border: 1px solid var(--fragment-border-color); + background-color: var(--fragment-background-color); + color: var(--fragment-foreground-color); + padding: 4px 6px; + margin: 4px 8px 4px 2px; + overflow: auto; + word-wrap: break-word; + font-size: 9pt; + line-height: 125%; + font-family: var(--font-family-monospace); + font-size: 105%; +} + +div.fragment { + padding: 0 0 1px 0; /*Fixed: last line underline overlap border*/ + margin: 4px 8px 4px 2px; + color: var(--fragment-foreground-color); + background-color: var(--fragment-background-color); + border: 1px solid var(--fragment-border-color); +} + +div.line { + font-family: var(--font-family-monospace); + font-size: 13px; + min-height: 13px; + line-height: 1.2; + text-wrap: unrestricted; + white-space: -moz-pre-wrap; /* Moz */ + white-space: -pre-wrap; /* Opera 4-6 */ + white-space: -o-pre-wrap; /* Opera 7 */ + white-space: pre-wrap; /* CSS3 */ + word-wrap: break-word; /* IE 5.5+ */ + text-indent: -53px; + padding-left: 53px; + padding-bottom: 0px; + margin: 0px; + -webkit-transition-property: background-color, box-shadow; + -webkit-transition-duration: 0.5s; + -moz-transition-property: background-color, box-shadow; + -moz-transition-duration: 0.5s; + -ms-transition-property: background-color, box-shadow; + -ms-transition-duration: 0.5s; + -o-transition-property: background-color, box-shadow; + -o-transition-duration: 0.5s; + transition-property: background-color, box-shadow; + transition-duration: 0.5s; +} + +div.line:after { + content:"\000A"; + white-space: pre; +} + +div.line.glow { + background-color: var(--glow-color); + box-shadow: 0 0 10px var(--glow-color); +} + +span.fold { + margin-left: 5px; + margin-right: 1px; + margin-top: 0px; + margin-bottom: 0px; + padding: 0px; + display: inline-block; + width: 12px; + height: 12px; + background-repeat:no-repeat; + background-position:center; +} + +span.lineno { + padding-right: 4px; + margin-right: 9px; + text-align: right; + border-right: 2px solid var(--fragment-lineno-border-color); + color: var(--fragment-lineno-foreground-color); + background-color: var(--fragment-lineno-background-color); + white-space: pre; +} +span.lineno a, span.lineno a:visited { + color: var(--fragment-lineno-link-fg-color); + background-color: var(--fragment-lineno-link-bg-color); +} + +span.lineno a:hover { + color: var(--fragment-lineno-link-hover-fg-color); + background-color: var(--fragment-lineno-link-hover-bg-color); +} + +.lineno { + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +div.classindex ul { + list-style: none; + padding-left: 0; +} + +div.classindex span.ai { + display: inline-block; +} + +div.groupHeader { + margin-left: 16px; + margin-top: 12px; + font-weight: bold; +} + +div.groupText { + margin-left: 16px; + font-style: italic; +} + +body { + color: var(--page-foreground-color); + margin: 0; +} + +div.contents { + margin-top: 10px; + margin-left: 12px; + margin-right: 8px; +} + +p.formulaDsp { + text-align: center; +} + +img.dark-mode-visible { + display: none; +} +img.light-mode-visible { + display: none; +} + +img.formulaDsp { + +} + +img.formulaInl, img.inline { + vertical-align: middle; +} + +div.center { + text-align: center; + margin-top: 0px; + margin-bottom: 0px; + padding: 0px; +} + +div.center img { + border: 0px; +} + +address.footer { + text-align: right; + padding-right: 12px; +} + +img.footer { + border: 0px; + vertical-align: middle; + width: var(--footer-logo-width); +} + +.compoundTemplParams { + color: var(--memdecl-template-color); + font-size: 80%; + line-height: 120%; +} + +/* @group Code Colorization */ + +span.keyword { + color: var(--code-keyword-color); +} + +span.keywordtype { + color: var(--code-type-keyword-color); +} + +span.keywordflow { + color: var(--code-flow-keyword-color); +} + +span.comment { + color: var(--code-comment-color); +} + +span.preprocessor { + color: var(--code-preprocessor-color); +} + +span.stringliteral { + color: var(--code-string-literal-color); +} + +span.charliteral { + color: var(--code-char-literal-color); +} + +span.xmlcdata { + color: var(--code-xml-cdata-color); +} + +span.vhdldigit { + color: var(--code-vhdl-digit-color); +} + +span.vhdlchar { + color: var(--code-vhdl-char-color); +} + +span.vhdlkeyword { + color: var(--code-vhdl-keyword-color); +} + +span.vhdllogic { + color: var(--code-vhdl-logic-color); +} + +blockquote { + background-color: var(--blockquote-background-color); + border-left: 2px solid var(--blockquote-border-color); + margin: 0 24px 0 4px; + padding: 0 12px 0 16px; +} + +/* @end */ + +td.tiny { + font-size: 75%; +} + +.dirtab { + padding: 4px; + border-collapse: collapse; + border: 1px solid var(--table-cell-border-color); +} + +th.dirtab { + background-color: var(--table-header-background-color); + color: var(--table-header-foreground-color); + font-weight: bold; +} + +hr { + height: 0px; + border: none; + border-top: 1px solid var(--separator-color); +} + +hr.footer { + height: 1px; +} + +/* @group Member Descriptions */ + +table.memberdecls { + border-spacing: 0px; + padding: 0px; +} + +.memberdecls td, .fieldtable tr { + -webkit-transition-property: background-color, box-shadow; + -webkit-transition-duration: 0.5s; + -moz-transition-property: background-color, box-shadow; + -moz-transition-duration: 0.5s; + -ms-transition-property: background-color, box-shadow; + -ms-transition-duration: 0.5s; + -o-transition-property: background-color, box-shadow; + -o-transition-duration: 0.5s; + transition-property: background-color, box-shadow; + transition-duration: 0.5s; +} + +.memberdecls td.glow, .fieldtable tr.glow { + background-color: var(--glow-color); + box-shadow: 0 0 15px var(--glow-color); +} + +.mdescLeft, .mdescRight, +.memItemLeft, .memItemRight, +.memTemplItemLeft, .memTemplItemRight, .memTemplParams { + background-color: var(--memdecl-background-color); + border: none; + margin: 4px; + padding: 1px 0 0 8px; +} + +.mdescLeft, .mdescRight { + padding: 0px 8px 4px 8px; + color: var(--memdecl-foreground-color); +} + +.memSeparator { + border-bottom: 1px solid var(--memdecl-separator-color); + line-height: 1px; + margin: 0px; + padding: 0px; +} + +.memItemLeft, .memTemplItemLeft { + white-space: nowrap; +} + +.memItemRight, .memTemplItemRight { + width: 100%; +} + +.memTemplParams { + color: var(--memdecl-template-color); + white-space: nowrap; + font-size: 80%; +} + +/* @end */ + +/* @group Member Details */ + +/* Styles for detailed member documentation */ + +.memtitle { + padding: 8px; + border-top: 1px solid var(--memdef-border-color); + border-left: 1px solid var(--memdef-border-color); + border-right: 1px solid var(--memdef-border-color); + border-top-right-radius: 4px; + border-top-left-radius: 4px; + margin-bottom: -1px; + background-image: var(--memdef-title-gradient-image); + background-repeat: repeat-x; + background-color: var(--memdef-title-background-color); + line-height: 1.25; + font-weight: 300; + float:left; +} + +.permalink +{ + font-size: 65%; + display: inline-block; + vertical-align: middle; +} + +.memtemplate { + font-size: 80%; + color: var(--memdef-template-color); + font-weight: normal; + margin-left: 9px; +} + +.mempage { + width: 100%; +} + +.memitem { + padding: 0; + margin-bottom: 10px; + margin-right: 5px; + -webkit-transition: box-shadow 0.5s linear; + -moz-transition: box-shadow 0.5s linear; + -ms-transition: box-shadow 0.5s linear; + -o-transition: box-shadow 0.5s linear; + transition: box-shadow 0.5s linear; + display: table !important; + width: 100%; +} + +.memitem.glow { + box-shadow: 0 0 15px var(--glow-color); +} + +.memname { + font-weight: 400; + margin-left: 6px; +} + +.memname td { + vertical-align: bottom; +} + +.memproto, dl.reflist dt { + border-top: 1px solid var(--memdef-border-color); + border-left: 1px solid var(--memdef-border-color); + border-right: 1px solid var(--memdef-border-color); + padding: 6px 0px 6px 0px; + color: var(--memdef-proto-text-color); + font-weight: bold; + text-shadow: var(--memdef-proto-text-shadow); + background-color: var(--memdef-proto-background-color); + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + border-top-right-radius: 4px; +} + +.overload { + font-family: var(--font-family-monospace); + font-size: 65%; +} + +.memdoc, dl.reflist dd { + border-bottom: 1px solid var(--memdef-border-color); + border-left: 1px solid var(--memdef-border-color); + border-right: 1px solid var(--memdef-border-color); + padding: 6px 10px 2px 10px; + border-top-width: 0; + background-image:url('nav_g.png'); + background-repeat:repeat-x; + background-color: var(--memdef-doc-background-color); + /* opera specific markup */ + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); + /* firefox specific markup */ + -moz-border-radius-bottomleft: 4px; + -moz-border-radius-bottomright: 4px; + -moz-box-shadow: rgba(0, 0, 0, 0.15) 5px 5px 5px; + /* webkit specific markup */ + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +dl.reflist dt { + padding: 5px; +} + +dl.reflist dd { + margin: 0px 0px 10px 0px; + padding: 5px; +} + +.paramkey { + text-align: right; +} + +.paramtype { + white-space: nowrap; +} + +.paramname { + color: var(--memdef-param-name-color); + white-space: nowrap; +} +.paramname em { + font-style: normal; +} +.paramname code { + line-height: 14px; +} + +.params, .retval, .exception, .tparams { + margin-left: 0px; + padding-left: 0px; +} + +.params .paramname, .retval .paramname, .tparams .paramname, .exception .paramname { + font-weight: bold; + vertical-align: top; +} + +.params .paramtype, .tparams .paramtype { + font-style: italic; + vertical-align: top; +} + +.params .paramdir, .tparams .paramdir { + font-family: var(--font-family-monospace); + vertical-align: top; +} + +table.mlabels { + border-spacing: 0px; +} + +td.mlabels-left { + width: 100%; + padding: 0px; +} + +td.mlabels-right { + vertical-align: bottom; + padding: 0px; + white-space: nowrap; +} + +span.mlabels { + margin-left: 8px; +} + +span.mlabel { + background-color: var(--label-background-color); + border-top:1px solid var(--label-left-top-border-color); + border-left:1px solid var(--label-left-top-border-color); + border-right:1px solid var(--label-right-bottom-border-color); + border-bottom:1px solid var(--label-right-bottom-border-color); + text-shadow: none; + color: var(--label-foreground-color); + margin-right: 4px; + padding: 2px 3px; + border-radius: 3px; + font-size: 7pt; + white-space: nowrap; + vertical-align: middle; +} + + + +/* @end */ + +/* these are for tree view inside a (index) page */ + +div.directory { + margin: 10px 0px; + border-top: 1px solid var(--directory-separator-color); + border-bottom: 1px solid var(--directory-separator-color); + width: 100%; +} + +.directory table { + border-collapse:collapse; +} + +.directory td { + margin: 0px; + padding: 0px; + vertical-align: top; +} + +.directory td.entry { + white-space: nowrap; + padding-right: 6px; + padding-top: 3px; +} + +.directory td.entry a { + outline:none; +} + +.directory td.entry a img { + border: none; +} + +.directory td.desc { + width: 100%; + padding-left: 6px; + padding-right: 6px; + padding-top: 3px; + border-left: 1px solid rgba(0,0,0,0.05); +} + +.directory tr.odd { + padding-left: 6px; + background-color: var(--index-odd-item-bg-color); +} + +.directory tr.even { + padding-left: 6px; + background-color: var(--index-even-item-bg-color); +} + +.directory img { + vertical-align: -30%; +} + +.directory .levels { + white-space: nowrap; + width: 100%; + text-align: right; + font-size: 9pt; +} + +.directory .levels span { + cursor: pointer; + padding-left: 2px; + padding-right: 2px; + color: var(--page-link-color); +} + +.arrow { + color: var(--nav-arrow-color); + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + cursor: pointer; + font-size: 80%; + display: inline-block; + width: 16px; + height: 22px; +} + +.icon { + font-family: var(--font-family-icon); + line-height: normal; + font-weight: bold; + font-size: 12px; + height: 14px; + width: 16px; + display: inline-block; + background-color: var(--icon-background-color); + color: var(--icon-foreground-color); + text-align: center; + border-radius: 4px; + margin-left: 2px; + margin-right: 2px; +} + +.icona { + width: 24px; + height: 22px; + display: inline-block; +} + +.iconfopen { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:var(--icon-folder-open-image); + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +.iconfclosed { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:var(--icon-folder-closed-image); + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +.icondoc { + width: 24px; + height: 18px; + margin-bottom: 4px; + background-image:var(--icon-doc-image); + background-position: 0px -4px; + background-repeat: repeat-y; + vertical-align:top; + display: inline-block; +} + +/* @end */ + +div.dynheader { + margin-top: 8px; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +address { + font-style: normal; + color: var(--footer-foreground-color); +} + +table.doxtable caption { + caption-side: top; +} + +table.doxtable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.doxtable td, table.doxtable th { + border: 1px solid var(--table-cell-border-color); + padding: 3px 7px 2px; +} + +table.doxtable th { + background-color: var(--table-header-background-color); + color: var(--table-header-foreground-color); + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +table.fieldtable { + margin-bottom: 10px; + border: 1px solid var(--memdef-border-color); + border-spacing: 0px; + border-radius: 4px; + box-shadow: 2px 2px 2px rgba(0, 0, 0, 0.15); +} + +.fieldtable td, .fieldtable th { + padding: 3px 7px 2px; +} + +.fieldtable td.fieldtype, .fieldtable td.fieldname { + white-space: nowrap; + border-right: 1px solid var(--memdef-border-color); + border-bottom: 1px solid var(--memdef-border-color); + vertical-align: top; +} + +.fieldtable td.fieldname { + padding-top: 3px; +} + +.fieldtable td.fielddoc { + border-bottom: 1px solid var(--memdef-border-color); +} + +.fieldtable td.fielddoc p:first-child { + margin-top: 0px; +} + +.fieldtable td.fielddoc p:last-child { + margin-bottom: 2px; +} + +.fieldtable tr:last-child td { + border-bottom: none; +} + +.fieldtable th { + background-image: var(--memdef-title-gradient-image); + background-repeat:repeat-x; + background-color: var(--memdef-title-background-color); + font-size: 90%; + color: var(--memdef-proto-text-color); + padding-bottom: 4px; + padding-top: 5px; + text-align:left; + font-weight: 400; + border-top-left-radius: 4px; + border-top-right-radius: 4px; + border-bottom: 1px solid var(--memdef-border-color); +} + + +.tabsearch { + top: 0px; + left: 10px; + height: 36px; + background-image: var(--nav-gradient-image); + z-index: 101; + overflow: hidden; + font-size: 13px; +} + +.navpath ul +{ + font-size: 11px; + background-image: var(--nav-gradient-image); + background-repeat:repeat-x; + background-position: 0 -5px; + height:30px; + line-height:30px; + color:var(--nav-text-normal-color); + border:solid 1px var(--nav-breadcrumb-border-color); + overflow:hidden; + margin:0px; + padding:0px; +} + +.navpath li +{ + list-style-type:none; + float:left; + padding-left:10px; + padding-right:15px; + background-image:var(--nav-breadcrumb-image); + background-repeat:no-repeat; + background-position:right; + color: var(--nav-foreground-color); +} + +.navpath li.navelem a +{ + height:32px; + display:block; + text-decoration: none; + outline: none; + color: var(--nav-text-normal-color); + font-family: var(--font-family-nav); + text-shadow: var(--nav-text-normal-shadow); + text-decoration: none; +} + +.navpath li.navelem a:hover +{ + color: var(--nav-text-hover-color); + text-shadow: var(--nav-text-hover-shadow); +} + +.navpath li.footer +{ + list-style-type:none; + float:right; + padding-left:10px; + padding-right:15px; + background-image:none; + background-repeat:no-repeat; + background-position:right; + color: var(--footer-foreground-color); + font-size: 8pt; +} + + +div.summary +{ + float: right; + font-size: 8pt; + padding-right: 5px; + width: 50%; + text-align: right; +} + +div.summary a +{ + white-space: nowrap; +} + +table.classindex +{ + margin: 10px; + white-space: nowrap; + margin-left: 3%; + margin-right: 3%; + width: 94%; + border: 0; + border-spacing: 0; + padding: 0; +} + +div.ingroups +{ + font-size: 8pt; + width: 50%; + text-align: left; +} + +div.ingroups a +{ + white-space: nowrap; +} + +div.header +{ + background-image: var(--header-gradient-image); + background-repeat:repeat-x; + background-color: var(--header-background-color); + margin: 0px; + border-bottom: 1px solid var(--header-separator-color); +} + +div.headertitle +{ + padding: 5px 5px 5px 10px; +} + +.PageDocRTL-title div.headertitle { + text-align: right; + direction: rtl; +} + +dl { + padding: 0 0 0 0; +} + +/* dl.note, dl.warning, dl.attention, dl.pre, dl.post, dl.invariant, dl.deprecated, dl.todo, dl.test, dl.bug, dl.examples */ +dl.section { + margin-left: 0px; + padding-left: 0px; +} + +dl.note { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #D0C000; +} + +dl.warning, dl.attention { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #FF0000; +} + +dl.pre, dl.post, dl.invariant { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #00D000; +} + +dl.deprecated { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #505050; +} + +dl.todo { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #00C0E0; +} + +dl.test { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #3030E0; +} + +dl.bug { + margin-left: -7px; + padding-left: 3px; + border-left: 4px solid; + border-color: #C08050; +} + +dl.section dd { + margin-bottom: 6px; +} + + +#projectrow +{ + height: 56px; +} + +#projectlogo +{ + text-align: center; + vertical-align: bottom; + border-collapse: separate; +} + +#projectlogo img +{ + border: 0px none; +} + +#projectalign +{ + vertical-align: middle; + padding-left: 0.5em; +} + +#projectname +{ + font-size: 200%; + font-family: var(--font-family-title); + margin: 0px; + padding: 2px 0px; +} + +#projectbrief +{ + font-size: 90%; + font-family: var(--font-family-title); + margin: 0px; + padding: 0px; +} + +#projectnumber +{ + font-size: 50%; + font-family: 50% var(--font-family-title); + margin: 0px; + padding: 0px; +} + +#titlearea +{ + padding: 0px; + margin: 0px; + width: 100%; + border-bottom: 1px solid var(--title-separator-color); + background-color: var(--title-background-color); +} + +.image +{ + text-align: center; +} + +.dotgraph +{ + text-align: center; +} + +.mscgraph +{ + text-align: center; +} + +.plantumlgraph +{ + text-align: center; +} + +.diagraph +{ + text-align: center; +} + +.caption +{ + font-weight: bold; +} + +dl.citelist { + margin-bottom:50px; +} + +dl.citelist dt { + color:var(--citation-label-color); + float:left; + font-weight:bold; + margin-right:10px; + padding:5px; + text-align:right; + width:52px; +} + +dl.citelist dd { + margin:2px 0 2px 72px; + padding:5px 0; +} + +div.toc { + padding: 14px 25px; + background-color: var(--toc-background-color); + border: 1px solid var(--toc-border-color); + border-radius: 7px 7px 7px 7px; + float: right; + height: auto; + margin: 0 8px 10px 10px; + width: 200px; +} + +div.toc li { + background: var(--toc-down-arrow-image) no-repeat scroll 0 5px transparent; + font: 10px/1.2 var(--font-family-toc); + margin-top: 5px; + padding-left: 10px; + padding-top: 2px; +} + +div.toc h3 { + font: bold 12px/1.2 var(--font-family-toc); + color: var(--toc-header-color); + border-bottom: 0 none; + margin: 0; +} + +div.toc ul { + list-style: none outside none; + border: medium none; + padding: 0px; +} + +div.toc li.level1 { + margin-left: 0px; +} + +div.toc li.level2 { + margin-left: 15px; +} + +div.toc li.level3 { + margin-left: 15px; +} + +div.toc li.level4 { + margin-left: 15px; +} + +span.emoji { + /* font family used at the site: https://unicode.org/emoji/charts/full-emoji-list.html + * font-family: "Noto Color Emoji", "Apple Color Emoji", "Segoe UI Emoji", Times, Symbola, Aegyptus, Code2000, Code2001, Code2002, Musica, serif, LastResort; + */ +} + +span.obfuscator { + display: none; +} + +.inherit_header { + font-weight: bold; + color: var(--inherit-header-color); + cursor: pointer; + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} + +.inherit_header td { + padding: 6px 0px 2px 5px; +} + +.inherit { + display: none; +} + +tr.heading h2 { + margin-top: 12px; + margin-bottom: 4px; +} + +/* tooltip related style info */ + +.ttc { + position: absolute; + display: none; +} + +#powerTip { + cursor: default; + /*white-space: nowrap;*/ + color: var(--tooltip-foreground-color); + background-color: var(--tooltip-background-color); + border: 1px solid var(--tooltip-border-color); + border-radius: 4px 4px 4px 4px; + box-shadow: var(--tooltip-shadow); + display: none; + font-size: smaller; + max-width: 80%; + opacity: 0.9; + padding: 1ex 1em 1em; + position: absolute; + z-index: 2147483647; +} + +#powerTip div.ttdoc { + color: var(--tooltip-doc-color); + font-style: italic; +} + +#powerTip div.ttname a { + font-weight: bold; +} + +#powerTip a { + color: var(--tooltip-link-color); +} + +#powerTip div.ttname { + font-weight: bold; +} + +#powerTip div.ttdeci { + color: var(--tooltip-declaration-color); +} + +#powerTip div { + margin: 0px; + padding: 0px; + font-size: 12px; + font-family: var(--font-family-tooltip); + line-height: 16px; +} + +#powerTip:before, #powerTip:after { + content: ""; + position: absolute; + margin: 0px; +} + +#powerTip.n:after, #powerTip.n:before, +#powerTip.s:after, #powerTip.s:before, +#powerTip.w:after, #powerTip.w:before, +#powerTip.e:after, #powerTip.e:before, +#powerTip.ne:after, #powerTip.ne:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.nw:after, #powerTip.nw:before, +#powerTip.sw:after, #powerTip.sw:before { + border: solid transparent; + content: " "; + height: 0; + width: 0; + position: absolute; +} + +#powerTip.n:after, #powerTip.s:after, +#powerTip.w:after, #powerTip.e:after, +#powerTip.nw:after, #powerTip.ne:after, +#powerTip.sw:after, #powerTip.se:after { + border-color: rgba(255, 255, 255, 0); +} + +#powerTip.n:before, #powerTip.s:before, +#powerTip.w:before, #powerTip.e:before, +#powerTip.nw:before, #powerTip.ne:before, +#powerTip.sw:before, #powerTip.se:before { + border-color: rgba(128, 128, 128, 0); +} + +#powerTip.n:after, #powerTip.n:before, +#powerTip.ne:after, #powerTip.ne:before, +#powerTip.nw:after, #powerTip.nw:before { + top: 100%; +} + +#powerTip.n:after, #powerTip.ne:after, #powerTip.nw:after { + border-top-color: var(--tooltip-background-color); + border-width: 10px; + margin: 0px -10px; +} +#powerTip.n:before, #powerTip.ne:before, #powerTip.nw:before { + border-top-color: var(--tooltip-border-color); + border-width: 11px; + margin: 0px -11px; +} +#powerTip.n:after, #powerTip.n:before { + left: 50%; +} + +#powerTip.nw:after, #powerTip.nw:before { + right: 14px; +} + +#powerTip.ne:after, #powerTip.ne:before { + left: 14px; +} + +#powerTip.s:after, #powerTip.s:before, +#powerTip.se:after, #powerTip.se:before, +#powerTip.sw:after, #powerTip.sw:before { + bottom: 100%; +} + +#powerTip.s:after, #powerTip.se:after, #powerTip.sw:after { + border-bottom-color: var(--tooltip-background-color); + border-width: 10px; + margin: 0px -10px; +} + +#powerTip.s:before, #powerTip.se:before, #powerTip.sw:before { + border-bottom-color: var(--tooltip-border-color); + border-width: 11px; + margin: 0px -11px; +} + +#powerTip.s:after, #powerTip.s:before { + left: 50%; +} + +#powerTip.sw:after, #powerTip.sw:before { + right: 14px; +} + +#powerTip.se:after, #powerTip.se:before { + left: 14px; +} + +#powerTip.e:after, #powerTip.e:before { + left: 100%; +} +#powerTip.e:after { + border-left-color: var(--tooltip-border-color); + border-width: 10px; + top: 50%; + margin-top: -10px; +} +#powerTip.e:before { + border-left-color: var(--tooltip-border-color); + border-width: 11px; + top: 50%; + margin-top: -11px; +} + +#powerTip.w:after, #powerTip.w:before { + right: 100%; +} +#powerTip.w:after { + border-right-color: var(--tooltip-border-color); + border-width: 10px; + top: 50%; + margin-top: -10px; +} +#powerTip.w:before { + border-right-color: var(--tooltip-border-color); + border-width: 11px; + top: 50%; + margin-top: -11px; +} + +@media print +{ + #top { display: none; } + #side-nav { display: none; } + #nav-path { display: none; } + body { overflow:visible; } + h1, h2, h3, h4, h5, h6 { page-break-after: avoid; } + .summary { display: none; } + .memitem { page-break-inside: avoid; } + #doc-content + { + margin-left:0 !important; + height:auto !important; + width:auto !important; + overflow:inherit; + display:inline; + } +} + +/* @group Markdown */ + +table.markdownTable { + border-collapse:collapse; + margin-top: 4px; + margin-bottom: 4px; +} + +table.markdownTable td, table.markdownTable th { + border: 1px solid var(--table-cell-border-color); + padding: 3px 7px 2px; +} + +table.markdownTable tr { +} + +th.markdownTableHeadLeft, th.markdownTableHeadRight, th.markdownTableHeadCenter, th.markdownTableHeadNone { + background-color: var(--table-header-background-color); + color: var(--table-header-foreground-color); + font-size: 110%; + padding-bottom: 4px; + padding-top: 5px; +} + +th.markdownTableHeadLeft, td.markdownTableBodyLeft { + text-align: left +} + +th.markdownTableHeadRight, td.markdownTableBodyRight { + text-align: right +} + +th.markdownTableHeadCenter, td.markdownTableBodyCenter { + text-align: center +} + +tt, code, kbd, samp +{ + display: inline-block; +} +/* @end */ + +u { + text-decoration: underline; +} + +details>summary { + list-style-type: none; +} + +details > summary::-webkit-details-marker { + display: none; +} + +details>summary::before { + content: "\25ba"; + padding-right:4px; + font-size: 80%; +} + +details[open]>summary::before { + content: "\25bc"; + padding-right:4px; + font-size: 80%; +} + +body { + scrollbar-color: var(--scrollbar-thumb-color) var(--scrollbar-background-color); +} + +::-webkit-scrollbar { + background-color: var(--scrollbar-background-color); + height: 12px; + width: 12px; +} +::-webkit-scrollbar-thumb { + border-radius: 6px; 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 ▼ include
 ▼ svm_classifier
 data_converter.hpp
 kernel_parameters.hpp
 multiclass_strategy.hpp
 svm_classifier.hpp
 types.hpp
 ▼ src
 data_converter.cpp
 kernel_parameters.cpp
 multiclass_strategy.cpp
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Consider the following example:

/*! Invisible class because of truncation */
+
class Invisible { };
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/*! Truncated class, inheritance relation is hidden */
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class Truncated : public Invisible { };
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/* Class not documented with doxygen comments */
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class Undocumented { };
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/*! Class that is inherited using public inheritance */
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class PublicBase : public Truncated { };
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template<class T> class Templ { };
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private:
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Used *m_usedClass;
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This will result in the following graph:

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The boxes in the above graph have the following meaning:

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+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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Class Hierarchy
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Go to the graphical class hierarchy

+This inheritance list is sorted roughly, but not completely, alphabetically:
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[detail level 12]
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 Csvm_classifier::DataConverterData converter between libtorch tensors and SVM library formats
 Csvm_classifier::EvaluationMetricsModel evaluation metrics
 ▼Csvm_classifier::MulticlassStrategyBaseAbstract base class for multiclass classification strategies
 Csvm_classifier::OneVsOneStrategyOne-vs-One (OvO) multiclass strategy
 Csvm_classifier::OneVsRestStrategyOne-vs-Rest (OvR) multiclass strategy
 Csvm_classifier::PredictionResultPrediction result structure
 Csvm_classifier::SVMClassifierSupport Vector Machine Classifier with scikit-learn compatible API
 Csvm_classifier::TrainingMetricsTraining metrics structure
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+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/index.html b/index.html new file mode 100644 index 0000000..2d40763 --- /dev/null +++ b/index.html @@ -0,0 +1,324 @@ + + + + + + + +SVM Classifier C++: SVM Classifier C++ + + + + + + + + + +
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SVM Classifier C++ 1.0.0 +
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High-performance Support Vector Machine classifier with scikit-learn compatible API
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SVM Classifier C++
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+

A high-performance Support Vector Machine classifier implementation in C++ with a scikit-learn compatible API. This library provides a unified interface for SVM classification using both liblinear (for linear kernels) and libsvm (for non-linear kernels), with support for multiclass classification and PyTorch tensor integration.

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Features

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  • 🚀 Scikit-learn Compatible API: Familiar fit(), predict(), predict_proba(), score() methods
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  • 🔧 Multiple Kernels: Linear, RBF, Polynomial, and Sigmoid kernels
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  • 📊 Multiclass Support: One-vs-Rest (OvR) and One-vs-One (OvO) strategies
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  • ⚡ Automatic Library Selection: Uses liblinear for linear kernels, libsvm for others
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  • 🔗 PyTorch Integration: Native support for libtorch tensors
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  • ⚙️ JSON Configuration: Easy parameter management with nlohmann::json
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  • 🧪 Comprehensive Testing: 100% test coverage with Catch2
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  • 📈 Performance Metrics: Detailed evaluation and training metrics
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  • 🔍 Cross-Validation: Built-in k-fold cross-validation support
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  • 🎯 Grid Search: Hyperparameter optimization capabilities
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+

Quick Start

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Prerequisites

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  • C++17 or later
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  • CMake 3.15+
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  • libtorch
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  • Git
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+

Building

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git clone <repository-url>
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cd svm_classifier
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mkdir build && cd build
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cmake ..
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make -j$(nproc)
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Basic Usage

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#include <svm_classifier/svm_classifier.hpp>
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#include <torch/torch.h>
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using namespace svm_classifier;
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// Create sample data
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auto X = torch::randn({100, 2}); // 100 samples, 2 features
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auto y = torch::randint(0, 3, {100}); // 3 classes
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// Create and train SVM
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SVMClassifier svm(KernelType::RBF, 1.0);
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auto metrics = svm.fit(X, y);
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// Make predictions
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auto predictions = svm.predict(X);
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auto probabilities = svm.predict_proba(X);
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double accuracy = svm.score(X, y);
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svm_classifier::SVMClassifier
Support Vector Machine Classifier with scikit-learn compatible API.
Definition svm_classifier.hpp:21
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JSON Configuration

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#include <nlohmann/json.hpp>
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nlohmann::json config = {
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{"kernel", "rbf"},
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{"C", 10.0},
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{"gamma", 0.1},
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{"multiclass_strategy", "ovo"},
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{"probability", true}
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};
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SVMClassifier svm(config);
+

API Reference

+

Constructor Options

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// Default constructor
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SVMClassifier svm;
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// With explicit parameters
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SVMClassifier svm(KernelType::RBF, 1.0, MulticlassStrategy::ONE_VS_REST);
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// From JSON configuration
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SVMClassifier svm(config_json);
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Core Methods

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Method Description Returns
fit(X, y) Train the classifier TrainingMetrics
predict(X) Predict class labels torch::Tensor
predict_proba(X) Predict class probabilities torch::Tensor
score(X, y) Calculate accuracy double
decision_function(X) Get decision values torch::Tensor
cross_validate(X, y, cv) K-fold cross-validation std::vector<double>
grid_search(X, y, grid, cv) Hyperparameter tuning nlohmann::json
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Parameter Configuration

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Common Parameters

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  • kernel: "linear", "rbf", "polynomial", "sigmoid"
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  • C: Regularization parameter (default: 1.0)
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  • multiclass_strategy: "ovr" (One-vs-Rest) or "ovo" (One-vs-One)
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  • probability: Enable probability estimates (default: false)
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  • tolerance: Convergence tolerance (default: 1e-3)
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Kernel-Specific Parameters

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  • RBF/Polynomial/Sigmoid: gamma (default: auto)
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  • Polynomial: degree (default: 3), coef0 (default: 0.0)
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  • Sigmoid: coef0 (default: 0.0)
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Examples

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Multi-class Classification

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// Generate multi-class dataset
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auto X = torch::randn({300, 4});
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auto y = torch::randint(0, 5, {300}); // 5 classes
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// Configure for multi-class
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nlohmann::json config = {
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{"kernel", "rbf"},
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{"C", 1.0},
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{"gamma", 0.1},
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{"multiclass_strategy", "ovo"},
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{"probability", true}
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};
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SVMClassifier svm(config);
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auto metrics = svm.fit(X, y);
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// Evaluate
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auto eval_metrics = svm.evaluate(X, y);
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std::cout << "Accuracy: " << eval_metrics.accuracy << std::endl;
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std::cout << "F1-Score: " << eval_metrics.f1_score << std::endl;
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svm_classifier::SVMClassifier::evaluate
EvaluationMetrics evaluate(const torch::Tensor &X, const torch::Tensor &y_true)
Calculate detailed evaluation metrics.
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svm_classifier::SVMClassifier::fit
TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y)
Train the SVM classifier.
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svm_classifier::EvaluationMetrics::accuracy
double accuracy
Classification accuracy.
Definition types.hpp:71
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Cross-Validation

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SVMClassifier svm(KernelType::RBF);
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auto cv_scores = svm.cross_validate(X, y, 5); // 5-fold CV
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double mean_score = 0.0;
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for (auto score : cv_scores) {
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mean_score += score;
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}
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mean_score /= cv_scores.size();
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svm_classifier::SVMClassifier::cross_validate
std::vector< double > cross_validate(const torch::Tensor &X, const torch::Tensor &y, int cv=5)
Perform cross-validation.
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Grid Search

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nlohmann::json param_grid = {
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{"C", {0.1, 1.0, 10.0}},
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{"gamma", {0.01, 0.1, 1.0}},
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{"kernel", {"rbf", "polynomial"}}
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};
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auto best_params = svm.grid_search(X, y, param_grid, 3);
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std::cout << "Best parameters: " << best_params.dump(2) << std::endl;
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svm_classifier::SVMClassifier::grid_search
nlohmann::json grid_search(const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)
Find optimal hyperparameters using grid search.
+

Testing

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Run All Tests

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cd build
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make test_all
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Test Categories

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make test_unit # Unit tests
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make test_integration # Integration tests
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make test_performance # Performance tests
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Coverage Report

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cmake -DCMAKE_BUILD_TYPE=Debug ..
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make coverage
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The coverage report will be generated in build/coverage_html/index.html.

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Project Structure

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svm_classifier/
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├── include/svm_classifier/ # Public headers
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│ ├── svm_classifier.hpp # Main classifier interface
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│ ├── data_converter.hpp # Tensor conversion utilities
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│ ├── multiclass_strategy.hpp # Multiclass strategies
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│ ├── kernel_parameters.hpp # Parameter management
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│ └── types.hpp # Common types and enums
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├── src/ # Implementation files
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├── tests/ # Comprehensive test suite
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├── examples/ # Usage examples
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├── external/ # Third-party dependencies
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└── CMakeLists.txt # Build configuration
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Dependencies

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Required

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  • libtorch: PyTorch C++ API for tensor operations
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  • liblinear: Linear SVM implementation
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  • libsvm: Non-linear SVM implementation
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  • nlohmann/json: JSON configuration handling
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Testing

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  • Catch2: Testing framework
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Build System

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  • CMake: Cross-platform build system
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+

Performance Characteristics

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Memory Usage

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  • Efficient sparse data handling
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  • Automatic memory management for SVM structures
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  • Configurable cache sizes for large datasets
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Speed

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  • Linear kernels: Uses highly optimized liblinear
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  • Non-linear kernels: Uses proven libsvm implementation
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  • Multi-threading support via libtorch
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Scalability

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  • Handles datasets from hundreds to millions of samples
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  • Memory-efficient data conversion
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  • Sparse feature support
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Library Selection Logic

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The classifier automatically selects the appropriate underlying library:

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  • Linear Kernel → liblinear (optimized for linear classification)
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  • RBF/Polynomial/Sigmoid → libsvm (supports arbitrary kernels)
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This ensures optimal performance for each kernel type while maintaining a unified API.

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Contributing

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  1. Fork the repository
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  2. Create a feature branch
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  3. Add tests for new functionality
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  4. Ensure all tests pass: make test_all
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  5. Check code coverage: make coverage
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  6. Submit a pull request
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+

Code Style

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  • Follow modern C++17 conventions
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  • Use RAII for resource management
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  • Comprehensive error handling
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  • Document all public APIs
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+

License

+

[Specify your license here]

+

Acknowledgments

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  • libsvm: Chih-Chung Chang and Chih-Jen Lin
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  • liblinear: Fan et al.
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  • PyTorch: Facebook AI Research
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  • nlohmann/json: Niels Lohmann
    • +
  • Catch2: Phil Nash and contributors
    • +
+
+
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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/dev/null +++ b/kernel__parameters_8cpp_source.html @@ -0,0 +1,439 @@ + + + + + + + +SVM Classifier C++: src/kernel_parameters.cpp Source File + + + + + + + + + +
+
+ + + + + + +
+
SVM Classifier C++ 1.0.0 +
+
High-performance Support Vector Machine classifier with scikit-learn compatible API
+
+
+ + + + + + + + + +
+
+ + +
+
+
+
+
+
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+
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+
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+
kernel_parameters.cpp
+
+
+
1#include "svm_classifier/kernel_parameters.hpp"
+
2#include <stdexcept>
+
3#include <cmath>
+
4
+
5namespace svm_classifier {
+
6
+
7 KernelParameters::KernelParameters()
+
8 : kernel_type_(KernelType::LINEAR)
+
9 , multiclass_strategy_(MulticlassStrategy::ONE_VS_REST)
+
10 , C_(1.0)
+
11 , tolerance_(1e-3)
+
12 , max_iterations_(-1)
+
13 , probability_(false)
+
14 , gamma_(-1.0) // Auto gamma
+
15 , degree_(3)
+
16 , coef0_(0.0)
+
17 , cache_size_(200.0)
+
18 {
+
19 }
+
20
+
21 KernelParameters::KernelParameters(const nlohmann::json& config) : KernelParameters()
+
22 {
+
23 set_parameters(config);
+
24 }
+
25
+
26 void KernelParameters::set_parameters(const nlohmann::json& config)
+
27 {
+
28 // Set kernel type first as it affects validation
+
29 if (config.contains("kernel")) {
+
30 if (config["kernel"].is_string()) {
+
31 set_kernel_type(string_to_kernel_type(config["kernel"]));
+
32 } else {
+
33 throw std::invalid_argument("Kernel must be a string");
+
34 }
+
35 }
+
36
+
37 // Set multiclass strategy
+
38 if (config.contains("multiclass_strategy")) {
+
39 if (config["multiclass_strategy"].is_string()) {
+
40 set_multiclass_strategy(string_to_multiclass_strategy(config["multiclass_strategy"]));
+
41 } else {
+
42 throw std::invalid_argument("Multiclass strategy must be a string");
+
43 }
+
44 }
+
45
+
46 // Set common parameters
+
47 if (config.contains("C")) {
+
48 if (config["C"].is_number()) {
+
49 set_C(config["C"]);
+
50 } else {
+
51 throw std::invalid_argument("C must be a number");
+
52 }
+
53 }
+
54
+
55 if (config.contains("tolerance")) {
+
56 if (config["tolerance"].is_number()) {
+
57 set_tolerance(config["tolerance"]);
+
58 } else {
+
59 throw std::invalid_argument("Tolerance must be a number");
+
60 }
+
61 }
+
62
+
63 if (config.contains("max_iterations")) {
+
64 if (config["max_iterations"].is_number_integer()) {
+
65 set_max_iterations(config["max_iterations"]);
+
66 } else {
+
67 throw std::invalid_argument("Max iterations must be an integer");
+
68 }
+
69 }
+
70
+
71 if (config.contains("probability")) {
+
72 if (config["probability"].is_boolean()) {
+
73 set_probability(config["probability"]);
+
74 } else {
+
75 throw std::invalid_argument("Probability must be a boolean");
+
76 }
+
77 }
+
78
+
79 // Set kernel-specific parameters
+
80 if (config.contains("gamma")) {
+
81 if (config["gamma"].is_number()) {
+
82 set_gamma(config["gamma"]);
+
83 } else if (config["gamma"].is_string() && config["gamma"] == "auto") {
+
84 set_gamma(-1.0); // Auto gamma
+
85 } else {
+
86 throw std::invalid_argument("Gamma must be a number or 'auto'");
+
87 }
+
88 }
+
89
+
90 if (config.contains("degree")) {
+
91 if (config["degree"].is_number_integer()) {
+
92 set_degree(config["degree"]);
+
93 } else {
+
94 throw std::invalid_argument("Degree must be an integer");
+
95 }
+
96 }
+
97
+
98 if (config.contains("coef0")) {
+
99 if (config["coef0"].is_number()) {
+
100 set_coef0(config["coef0"]);
+
101 } else {
+
102 throw std::invalid_argument("Coef0 must be a number");
+
103 }
+
104 }
+
105
+
106 if (config.contains("cache_size")) {
+
107 if (config["cache_size"].is_number()) {
+
108 set_cache_size(config["cache_size"]);
+
109 } else {
+
110 throw std::invalid_argument("Cache size must be a number");
+
111 }
+
112 }
+
113
+
114 // Validate all parameters
+
115 validate();
+
116 }
+
117
+
118 nlohmann::json KernelParameters::get_parameters() const
+
119 {
+
120 nlohmann::json params = {
+
121 {"kernel", kernel_type_to_string(kernel_type_)},
+
122 {"multiclass_strategy", multiclass_strategy_to_string(multiclass_strategy_)},
+
123 {"C", C_},
+
124 {"tolerance", tolerance_},
+
125 {"max_iterations", max_iterations_},
+
126 {"probability", probability_},
+
127 {"cache_size", cache_size_}
+
128 };
+
129
+
130 // Add kernel-specific parameters
+
131 switch (kernel_type_) {
+
132 case KernelType::LINEAR:
+
133 // No additional parameters for linear kernel
+
134 break;
+
135
+
136 case KernelType::RBF:
+
137 params["gamma"] = is_gamma_auto() ? "auto" : gamma_;
+
138 break;
+
139
+
140 case KernelType::POLYNOMIAL:
+
141 params["degree"] = degree_;
+
142 params["gamma"] = is_gamma_auto() ? "auto" : gamma_;
+
143 params["coef0"] = coef0_;
+
144 break;
+
145
+
146 case KernelType::SIGMOID:
+
147 params["gamma"] = is_gamma_auto() ? "auto" : gamma_;
+
148 params["coef0"] = coef0_;
+
149 break;
+
150 }
+
151
+
152 return params;
+
153 }
+
154
+
155 void KernelParameters::set_kernel_type(KernelType kernel)
+
156 {
+
157 kernel_type_ = kernel;
+
158
+
159 // Reset kernel-specific parameters to defaults when kernel changes
+
160 auto defaults = get_default_parameters(kernel);
+
161
+
162 if (defaults.contains("gamma")) {
+
163 gamma_ = defaults["gamma"];
+
164 }
+
165 if (defaults.contains("degree")) {
+
166 degree_ = defaults["degree"];
+
167 }
+
168 if (defaults.contains("coef0")) {
+
169 coef0_ = defaults["coef0"];
+
170 }
+
171 }
+
172
+
173 void KernelParameters::set_C(double c)
+
174 {
+
175 if (c <= 0.0) {
+
176 throw std::invalid_argument("C must be positive (C > 0)");
+
177 }
+
178 C_ = c;
+
179 }
+
180
+
181 void KernelParameters::set_gamma(double gamma)
+
182 {
+
183 // Allow negative values for auto gamma (-1.0)
+
184 if (gamma > 0.0 || gamma == -1.0) {
+
185 gamma_ = gamma;
+
186 } else {
+
187 throw std::invalid_argument("Gamma must be positive or -1 for auto");
+
188 }
+
189 }
+
190
+
191 void KernelParameters::set_degree(int degree)
+
192 {
+
193 if (degree < 1) {
+
194 throw std::invalid_argument("Degree must be >= 1");
+
195 }
+
196 degree_ = degree;
+
197 }
+
198
+
199 void KernelParameters::set_coef0(double coef0)
+
200 {
+
201 coef0_ = coef0;
+
202 }
+
203
+
204 void KernelParameters::set_tolerance(double tol)
+
205 {
+
206 if (tol <= 0.0) {
+
207 throw std::invalid_argument("Tolerance must be positive (tolerance > 0)");
+
208 }
+
209 tolerance_ = tol;
+
210 }
+
211
+
212 void KernelParameters::set_max_iterations(int max_iter)
+
213 {
+
214 if (max_iter <= 0 && max_iter != -1) {
+
215 throw std::invalid_argument("Max iterations must be positive or -1 for no limit");
+
216 }
+
217 max_iterations_ = max_iter;
+
218 }
+
219
+
220 void KernelParameters::set_cache_size(double cache_size)
+
221 {
+
222 if (cache_size < 0.0) {
+
223 throw std::invalid_argument("Cache size must be non-negative");
+
224 }
+
225 cache_size_ = cache_size;
+
226 }
+
227
+
228 void KernelParameters::set_probability(bool probability)
+
229 {
+
230 probability_ = probability;
+
231 }
+
232
+
233 void KernelParameters::set_multiclass_strategy(MulticlassStrategy strategy)
+
234 {
+
235 multiclass_strategy_ = strategy;
+
236 }
+
237
+
238 void KernelParameters::validate() const
+
239 {
+
240 // Validate common parameters
+
241 if (C_ <= 0.0) {
+
242 throw std::invalid_argument("C must be positive");
+
243 }
+
244
+
245 if (tolerance_ <= 0.0) {
+
246 throw std::invalid_argument("Tolerance must be positive");
+
247 }
+
248
+
249 if (max_iterations_ <= 0 && max_iterations_ != -1) {
+
250 throw std::invalid_argument("Max iterations must be positive or -1");
+
251 }
+
252
+
253 if (cache_size_ < 0.0) {
+
254 throw std::invalid_argument("Cache size must be non-negative");
+
255 }
+
256
+
257 // Validate kernel-specific parameters
+
258 validate_kernel_parameters();
+
259 }
+
260
+
261 void KernelParameters::validate_kernel_parameters() const
+
262 {
+
263 switch (kernel_type_) {
+
264 case KernelType::LINEAR:
+
265 // Linear kernel has no additional parameters to validate
+
266 break;
+
267
+
268 case KernelType::RBF:
+
269 if (gamma_ > 0.0 || gamma_ == -1.0) {
+
270 // Valid gamma (positive or auto)
+
271 } else {
+
272 throw std::invalid_argument("RBF kernel gamma must be positive or auto (-1)");
+
273 }
+
274 break;
+
275
+
276 case KernelType::POLYNOMIAL:
+
277 if (degree_ < 1) {
+
278 throw std::invalid_argument("Polynomial degree must be >= 1");
+
279 }
+
280 if (gamma_ > 0.0 || gamma_ == -1.0) {
+
281 // Valid gamma
+
282 } else {
+
283 throw std::invalid_argument("Polynomial kernel gamma must be positive or auto (-1)");
+
284 }
+
285 // coef0 can be any real number
+
286 break;
+
287
+
288 case KernelType::SIGMOID:
+
289 if (gamma_ > 0.0 || gamma_ == -1.0) {
+
290 // Valid gamma
+
291 } else {
+
292 throw std::invalid_argument("Sigmoid kernel gamma must be positive or auto (-1)");
+
293 }
+
294 // coef0 can be any real number
+
295 break;
+
296 }
+
297 }
+
298
+
299 nlohmann::json KernelParameters::get_default_parameters(KernelType kernel)
+
300 {
+
301 nlohmann::json defaults = {
+
302 {"C", 1.0},
+
303 {"tolerance", 1e-3},
+
304 {"max_iterations", -1},
+
305 {"probability", false},
+
306 {"multiclass_strategy", "ovr"},
+
307 {"cache_size", 200.0}
+
308 };
+
309
+
310 switch (kernel) {
+
311 case KernelType::LINEAR:
+
312 defaults["kernel"] = "linear";
+
313 break;
+
314
+
315 case KernelType::RBF:
+
316 defaults["kernel"] = "rbf";
+
317 defaults["gamma"] = -1.0; // Auto gamma
+
318 break;
+
319
+
320 case KernelType::POLYNOMIAL:
+
321 defaults["kernel"] = "polynomial";
+
322 defaults["degree"] = 3;
+
323 defaults["gamma"] = -1.0; // Auto gamma
+
324 defaults["coef0"] = 0.0;
+
325 break;
+
326
+
327 case KernelType::SIGMOID:
+
328 defaults["kernel"] = "sigmoid";
+
329 defaults["gamma"] = -1.0; // Auto gamma
+
330 defaults["coef0"] = 0.0;
+
331 break;
+
332 }
+
333
+
334 return defaults;
+
335 }
+
336
+
337 void KernelParameters::reset_to_defaults()
+
338 {
+
339 auto defaults = get_default_parameters(kernel_type_);
+
340 set_parameters(defaults);
+
341 }
+
342
+
343 void KernelParameters::set_gamma_auto()
+
344 {
+
345 gamma_ = -1.0;
+
346 }
+
347
+
348} // namespace svm_classifier
+
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
+ + diff --git a/kernel__parameters_8hpp_source.html b/kernel__parameters_8hpp_source.html new file mode 100644 index 0000000..018bdf4 --- /dev/null +++ b/kernel__parameters_8hpp_source.html @@ -0,0 +1,190 @@ + + + + + + + +SVM Classifier C++: include/svm_classifier/kernel_parameters.hpp Source File + + + + + + + + + +
+
+ + + + + + +
+
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+
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+
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+
kernel_parameters.hpp
+
+
+
1#pragma once
+
2
+
3#include "types.hpp"
+
4#include <torch/torch.h>
+
5#include <vector>
+
6#include <memory>
+
7
+
8// Forward declarations for libsvm and liblinear structures
+
9struct svm_node;
+
10struct svm_problem;
+
11struct feature_node;
+
12struct problem;
+
13
+
14namespace svm_classifier {
+
15
+
23 class DataConverter {
+
24 public:
+
28 DataConverter();
+
29
+
33 ~DataConverter();
+
34
+
41 std::unique_ptr<svm_problem> to_svm_problem(const torch::Tensor& X,
+
42 const torch::Tensor& y = torch::Tensor());
+
43
+
50 std::unique_ptr<problem> to_linear_problem(const torch::Tensor& X,
+
51 const torch::Tensor& y = torch::Tensor());
+
52
+
58 svm_node* to_svm_node(const torch::Tensor& sample);
+
59
+
65 feature_node* to_feature_node(const torch::Tensor& sample);
+
66
+
72 torch::Tensor from_predictions(const std::vector<double>& predictions);
+
73
+
79 torch::Tensor from_probabilities(const std::vector<std::vector<double>>& probabilities);
+
80
+
86 torch::Tensor from_decision_values(const std::vector<std::vector<double>>& decision_values);
+
87
+
94 void validate_tensors(const torch::Tensor& X, const torch::Tensor& y = torch::Tensor());
+
95
+
100 int get_n_features() const { return n_features_; }
+
101
+
106 int get_n_samples() const { return n_samples_; }
+
107
+
111 void cleanup();
+
112
+
117 void set_sparse_threshold(double threshold) { sparse_threshold_ = threshold; }
+
118
+
123 double get_sparse_threshold() const { return sparse_threshold_; }
+
124
+
125 private:
+
126 int n_features_;
+
127 int n_samples_;
+
128 double sparse_threshold_;
+
129
+
130 // Memory management for libsvm structures
+
131 std::vector<std::vector<svm_node>> svm_nodes_storage_;
+
132 std::vector<svm_node*> svm_x_space_;
+
133 std::vector<double> svm_y_space_;
+
134
+
135 // Memory management for liblinear structures
+
136 std::vector<std::vector<feature_node>> linear_nodes_storage_;
+
137 std::vector<feature_node*> linear_x_space_;
+
138 std::vector<double> linear_y_space_;
+
139
+
140 // Single sample storage (for prediction)
+
141 std::vector<svm_node> single_svm_nodes_;
+
142 std::vector<feature_node> single_linear_nodes_;
+
143
+
149 std::vector<std::vector<svm_node>> tensor_to_svm_nodes(const torch::Tensor& X);
+
150
+
156 std::vector<std::vector<feature_node>> tensor_to_linear_nodes(const torch::Tensor& X);
+
157
+
163 std::vector<svm_node> sample_to_svm_nodes(const torch::Tensor& sample);
+
164
+
170 std::vector<feature_node> sample_to_linear_nodes(const torch::Tensor& sample);
+
171
+
177 std::vector<double> extract_labels(const torch::Tensor& y);
+
178
+
184 torch::Tensor ensure_cpu_tensor(const torch::Tensor& tensor);
+
185
+
192 void validate_tensor_properties(const torch::Tensor& tensor, int expected_dims, const std::string& name);
+
193 };
+
194
+
195} // namespace svm_classifier
+
svm_classifier::DataConverter::get_n_samples
int get_n_samples() const
Get number of samples from last conversion.
Definition kernel_parameters.hpp:106
+
svm_classifier::DataConverter::to_svm_node
svm_node * to_svm_node(const torch::Tensor &sample)
Convert single sample to libsvm format.
+
svm_classifier::DataConverter::get_sparse_threshold
double get_sparse_threshold() const
Get sparse threshold.
Definition kernel_parameters.hpp:123
+
svm_classifier::DataConverter::get_n_features
int get_n_features() const
Get number of features from last conversion.
Definition kernel_parameters.hpp:100
+
svm_classifier::DataConverter::cleanup
void cleanup()
Clean up all allocated memory.
+
svm_classifier::DataConverter::from_decision_values
torch::Tensor from_decision_values(const std::vector< std::vector< double > > &decision_values)
Convert decision values back to PyTorch tensor.
+
svm_classifier::DataConverter::from_probabilities
torch::Tensor from_probabilities(const std::vector< std::vector< double > > &probabilities)
Convert probabilities back to PyTorch tensor.
+
svm_classifier::DataConverter::DataConverter
DataConverter()
Default constructor.
+
svm_classifier::DataConverter::to_svm_problem
std::unique_ptr< svm_problem > to_svm_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Convert PyTorch tensors to libsvm format.
+
svm_classifier::DataConverter::to_feature_node
feature_node * to_feature_node(const torch::Tensor &sample)
Convert single sample to liblinear format.
+
svm_classifier::DataConverter::to_linear_problem
std::unique_ptr< problem > to_linear_problem(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Convert PyTorch tensors to liblinear format.
+
svm_classifier::DataConverter::set_sparse_threshold
void set_sparse_threshold(double threshold)
Set sparse threshold (features with absolute value below this are ignored)
Definition kernel_parameters.hpp:117
+
svm_classifier::DataConverter::validate_tensors
void validate_tensors(const torch::Tensor &X, const torch::Tensor &y=torch::Tensor())
Validate input tensors.
+
svm_classifier::DataConverter::from_predictions
torch::Tensor from_predictions(const std::vector< double > &predictions)
Convert predictions back to PyTorch tensor.
+
svm_classifier::DataConverter::~DataConverter
~DataConverter()
Destructor - cleans up allocated memory.
+
+ +
+Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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    +
    + + + + + + +
    +
    SVM Classifier C++ 1.0.0 +
    +
    High-performance Support Vector Machine classifier with scikit-learn compatible API
    +
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    +
    +
    multiclass_strategy.cpp
    +
    +
    +
    1#include "svm_classifier/multiclass_strategy.hpp"
    +
    2#include "svm.h" // libsvm
    +
    3#include "linear.h" // liblinear
    +
    4#include <algorithm>
    +
    5#include <unordered_map>
    +
    6#include <unordered_set>
    +
    7#include <chrono>
    +
    8#include <cmath>
    +
    9
    +
    10namespace svm_classifier {
    +
    11
    +
    12 // OneVsRestStrategy Implementation
    +
    13 OneVsRestStrategy::OneVsRestStrategy()
    +
    14 : library_type_(SVMLibrary::LIBLINEAR)
    +
    15 {
    +
    16 }
    +
    17
    +
    18 OneVsRestStrategy::~OneVsRestStrategy()
    +
    19 {
    +
    20 cleanup_models();
    +
    21 }
    +
    22
    +
    23 TrainingMetrics OneVsRestStrategy::fit(const torch::Tensor& X,
    +
    24 const torch::Tensor& y,
    +
    25 const KernelParameters& params,
    +
    26 DataConverter& converter)
    +
    27 {
    +
    28 cleanup_models();
    +
    29
    +
    30 auto start_time = std::chrono::high_resolution_clock::now();
    +
    31
    +
    32 // Store parameters and determine library type
    +
    33 params_ = params;
    +
    34 library_type_ = get_svm_library(params.get_kernel_type());
    +
    35
    +
    36 // Extract unique classes
    +
    37 auto y_cpu = y.to(torch::kCPU);
    +
    38 auto unique_classes_tensor = torch::unique(y_cpu);
    +
    39 classes_.clear();
    +
    40
    +
    41 for (int i = 0; i < unique_classes_tensor.size(0); ++i) {
    +
    42 classes_.push_back(unique_classes_tensor[i].item<int>());
    +
    43 }
    +
    44
    +
    45 std::sort(classes_.begin(), classes_.end());
    +
    46
    +
    47 // Handle binary classification case
    +
    48 if (classes_.size() <= 2) {
    +
    49 // For binary classification, train a single classifier
    +
    50 classes_.resize(2); // Ensure we have exactly 2 classes
    +
    51
    +
    52 auto binary_y = y;
    +
    53 if (classes_.size() == 1) {
    +
    54 // Edge case: only one class, create dummy binary problem
    +
    55 classes_.push_back(classes_[0] + 1);
    +
    56 binary_y = torch::cat({ y, torch::full({1}, classes_[1], y.options()) });
    +
    57 auto dummy_x = torch::zeros({ 1, X.size(1) }, X.options());
    +
    58 auto extended_X = torch::cat({ X, dummy_x });
    +
    59
    +
    60 double training_time = train_binary_classifier(extended_X, binary_y, params, converter, 0);
    +
    61 } else {
    +
    62 double training_time = train_binary_classifier(X, binary_y, params, converter, 0);
    +
    63 }
    +
    64 } else {
    +
    65 // Multiclass case: train one classifier per class
    +
    66 if (library_type_ == SVMLibrary::LIBSVM) {
    +
    67 svm_models_.resize(classes_.size());
    +
    68 } else {
    +
    69 linear_models_.resize(classes_.size());
    +
    70 }
    +
    71
    +
    72 double total_training_time = 0.0;
    +
    73
    +
    74 for (size_t i = 0; i < classes_.size(); ++i) {
    +
    75 auto binary_y = create_binary_labels(y, classes_[i]);
    +
    76 total_training_time += train_binary_classifier(X, binary_y, params, converter, i);
    +
    77 }
    +
    78 }
    +
    79
    +
    80 auto end_time = std::chrono::high_resolution_clock::now();
    +
    81 auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
    +
    82
    +
    83 is_trained_ = true;
    +
    84
    +
    85 TrainingMetrics metrics;
    +
    86 metrics.training_time = duration.count() / 1000.0;
    +
    87 metrics.status = TrainingStatus::SUCCESS;
    +
    88
    +
    89 return metrics;
    +
    90 }
    +
    91
    +
    92 std::vector<int> OneVsRestStrategy::predict(const torch::Tensor& X, DataConverter& converter)
    +
    93 {
    +
    94 if (!is_trained_) {
    +
    95 throw std::runtime_error("Model is not trained");
    +
    96 }
    +
    97
    +
    98 auto decision_values = decision_function(X, converter);
    +
    99 std::vector<int> predictions;
    +
    100 predictions.reserve(X.size(0));
    +
    101
    +
    102 for (const auto& decision_row : decision_values) {
    +
    103 // Find the class with maximum decision value
    +
    104 auto max_it = std::max_element(decision_row.begin(), decision_row.end());
    +
    105 int predicted_class_idx = std::distance(decision_row.begin(), max_it);
    +
    106 predictions.push_back(classes_[predicted_class_idx]);
    +
    107 }
    +
    108
    +
    109 return predictions;
    +
    110 }
    +
    111
    +
    112 std::vector<std::vector<double>> OneVsRestStrategy::predict_proba(const torch::Tensor& X,
    +
    113 DataConverter& converter)
    +
    114 {
    +
    115 if (!supports_probability()) {
    +
    116 throw std::runtime_error("Probability prediction not supported for current configuration");
    +
    117 }
    +
    118
    +
    119 if (!is_trained_) {
    +
    120 throw std::runtime_error("Model is not trained");
    +
    121 }
    +
    122
    +
    123 std::vector<std::vector<double>> probabilities;
    +
    124 probabilities.reserve(X.size(0));
    +
    125
    +
    126 for (int i = 0; i < X.size(0); ++i) {
    +
    127 auto sample = X[i];
    +
    128 std::vector<double> sample_probs;
    +
    129 sample_probs.reserve(classes_.size());
    +
    130
    +
    131 if (library_type_ == SVMLibrary::LIBSVM) {
    +
    132 for (size_t j = 0; j < classes_.size(); ++j) {
    +
    133 if (svm_models_[j]) {
    +
    134 auto sample_node = converter.to_svm_node(sample);
    +
    135 double prob_estimates[2];
    +
    136 svm_predict_probability(svm_models_[j].get(), sample_node, prob_estimates);
    +
    137 sample_probs.push_back(prob_estimates[0]); // Probability of positive class
    +
    138 } else {
    +
    139 sample_probs.push_back(0.0);
    +
    140 }
    +
    141 }
    +
    142 } else {
    +
    143 for (size_t j = 0; j < classes_.size(); ++j) {
    +
    144 if (linear_models_[j]) {
    +
    145 auto sample_node = converter.to_feature_node(sample);
    +
    146 double prob_estimates[2];
    +
    147 predict_probability(linear_models_[j].get(), sample_node, prob_estimates);
    +
    148 sample_probs.push_back(prob_estimates[0]); // Probability of positive class
    +
    149 } else {
    +
    150 sample_probs.push_back(0.0);
    +
    151 }
    +
    152 }
    +
    153 }
    +
    154
    +
    155 // Normalize probabilities
    +
    156 double sum = std::accumulate(sample_probs.begin(), sample_probs.end(), 0.0);
    +
    157 if (sum > 0.0) {
    +
    158 for (auto& prob : sample_probs) {
    +
    159 prob /= sum;
    +
    160 }
    +
    161 } else {
    +
    162 // Uniform distribution if all probabilities are zero
    +
    163 std::fill(sample_probs.begin(), sample_probs.end(), 1.0 / classes_.size());
    +
    164 }
    +
    165
    +
    166 probabilities.push_back(sample_probs);
    +
    167 }
    +
    168
    +
    169 return probabilities;
    +
    170 }
    +
    171
    +
    172 std::vector<std::vector<double>> OneVsRestStrategy::decision_function(const torch::Tensor& X,
    +
    173 DataConverter& converter)
    +
    174 {
    +
    175 if (!is_trained_) {
    +
    176 throw std::runtime_error("Model is not trained");
    +
    177 }
    +
    178
    +
    179 std::vector<std::vector<double>> decision_values;
    +
    180 decision_values.reserve(X.size(0));
    +
    181
    +
    182 for (int i = 0; i < X.size(0); ++i) {
    +
    183 auto sample = X[i];
    +
    184 std::vector<double> sample_decisions;
    +
    185 sample_decisions.reserve(classes_.size());
    +
    186
    +
    187 if (library_type_ == SVMLibrary::LIBSVM) {
    +
    188 for (size_t j = 0; j < classes_.size(); ++j) {
    +
    189 if (svm_models_[j]) {
    +
    190 auto sample_node = converter.to_svm_node(sample);
    +
    191 double decision_value;
    +
    192 svm_predict_values(svm_models_[j].get(), sample_node, &decision_value);
    +
    193 sample_decisions.push_back(decision_value);
    +
    194 } else {
    +
    195 sample_decisions.push_back(0.0);
    +
    196 }
    +
    197 }
    +
    198 } else {
    +
    199 for (size_t j = 0; j < classes_.size(); ++j) {
    +
    200 if (linear_models_[j]) {
    +
    201 auto sample_node = converter.to_feature_node(sample);
    +
    202 double decision_value;
    +
    203 predict_values(linear_models_[j].get(), sample_node, &decision_value);
    +
    204 sample_decisions.push_back(decision_value);
    +
    205 } else {
    +
    206 sample_decisions.push_back(0.0);
    +
    207 }
    +
    208 }
    +
    209 }
    +
    210
    +
    211 decision_values.push_back(sample_decisions);
    +
    212 }
    +
    213
    +
    214 return decision_values;
    +
    215 }
    +
    216
    +
    217 bool OneVsRestStrategy::supports_probability() const
    +
    218 {
    +
    219 if (!is_trained_) {
    +
    220 return params_.get_probability();
    +
    221 }
    +
    222
    +
    223 // Check if any model supports probability
    +
    224 if (library_type_ == SVMLibrary::LIBSVM) {
    +
    225 for (const auto& model : svm_models_) {
    +
    226 if (model && svm_check_probability_model(model.get())) {
    +
    227 return true;
    +
    228 }
    +
    229 }
    +
    230 } else {
    +
    231 for (const auto& model : linear_models_) {
    +
    232 if (model && check_probability_model(model.get())) {
    +
    233 return true;
    +
    234 }
    +
    235 }
    +
    236 }
    +
    237
    +
    238 return false;
    +
    239 }
    +
    240
    +
    241 torch::Tensor OneVsRestStrategy::create_binary_labels(const torch::Tensor& y, int positive_class)
    +
    242 {
    +
    243 auto binary_labels = torch::ones_like(y) * (-1); // Initialize with -1 (negative class)
    +
    244 auto positive_mask = (y == positive_class);
    +
    245 binary_labels.masked_fill_(positive_mask, 1); // Set positive class to +1
    +
    246
    +
    247 return binary_labels;
    +
    248 }
    +
    249
    +
    250 double OneVsRestStrategy::train_binary_classifier(const torch::Tensor& X,
    +
    251 const torch::Tensor& y_binary,
    +
    252 const KernelParameters& params,
    +
    253 DataConverter& converter,
    +
    254 int class_idx)
    +
    255 {
    +
    256 auto start_time = std::chrono::high_resolution_clock::now();
    +
    257
    +
    258 if (library_type_ == SVMLibrary::LIBSVM) {
    +
    259 // Use libsvm
    +
    260 auto problem = converter.to_svm_problem(X, y_binary);
    +
    261
    +
    262 // Setup SVM parameters
    +
    263 svm_parameter svm_params;
    +
    264 svm_params.svm_type = C_SVC;
    +
    265
    +
    266 switch (params.get_kernel_type()) {
    +
    267 case KernelType::RBF:
    +
    268 svm_params.kernel_type = RBF;
    +
    269 break;
    +
    270 case KernelType::POLYNOMIAL:
    +
    271 svm_params.kernel_type = POLY;
    +
    272 break;
    +
    273 case KernelType::SIGMOID:
    +
    274 svm_params.kernel_type = SIGMOID;
    +
    275 break;
    +
    276 default:
    +
    277 throw std::runtime_error("Invalid kernel type for libsvm");
    +
    278 }
    +
    279
    +
    280 svm_params.degree = params.get_degree();
    +
    281 svm_params.gamma = (params.get_gamma() == -1.0) ? 1.0 / X.size(1) : params.get_gamma();
    +
    282 svm_params.coef0 = params.get_coef0();
    +
    283 svm_params.cache_size = params.get_cache_size();
    +
    284 svm_params.eps = params.get_tolerance();
    +
    285 svm_params.C = params.get_C();
    +
    286 svm_params.nr_weight = 0;
    +
    287 svm_params.weight_label = nullptr;
    +
    288 svm_params.weight = nullptr;
    +
    289 svm_params.nu = 0.5;
    +
    290 svm_params.p = 0.1;
    +
    291 svm_params.shrinking = 1;
    +
    292 svm_params.probability = params.get_probability() ? 1 : 0;
    +
    293
    +
    294 // Check parameters
    +
    295 const char* error_msg = svm_check_parameter(problem.get(), &svm_params);
    +
    296 if (error_msg) {
    +
    297 throw std::runtime_error("SVM parameter error: " + std::string(error_msg));
    +
    298 }
    +
    299
    +
    300 // Train model
    +
    301 auto model = svm_train(problem.get(), &svm_params);
    +
    302 if (!model) {
    +
    303 throw std::runtime_error("Failed to train SVM model");
    +
    304 }
    +
    305
    +
    306 svm_models_[class_idx] = std::unique_ptr<svm_model>(model);
    +
    307
    +
    308 } else {
    +
    309 // Use liblinear
    +
    310 auto problem = converter.to_linear_problem(X, y_binary);
    +
    311
    +
    312 // Setup linear parameters
    +
    313 parameter linear_params;
    +
    314 linear_params.solver_type = L2R_L2LOSS_SVC_DUAL; // Default solver for C-SVC
    +
    315 linear_params.C = params.get_C();
    +
    316 linear_params.eps = params.get_tolerance();
    +
    317 linear_params.nr_weight = 0;
    +
    318 linear_params.weight_label = nullptr;
    +
    319 linear_params.weight = nullptr;
    +
    320 linear_params.p = 0.1;
    +
    321 linear_params.nu = 0.5;
    +
    322 linear_params.init_sol = nullptr;
    +
    323 linear_params.regularize_bias = 0;
    +
    324
    +
    325 // Check parameters
    +
    326 const char* error_msg = check_parameter(problem.get(), &linear_params);
    +
    327 if (error_msg) {
    +
    328 throw std::runtime_error("Linear parameter error: " + std::string(error_msg));
    +
    329 }
    +
    330
    +
    331 // Train model
    +
    332 auto model = train(problem.get(), &linear_params);
    +
    333 if (!model) {
    +
    334 throw std::runtime_error("Failed to train linear model");
    +
    335 }
    +
    336
    +
    337 linear_models_[class_idx] = std::unique_ptr<::model>(model);
    +
    338 }
    +
    339
    +
    340 auto end_time = std::chrono::high_resolution_clock::now();
    +
    341 auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
    +
    342
    +
    343 return duration.count() / 1000.0;
    +
    344 }
    +
    345
    +
    346 void OneVsRestStrategy::cleanup_models()
    +
    347 {
    +
    348 for (auto& model : svm_models_) {
    +
    349 if (model) {
    +
    350 svm_free_and_destroy_model(&model);
    +
    351 }
    +
    352 }
    +
    353 svm_models_.clear();
    +
    354
    +
    355 for (auto& model : linear_models_) {
    +
    356 if (model) {
    +
    357 free_and_destroy_model(&model);
    +
    358 }
    +
    359 }
    +
    360 linear_models_.clear();
    +
    361
    +
    362 is_trained_ = false;
    +
    363 }
    +
    364
    +
    365 // OneVsOneStrategy Implementation
    +
    366 OneVsOneStrategy::OneVsOneStrategy()
    +
    367 : library_type_(SVMLibrary::LIBLINEAR)
    +
    368 {
    +
    369 }
    +
    370
    +
    371 OneVsOneStrategy::~OneVsOneStrategy()
    +
    372 {
    +
    373 cleanup_models();
    +
    374 }
    +
    375
    +
    376 TrainingMetrics OneVsOneStrategy::fit(const torch::Tensor& X,
    +
    377 const torch::Tensor& y,
    +
    378 const KernelParameters& params,
    +
    379 DataConverter& converter)
    +
    380 {
    +
    381 cleanup_models();
    +
    382
    +
    383 auto start_time = std::chrono::high_resolution_clock::now();
    +
    384
    +
    385 // Store parameters and determine library type
    +
    386 params_ = params;
    +
    387 library_type_ = get_svm_library(params.get_kernel_type());
    +
    388
    +
    389 // Extract unique classes
    +
    390 auto y_cpu = y.to(torch::kCPU);
    +
    391 auto unique_classes_tensor = torch::unique(y_cpu);
    +
    392 classes_.clear();
    +
    393
    +
    394 for (int i = 0; i < unique_classes_tensor.size(0); ++i) {
    +
    395 classes_.push_back(unique_classes_tensor[i].item<int>());
    +
    396 }
    +
    397
    +
    398 std::sort(classes_.begin(), classes_.end());
    +
    399
    +
    400 // Generate all class pairs
    +
    401 class_pairs_.clear();
    +
    402 for (size_t i = 0; i < classes_.size(); ++i) {
    +
    403 for (size_t j = i + 1; j < classes_.size(); ++j) {
    +
    404 class_pairs_.emplace_back(classes_[i], classes_[j]);
    +
    405 }
    +
    406 }
    +
    407
    +
    408 // Initialize model storage
    +
    409 if (library_type_ == SVMLibrary::LIBSVM) {
    +
    410 svm_models_.resize(class_pairs_.size());
    +
    411 } else {
    +
    412 linear_models_.resize(class_pairs_.size());
    +
    413 }
    +
    414
    +
    415 double total_training_time = 0.0;
    +
    416
    +
    417 // Train one classifier for each class pair
    +
    418 for (size_t i = 0; i < class_pairs_.size(); ++i) {
    +
    419 auto [class1, class2] = class_pairs_[i];
    +
    420 total_training_time += train_pairwise_classifier(X, y, class1, class2, params, converter, i);
    +
    421 }
    +
    422
    +
    423 auto end_time = std::chrono::high_resolution_clock::now();
    +
    424 auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
    +
    425
    +
    426 is_trained_ = true;
    +
    427
    +
    428 TrainingMetrics metrics;
    +
    429 metrics.training_time = duration.count() / 1000.0;
    +
    430 metrics.status = TrainingStatus::SUCCESS;
    +
    431
    +
    432 return metrics;
    +
    433 }
    +
    434
    +
    435 std::vector<int> OneVsOneStrategy::predict(const torch::Tensor& X, DataConverter& converter)
    +
    436 {
    +
    437 if (!is_trained_) {
    +
    438 throw std::runtime_error("Model is not trained");
    +
    439 }
    +
    440
    +
    441 auto decision_values = decision_function(X, converter);
    +
    442 return vote_predictions(decision_values);
    +
    443 }
    +
    444
    +
    445 std::vector<std::vector<double>> OneVsOneStrategy::predict_proba(const torch::Tensor& X,
    +
    446 DataConverter& converter)
    +
    447 {
    +
    448 // OvO probability estimation is more complex and typically done via
    +
    449 // pairwise coupling (Hastie & Tibshirani, 1998)
    +
    450 // For simplicity, we'll use decision function values and normalize
    +
    451
    +
    452 auto decision_values = decision_function(X, converter);
    +
    453 std::vector<std::vector<double>> probabilities;
    +
    454 probabilities.reserve(X.size(0));
    +
    455
    +
    456 for (const auto& decision_row : decision_values) {
    +
    457 std::vector<double> class_scores(classes_.size(), 0.0);
    +
    458
    +
    459 // Aggregate decision values for each class
    +
    460 for (size_t i = 0; i < class_pairs_.size(); ++i) {
    +
    461 auto [class1, class2] = class_pairs_[i];
    +
    462 double decision = decision_row[i];
    +
    463
    +
    464 auto it1 = std::find(classes_.begin(), classes_.end(), class1);
    +
    465 auto it2 = std::find(classes_.begin(), classes_.end(), class2);
    +
    466
    +
    467 if (it1 != classes_.end() && it2 != classes_.end()) {
    +
    468 size_t idx1 = std::distance(classes_.begin(), it1);
    +
    469 size_t idx2 = std::distance(classes_.begin(), it2);
    +
    470
    +
    471 if (decision > 0) {
    +
    472 class_scores[idx1] += 1.0;
    +
    473 } else {
    +
    474 class_scores[idx2] += 1.0;
    +
    475 }
    +
    476 }
    +
    477 }
    +
    478
    +
    479 // Convert scores to probabilities
    +
    480 double sum = std::accumulate(class_scores.begin(), class_scores.end(), 0.0);
    +
    481 if (sum > 0.0) {
    +
    482 for (auto& score : class_scores) {
    +
    483 score /= sum;
    +
    484 }
    +
    485 } else {
    +
    486 std::fill(class_scores.begin(), class_scores.end(), 1.0 / classes_.size());
    +
    487 }
    +
    488
    +
    489 probabilities.push_back(class_scores);
    +
    490 }
    +
    491
    +
    492 return probabilities;
    +
    493 }
    +
    494
    +
    495 std::vector<std::vector<double>> OneVsOneStrategy::decision_function(const torch::Tensor& X,
    +
    svm_classifier::OneVsRestStrategy::OneVsRestStrategy
    OneVsRestStrategy()
    Constructor.
    +
    + +
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    +
    multiclass_strategy.hpp
    +
    +
    +
    1#pragma once
    +
    2
    +
    3#include "types.hpp"
    +
    4#include "kernel_parameters.hpp"
    +
    5#include "data_converter.hpp"
    +
    6#include <torch/torch.h>
    +
    7#include <vector>
    +
    8#include <memory>
    +
    9#include <unordered_map>
    +
    10
    +
    11// Forward declarations
    +
    12struct svm_model;
    +
    13struct model;
    +
    14
    +
    15namespace svm_classifier {
    +
    16
    +
    +
    20 class MulticlassStrategyBase {
    +
    21 public:
    +
    25 virtual ~MulticlassStrategyBase() = default;
    +
    26
    +
    35 virtual TrainingMetrics fit(const torch::Tensor& X,
    +
    36 const torch::Tensor& y,
    +
    37 const KernelParameters& params,
    +
    38 DataConverter& converter) = 0;
    +
    39
    +
    46 virtual std::vector<int> predict(const torch::Tensor& X,
    +
    47 DataConverter& converter) = 0;
    +
    48
    +
    55 virtual std::vector<std::vector<double>> predict_proba(const torch::Tensor& X,
    +
    56 DataConverter& converter) = 0;
    +
    57
    +
    64 virtual std::vector<std::vector<double>> decision_function(const torch::Tensor& X,
    +
    65 DataConverter& converter) = 0;
    +
    66
    +
    71 virtual std::vector<int> get_classes() const = 0;
    +
    72
    +
    77 virtual bool supports_probability() const = 0;
    +
    78
    +
    83 virtual int get_n_classes() const = 0;
    +
    84
    +
    89 virtual MulticlassStrategy get_strategy_type() const = 0;
    +
    90
    +
    91 protected:
    +
    92 std::vector<int> classes_;
    +
    93 bool is_trained_ = false;
    +
    94 };
    +
    +
    95
    +
    +
    99 class OneVsRestStrategy : public MulticlassStrategyBase {
    +
    100 public:
    +
    104 OneVsRestStrategy();
    +
    105
    +
    109 ~OneVsRestStrategy() override;
    +
    110
    +
    111 TrainingMetrics fit(const torch::Tensor& X,
    +
    112 const torch::Tensor& y,
    +
    113 const KernelParameters& params,
    +
    114 DataConverter& converter) override;
    +
    115
    +
    116 std::vector<int> predict(const torch::Tensor& X,
    +
    117 DataConverter& converter) override;
    +
    118
    +
    119 std::vector<std::vector<double>> predict_proba(const torch::Tensor& X,
    +
    120 DataConverter& converter) override;
    +
    121
    +
    122 std::vector<std::vector<double>> decision_function(const torch::Tensor& X,
    +
    123 DataConverter& converter) override;
    +
    124
    +
    125 std::vector<int> get_classes() const override { return classes_; }
    +
    126
    +
    127 bool supports_probability() const override;
    +
    128
    +
    129 int get_n_classes() const override { return static_cast<int>(classes_.size()); }
    +
    130
    +
    131 MulticlassStrategy get_strategy_type() const override { return MulticlassStrategy::ONE_VS_REST; }
    +
    132
    +
    133 private:
    +
    134 std::vector<std::unique_ptr<svm_model>> svm_models_;
    +
    135 std::vector<std::unique_ptr<model>> linear_models_;
    +
    136 KernelParameters params_;
    +
    137 SVMLibrary library_type_;
    +
    138
    +
    145 torch::Tensor create_binary_labels(const torch::Tensor& y, int positive_class);
    +
    146
    +
    156 double train_binary_classifier(const torch::Tensor& X,
    +
    157 const torch::Tensor& y_binary,
    +
    158 const KernelParameters& params,
    +
    159 DataConverter& converter,
    +
    160 int class_idx);
    +
    161
    +
    165 void cleanup_models();
    +
    166 };
    +
    +
    167
    +
    +
    171 class OneVsOneStrategy : public MulticlassStrategyBase {
    +
    172 public:
    +
    176 OneVsOneStrategy();
    +
    177
    +
    181 ~OneVsOneStrategy() override;
    +
    182
    +
    183 TrainingMetrics fit(const torch::Tensor& X,
    +
    184 const torch::Tensor& y,
    +
    185 const KernelParameters& params,
    +
    186 DataConverter& converter) override;
    +
    187
    +
    188 std::vector<int> predict(const torch::Tensor& X,
    +
    189 DataConverter& converter) override;
    +
    190
    +
    191 std::vector<std::vector<double>> predict_proba(const torch::Tensor& X,
    +
    192 DataConverter& converter) override;
    +
    193
    +
    194 std::vector<std::vector<double>> decision_function(const torch::Tensor& X,
    +
    195 DataConverter& converter) override;
    +
    196
    +
    197 std::vector<int> get_classes() const override { return classes_; }
    +
    198
    +
    199 bool supports_probability() const override;
    +
    200
    +
    201 int get_n_classes() const override { return static_cast<int>(classes_.size()); }
    +
    202
    +
    203 MulticlassStrategy get_strategy_type() const override { return MulticlassStrategy::ONE_VS_ONE; }
    +
    204
    +
    205 private:
    +
    206 std::vector<std::unique_ptr<svm_model>> svm_models_;
    +
    207 std::vector<std::unique_ptr<model>> linear_models_;
    +
    208 std::vector<std::pair<int, int>> class_pairs_;
    +
    209 KernelParameters params_;
    +
    210 SVMLibrary library_type_;
    +
    211
    +
    220 std::pair<torch::Tensor, torch::Tensor> extract_binary_data(const torch::Tensor& X,
    +
    221 const torch::Tensor& y,
    +
    222 int class1,
    +
    223 int class2);
    +
    224
    +
    236 double train_pairwise_classifier(const torch::Tensor& X,
    +
    237 const torch::Tensor& y,
    +
    238 int class1,
    +
    239 int class2,
    +
    240 const KernelParameters& params,
    +
    241 DataConverter& converter,
    +
    242 int model_idx);
    +
    243
    +
    249 std::vector<int> vote_predictions(const std::vector<std::vector<double>>& decisions);
    +
    250
    +
    254 void cleanup_models();
    +
    255 };
    +
    +
    256
    +
    262 std::unique_ptr<MulticlassStrategyBase> create_multiclass_strategy(MulticlassStrategy strategy);
    +
    263
    +
    264} // namespace svm_classifier
    +
    svm_classifier::DataConverter
    Data converter between libtorch tensors and SVM library formats.
    Definition data_converter.hpp:23
    +
    svm_classifier::MulticlassStrategyBase
    Abstract base class for multiclass classification strategies.
    Definition multiclass_strategy.hpp:20
    +
    svm_classifier::MulticlassStrategyBase::classes_
    std::vector< int > classes_
    Unique class labels.
    Definition multiclass_strategy.hpp:92
    +
    svm_classifier::MulticlassStrategyBase::get_n_classes
    virtual int get_n_classes() const =0
    Get number of classes.
    +
    svm_classifier::MulticlassStrategyBase::supports_probability
    virtual bool supports_probability() const =0
    Check if the model supports probability prediction.
    +
    svm_classifier::MulticlassStrategyBase::get_strategy_type
    virtual MulticlassStrategy get_strategy_type() const =0
    Get strategy type.
    +
    svm_classifier::MulticlassStrategyBase::get_classes
    virtual std::vector< int > get_classes() const =0
    Get unique class labels.
    +
    svm_classifier::MulticlassStrategyBase::fit
    virtual TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter)=0
    Train the multiclass classifier.
    +
    svm_classifier::MulticlassStrategyBase::predict
    virtual std::vector< int > predict(const torch::Tensor &X, DataConverter &converter)=0
    Predict class labels.
    +
    svm_classifier::MulticlassStrategyBase::~MulticlassStrategyBase
    virtual ~MulticlassStrategyBase()=default
    Virtual destructor.
    +
    svm_classifier::MulticlassStrategyBase::is_trained_
    bool is_trained_
    Whether the model is trained.
    Definition multiclass_strategy.hpp:93
    +
    svm_classifier::MulticlassStrategyBase::predict_proba
    virtual std::vector< std::vector< double > > predict_proba(const torch::Tensor &X, DataConverter &converter)=0
    Predict class probabilities.
    +
    svm_classifier::MulticlassStrategyBase::decision_function
    virtual std::vector< std::vector< double > > decision_function(const torch::Tensor &X, DataConverter &converter)=0
    Get decision function values.
    +
    svm_classifier::OneVsOneStrategy
    One-vs-One (OvO) multiclass strategy.
    Definition multiclass_strategy.hpp:171
    +
    svm_classifier::OneVsOneStrategy::get_n_classes
    int get_n_classes() const override
    Get number of classes.
    Definition multiclass_strategy.hpp:201
    +
    svm_classifier::OneVsOneStrategy::get_classes
    std::vector< int > get_classes() const override
    Get unique class labels.
    Definition multiclass_strategy.hpp:197
    +
    svm_classifier::OneVsOneStrategy::decision_function
    std::vector< std::vector< double > > decision_function(const torch::Tensor &X, DataConverter &converter) override
    Get decision function values.
    +
    svm_classifier::OneVsOneStrategy::OneVsOneStrategy
    OneVsOneStrategy()
    Constructor.
    +
    svm_classifier::OneVsOneStrategy::supports_probability
    bool supports_probability() const override
    Check if the model supports probability prediction.
    +
    svm_classifier::OneVsOneStrategy::get_strategy_type
    MulticlassStrategy get_strategy_type() const override
    Get strategy type.
    Definition multiclass_strategy.hpp:203
    +
    svm_classifier::OneVsOneStrategy::predict
    std::vector< int > predict(const torch::Tensor &X, DataConverter &converter) override
    Predict class labels.
    +
    svm_classifier::OneVsOneStrategy::predict_proba
    std::vector< std::vector< double > > predict_proba(const torch::Tensor &X, DataConverter &converter) override
    Predict class probabilities.
    +
    svm_classifier::OneVsOneStrategy::fit
    TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override
    Train the multiclass classifier.
    +
    svm_classifier::OneVsOneStrategy::~OneVsOneStrategy
    ~OneVsOneStrategy() override
    Destructor.
    +
    svm_classifier::OneVsRestStrategy
    One-vs-Rest (OvR) multiclass strategy.
    Definition multiclass_strategy.hpp:99
    +
    svm_classifier::OneVsRestStrategy::supports_probability
    bool supports_probability() const override
    Check if the model supports probability prediction.
    +
    svm_classifier::OneVsRestStrategy::OneVsRestStrategy
    OneVsRestStrategy()
    Constructor.
    +
    svm_classifier::OneVsRestStrategy::get_n_classes
    int get_n_classes() const override
    Get number of classes.
    Definition multiclass_strategy.hpp:129
    +
    svm_classifier::OneVsRestStrategy::predict_proba
    std::vector< std::vector< double > > predict_proba(const torch::Tensor &X, DataConverter &converter) override
    Predict class probabilities.
    +
    svm_classifier::OneVsRestStrategy::get_classes
    std::vector< int > get_classes() const override
    Get unique class labels.
    Definition multiclass_strategy.hpp:125
    +
    svm_classifier::OneVsRestStrategy::predict
    std::vector< int > predict(const torch::Tensor &X, DataConverter &converter) override
    Predict class labels.
    +
    svm_classifier::OneVsRestStrategy::decision_function
    std::vector< std::vector< double > > decision_function(const torch::Tensor &X, DataConverter &converter) override
    Get decision function values.
    +
    svm_classifier::OneVsRestStrategy::fit
    TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y, const KernelParameters &params, DataConverter &converter) override
    Train the multiclass classifier.
    +
    svm_classifier::OneVsRestStrategy::~OneVsRestStrategy
    ~OneVsRestStrategy() override
    Destructor.
    +
    svm_classifier::OneVsRestStrategy::get_strategy_type
    MulticlassStrategy get_strategy_type() const override
    Get strategy type.
    Definition multiclass_strategy.hpp:131
    +
    svm_classifier::TrainingMetrics
    Training metrics structure.
    Definition types.hpp:59
    +
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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diff --git a/search/search.css b/search/search.css new file mode 100644 index 0000000..19f76f9 --- /dev/null +++ b/search/search.css @@ -0,0 +1,291 @@ +/*---------------- Search Box positioning */ + +#main-menu > li:last-child { + /* This
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+ padding: 0; +} + + +#MSearchField { + display: inline-block; + vertical-align: middle; + width: 7.5em; + height: 19px; + margin: 0 0.15em; + padding: 0; + line-height: 1em; + border:none; + color: var(--search-foreground-color); + outline: none; + font-family: var(--font-family-search); + -webkit-border-radius: 0px; + border-radius: 0px; + background: none; +} + +@media(hover: none) { + /* to avoid zooming on iOS */ + #MSearchField { + font-size: 16px; + } +} + +#MSearchBox .right { + display: inline-block; + vertical-align: middle; + width: 1.4em; + height: 1.4em; +} + +#MSearchClose { + display: none; + font-size: inherit; + background : none; + border: none; + margin: 0; + padding: 0; + outline: none; + +} + +#MSearchCloseImg { + padding: 0.3em; + margin: 0; +} + +.MSearchBoxActive #MSearchField { + color: var(--search-active-color); +} + + + +/*---------------- Search filter selection */ + +#MSearchSelectWindow { + display: none; + position: absolute; + left: 0; top: 0; + border: 1px solid var(--search-filter-border-color); + background-color: var(--search-filter-background-color); + z-index: 10001; + padding-top: 4px; + padding-bottom: 4px; + -moz-border-radius: 4px; + -webkit-border-top-left-radius: 4px; + -webkit-border-top-right-radius: 4px; + -webkit-border-bottom-left-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -webkit-box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.15); +} + +.SelectItem { + font: 8pt var(--font-family-search); + padding-left: 2px; + padding-right: 12px; + border: 0px; +} + +span.SelectionMark { + margin-right: 4px; + font-family: var(--font-family-monospace); + outline-style: none; + text-decoration: none; +} + +a.SelectItem { + display: block; + outline-style: none; + color: var(--search-filter-foreground-color); + text-decoration: none; + padding-left: 6px; + padding-right: 12px; +} + +a.SelectItem:focus, +a.SelectItem:active { + color: var(--search-filter-foreground-color); + outline-style: none; + text-decoration: none; +} + +a.SelectItem:hover { + color: var(--search-filter-highlight-text-color); + background-color: var(--search-filter-highlight-bg-color); + outline-style: none; + text-decoration: none; + cursor: pointer; + display: block; +} + +/*---------------- Search results window */ + +iframe#MSearchResults { + /*width: 60ex;*/ + height: 15em; +} + +#MSearchResultsWindow { + display: none; + position: absolute; + left: 0; top: 0; + border: 1px solid var(--search-results-border-color); + background-color: var(--search-results-background-color); + z-index:10000; + width: 300px; + height: 400px; + overflow: auto; +} + +/* ----------------------------------- */ + + +#SRIndex { + clear:both; +} + +.SREntry { + font-size: 10pt; + padding-left: 1ex; +} + +.SRPage .SREntry { + font-size: 8pt; + padding: 1px 5px; +} + +div.SRPage { + margin: 5px 2px; + background-color: var(--search-results-background-color); +} + +.SRChildren { + padding-left: 3ex; padding-bottom: .5em +} + +.SRPage .SRChildren { + display: none; +} + +.SRSymbol { + font-weight: bold; + color: var(--search-results-foreground-color); + font-family: var(--font-family-search); + text-decoration: none; + outline: none; +} + +a.SRScope { + display: block; + color: var(--search-results-foreground-color); + font-family: var(--font-family-search); + font-size: 8pt; + text-decoration: none; + outline: none; +} + +a.SRSymbol:focus, a.SRSymbol:active, +a.SRScope:focus, a.SRScope:active { + text-decoration: underline; +} + +span.SRScope { + padding-left: 4px; + font-family: var(--font-family-search); +} + +.SRPage .SRStatus { + padding: 2px 5px; + font-size: 8pt; + font-style: italic; + font-family: var(--font-family-search); +} + +.SRResult { + display: none; +} + +div.searchresults { + margin-left: 10px; + margin-right: 10px; +} + +/*---------------- External search page results */ + +.pages b { + color: white; + padding: 5px 5px 3px 5px; + background-image: var(--nav-gradient-active-image-parent); + background-repeat: repeat-x; + text-shadow: 0 1px 1px #000000; +} + +.pages { + line-height: 17px; + margin-left: 4px; + text-decoration: none; +} + +.hl { + font-weight: bold; +} + +#searchresults { + margin-bottom: 20px; +} + +.searchpages { + margin-top: 10px; +} + diff --git a/search/search.js b/search/search.js new file mode 100644 index 0000000..6fd40c6 --- /dev/null +++ b/search/search.js @@ -0,0 +1,840 @@ +/* + @licstart The following is the entire license notice for the JavaScript code in this file. + + The MIT License (MIT) + + Copyright (C) 1997-2020 by Dimitri van Heesch + + Permission is hereby granted, free of charge, to any person obtaining a copy of this software + and associated documentation files (the "Software"), to deal in the Software without restriction, + including without limitation the rights to use, copy, modify, merge, publish, distribute, + sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all copies or + substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING + BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, + DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + @licend The above is the entire license notice for the JavaScript code in this file + */ +function convertToId(search) +{ + var result = ''; + for (i=0;i do a search + { + this.Search(); + } + } + + this.OnSearchSelectKey = function(evt) + { + var e = (evt) ? evt : window.event; // for IE + if (e.keyCode==40 && this.searchIndex0) // Up + { + this.searchIndex--; + this.OnSelectItem(this.searchIndex); + } + else if (e.keyCode==13 || e.keyCode==27) + { + e.stopPropagation(); + this.OnSelectItem(this.searchIndex); + this.CloseSelectionWindow(); + this.DOMSearchField().focus(); + } + return false; + } + + // --------- Actions + + // Closes the results window. + this.CloseResultsWindow = function() + { + this.DOMPopupSearchResultsWindow().style.display = 'none'; + this.DOMSearchClose().style.display = 'none'; + this.Activate(false); + } + + this.CloseSelectionWindow = function() + { + this.DOMSearchSelectWindow().style.display = 'none'; + } + + // Performs a search. + this.Search = function() + { + this.keyTimeout = 0; + + // strip leading whitespace + var searchValue = this.DOMSearchField().value.replace(/^ +/, ""); + + var code = searchValue.toLowerCase().charCodeAt(0); + var idxChar = searchValue.substr(0, 1).toLowerCase(); + if ( 0xD800 <= code && code <= 0xDBFF && searchValue > 1) // surrogate pair + { + idxChar = searchValue.substr(0, 2); + } + + var jsFile; + + var idx = indexSectionsWithContent[this.searchIndex].indexOf(idxChar); + if (idx!=-1) + { + var hexCode=idx.toString(16); + jsFile = this.resultsPath + indexSectionNames[this.searchIndex] + '_' + hexCode + '.js'; + } + + var loadJS = function(url, impl, loc){ + var scriptTag = document.createElement('script'); + scriptTag.src = url; + scriptTag.onload = impl; + scriptTag.onreadystatechange = impl; + loc.appendChild(scriptTag); + } + + var domPopupSearchResultsWindow = this.DOMPopupSearchResultsWindow(); + var domSearchBox = this.DOMSearchBox(); + var domPopupSearchResults = this.DOMPopupSearchResults(); + var domSearchClose = this.DOMSearchClose(); + var resultsPath = this.resultsPath; + + var handleResults = function() { + document.getElementById("Loading").style.display="none"; + if (typeof searchData !== 'undefined') { + createResults(resultsPath); + document.getElementById("NoMatches").style.display="none"; + } + + if (idx!=-1) { + searchResults.Search(searchValue); + } else { // no file with search results => force empty search results + searchResults.Search('===='); + } + + if (domPopupSearchResultsWindow.style.display!='block') + { + domSearchClose.style.display = 'inline-block'; + var left = getXPos(domSearchBox) + 150; + var top = getYPos(domSearchBox) + 20; + domPopupSearchResultsWindow.style.display = 'block'; + left -= domPopupSearchResults.offsetWidth; + var maxWidth = document.body.clientWidth; + var maxHeight = document.body.clientHeight; + var width = 300; + if (left<10) left=10; + if (width+left+8>maxWidth) width=maxWidth-left-8; + var height = 400; + if (height+top+8>maxHeight) height=maxHeight-top-8; + domPopupSearchResultsWindow.style.top = top + 'px'; + domPopupSearchResultsWindow.style.left = left + 'px'; + domPopupSearchResultsWindow.style.width = width + 'px'; + domPopupSearchResultsWindow.style.height = height + 'px'; + } + } + + if (jsFile) { + loadJS(jsFile, handleResults, this.DOMPopupSearchResultsWindow()); + } else { + handleResults(); + } + + this.lastSearchValue = searchValue; + } + + // -------- Activation Functions + + // Activates or deactivates the search panel, resetting things to + // their default values if necessary. + this.Activate = function(isActive) + { + if (isActive || // open it + this.DOMPopupSearchResultsWindow().style.display == 'block' + ) + { + this.DOMSearchBox().className = 'MSearchBoxActive'; + this.searchActive = true; + } + else if (!isActive) // directly remove the panel + { + this.DOMSearchBox().className = 'MSearchBoxInactive'; + this.searchActive = false; + this.lastSearchValue = '' + this.lastResultsPage = ''; + this.DOMSearchField().value = ''; + } + } +} + +// ----------------------------------------------------------------------- + +// The class that handles everything on the search results page. +function SearchResults(name) +{ + // The number of matches from the last run of . + this.lastMatchCount = 0; + this.lastKey = 0; + this.repeatOn = false; + + // Toggles the visibility of the passed element ID. + this.FindChildElement = function(id) + { + var parentElement = document.getElementById(id); + var element = parentElement.firstChild; + + while (element && element!=parentElement) + { + if (element.nodeName.toLowerCase() == 'div' && element.className == 'SRChildren') + { + return element; + } + + if (element.nodeName.toLowerCase() == 'div' && element.hasChildNodes()) + { + element = element.firstChild; + } + else if (element.nextSibling) + { + element = element.nextSibling; + } + else + { + do + { + element = element.parentNode; + } + while (element && element!=parentElement && !element.nextSibling); + + if (element && element!=parentElement) + { + element = element.nextSibling; + } + } + } + } + + this.Toggle = function(id) + { + var element = this.FindChildElement(id); + if (element) + { + if (element.style.display == 'block') + { + element.style.display = 'none'; + } + else + { + element.style.display = 'block'; + } + } + } + + // Searches for the passed string. If there is no parameter, + // it takes it from the URL query. + // + // Always returns true, since other documents may try to call it + // and that may or may not be possible. + this.Search = function(search) + { + if (!search) // get search word from URL + { + search = window.location.search; + search = search.substring(1); // Remove the leading '?' + search = unescape(search); + } + + search = search.replace(/^ +/, ""); // strip leading spaces + search = search.replace(/ +$/, ""); // strip trailing spaces + search = search.toLowerCase(); + search = convertToId(search); + + var resultRows = document.getElementsByTagName("div"); + var matches = 0; + + var i = 0; + while (i < resultRows.length) + { + var row = resultRows.item(i); + if (row.className == "SRResult") + { + var rowMatchName = row.id.toLowerCase(); + rowMatchName = rowMatchName.replace(/^sr\d*_/, ''); // strip 'sr123_' + + if (search.length<=rowMatchName.length && + rowMatchName.substr(0, search.length)==search) + { + row.style.display = 'block'; + matches++; + } + else + { + row.style.display = 'none'; + } + } + i++; + } + document.getElementById("Searching").style.display='none'; + if (matches == 0) // no results + { + document.getElementById("NoMatches").style.display='block'; + } + else // at least one result + { + document.getElementById("NoMatches").style.display='none'; + } + this.lastMatchCount = matches; + return true; + } + + // return the first item with index index or higher that is visible + this.NavNext = function(index) + { + var focusItem; + while (1) + { + var focusName = 'Item'+index; + focusItem = document.getElementById(focusName); + if (focusItem && focusItem.parentNode.parentNode.style.display=='block') + { + break; + } + else if (!focusItem) // last element + { + break; + } + focusItem=null; + index++; + } + return focusItem; + } + + this.NavPrev = function(index) + { + var focusItem; + while (1) + { + var focusName = 'Item'+index; + focusItem = document.getElementById(focusName); + if (focusItem && focusItem.parentNode.parentNode.style.display=='block') + { + break; + } + else if (!focusItem) // last element + { + break; + } + focusItem=null; + index--; + } + return focusItem; + } + + this.ProcessKeys = function(e) + { + if (e.type == "keydown") + { + this.repeatOn = false; + this.lastKey = e.keyCode; + } + else if (e.type == "keypress") + { + if (!this.repeatOn) + { + if (this.lastKey) this.repeatOn = true; + return false; // ignore first keypress after keydown + } + } + else if (e.type == "keyup") + { + this.lastKey = 0; + this.repeatOn = false; + } + return this.lastKey!=0; + } + + this.Nav = function(evt,itemIndex) + { + var e = (evt) ? evt : window.event; // for IE + if (e.keyCode==13) return true; + if (!this.ProcessKeys(e)) return false; + + if (this.lastKey==38) // Up + { + var newIndex = itemIndex-1; + var focusItem = this.NavPrev(newIndex); + if (focusItem) + { + var child = this.FindChildElement(focusItem.parentNode.parentNode.id); + if (child && child.style.display == 'block') // children visible + { + var n=0; + var tmpElem; + while (1) // search for last child + { + tmpElem = document.getElementById('Item'+newIndex+'_c'+n); + if (tmpElem) + { + focusItem = tmpElem; + } + else // found it! + { + break; + } + n++; + } + } + } + if (focusItem) + { + focusItem.focus(); + } + else // return focus to search field + { + document.getElementById("MSearchField").focus(); + } + } + else if (this.lastKey==40) // Down + { + var newIndex = itemIndex+1; + var focusItem; + var item = document.getElementById('Item'+itemIndex); + var elem = this.FindChildElement(item.parentNode.parentNode.id); + if (elem && elem.style.display == 'block') // children visible + { + focusItem = document.getElementById('Item'+itemIndex+'_c0'); + } + if (!focusItem) focusItem = this.NavNext(newIndex); + if (focusItem) focusItem.focus(); + } + else if (this.lastKey==39) // Right + { + var item = document.getElementById('Item'+itemIndex); + var elem = this.FindChildElement(item.parentNode.parentNode.id); + if (elem) elem.style.display = 'block'; + } + else if (this.lastKey==37) // Left + { + var item = document.getElementById('Item'+itemIndex); + var elem = this.FindChildElement(item.parentNode.parentNode.id); + if (elem) elem.style.display = 'none'; + } + else if (this.lastKey==27) // Escape + { + e.stopPropagation(); + searchBox.CloseResultsWindow(); + document.getElementById("MSearchField").focus(); + } + else if (this.lastKey==13) // Enter + { + return true; + } + return false; + } + + this.NavChild = function(evt,itemIndex,childIndex) + { + var e = (evt) ? evt : window.event; // for IE + if (e.keyCode==13) return true; + if (!this.ProcessKeys(e)) return false; + + if (this.lastKey==38) // Up + { + if (childIndex>0) + { + var newIndex = childIndex-1; + document.getElementById('Item'+itemIndex+'_c'+newIndex).focus(); + } + else // already at first child, jump to parent + { + document.getElementById('Item'+itemIndex).focus(); + } + } + else if (this.lastKey==40) // Down + { + var newIndex = childIndex+1; + var elem = document.getElementById('Item'+itemIndex+'_c'+newIndex); + if (!elem) // last child, jump to parent next parent + { + elem = this.NavNext(itemIndex+1); + } + if (elem) + { + elem.focus(); + } + } + else if (this.lastKey==27) // Escape + { + e.stopPropagation(); + searchBox.CloseResultsWindow(); + document.getElementById("MSearchField").focus(); + } + else if (this.lastKey==13) // Enter + { + return true; + } + return false; + } +} + +function setKeyActions(elem,action) +{ + elem.setAttribute('onkeydown',action); + elem.setAttribute('onkeypress',action); + elem.setAttribute('onkeyup',action); +} + +function setClassAttr(elem,attr) +{ + elem.setAttribute('class',attr); + elem.setAttribute('className',attr); +} + +function createResults(resultsPath) +{ + var results = document.getElementById("SRResults"); + results.innerHTML = ''; + for (var e=0; e-{AmhX=Jf(#6djGiuzAr*{o?=JLmPLyc> z_*`QK&+BH@jWrYJ7>r6%keRM@)Qyv8R=enp0jiI>aWlGyB58O zFVR20d+y`K7vDw(hJF3;>dD*3-?v=<8M)@x|EEGLnJsniYK!2U1 Y!`|5biEc?d1`HDhPgg&ebxsLQ02F6;9RL6T literal 0 HcmV?d00001 diff --git a/splitbard.png b/splitbard.png new file mode 100644 index 0000000000000000000000000000000000000000..8367416d757fd7b6dc4272b6432dc75a75abd068 GIT binary patch literal 282 zcmeAS@N?(olHy`uVBq!ia0vp^Yzz!63>-{AmhX=Jf@VhhFKy35^fiT zT~&lUj3=cDh^%3HDY9k5CEku}PHXNoNC(_$U3XPb&Q*ME25pT;2(*BOgAf<+R$lzakPG`kF31()Fx{L5Wrac|GQzjeE= zueY1`Ze{#x<8=S|`~MgGetGce)#vN&|J{Cd^tS%;tBYTo?+^d68<#n_Y_xx`J||4O V@QB{^CqU0Kc)I$ztaD0e0svEzbJzd? literal 0 HcmV?d00001 diff --git a/structsvm__classifier_1_1EvaluationMetrics-members.html b/structsvm__classifier_1_1EvaluationMetrics-members.html new file mode 100644 index 0000000..64e919b --- /dev/null +++ b/structsvm__classifier_1_1EvaluationMetrics-members.html @@ -0,0 +1,94 @@ + + + + + + + +SVM Classifier C++: Member List + + + + + + + + + +
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    SVM Classifier C++ 1.0.0 +
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    accuracysvm_classifier::EvaluationMetrics
    confusion_matrixsvm_classifier::EvaluationMetrics
    f1_scoresvm_classifier::EvaluationMetrics
    precisionsvm_classifier::EvaluationMetrics
    recallsvm_classifier::EvaluationMetrics
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
    + + diff --git a/structsvm__classifier_1_1EvaluationMetrics.html b/structsvm__classifier_1_1EvaluationMetrics.html new file mode 100644 index 0000000..da7e250 --- /dev/null +++ b/structsvm__classifier_1_1EvaluationMetrics.html @@ -0,0 +1,211 @@ + + + + + + + +SVM Classifier C++: svm_classifier::EvaluationMetrics Struct Reference + + + + + + + + + +
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    SVM Classifier C++ 1.0.0 +
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    svm_classifier::EvaluationMetrics Struct Reference
    +
    +
    + +

    Model evaluation metrics. + More...

    + +

    #include <types.hpp>

    + + + + + + + + + + + + + + + + + +

    +Public Attributes

    double accuracy = 0.0
     Classification accuracy.
     
    double precision = 0.0
     Macro-averaged precision.
     
    double recall = 0.0
     Macro-averaged recall.
     
    double f1_score = 0.0
     Macro-averaged F1-score.
     
    std::vector< std::vector< int > > confusion_matrix
     Confusion matrix.
     
    +

    Detailed Description

    +

    Model evaluation metrics.

    + +

    Definition at line 70 of file types.hpp.

    +

    Member Data Documentation

    + +

    ◆ accuracy

    + +
    +
    + + + + +
    double svm_classifier::EvaluationMetrics::accuracy = 0.0
    +
    + +

    Classification accuracy.

    + +

    Definition at line 71 of file types.hpp.

    + +
    +
    + +

    ◆ confusion_matrix

    + +
    +
    + + + + +
    std::vector<std::vector<int> > svm_classifier::EvaluationMetrics::confusion_matrix
    +
    + +

    Confusion matrix.

    + +

    Definition at line 75 of file types.hpp.

    + +
    +
    + +

    ◆ f1_score

    + +
    +
    + + + + +
    double svm_classifier::EvaluationMetrics::f1_score = 0.0
    +
    + +

    Macro-averaged F1-score.

    + +

    Definition at line 74 of file types.hpp.

    + +
    +
    + +

    ◆ precision

    + +
    +
    + + + + +
    double svm_classifier::EvaluationMetrics::precision = 0.0
    +
    + +

    Macro-averaged precision.

    + +

    Definition at line 72 of file types.hpp.

    + +
    +
    + +

    ◆ recall

    + +
    +
    + + + + +
    double svm_classifier::EvaluationMetrics::recall = 0.0
    +
    + +

    Macro-averaged recall.

    + +

    Definition at line 73 of file types.hpp.

    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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    This is the complete list of members for svm_classifier::PredictionResult, including all inherited members.

    + + + + + +
    decision_valuessvm_classifier::PredictionResult
    has_probabilitiessvm_classifier::PredictionResult
    predictionssvm_classifier::PredictionResult
    probabilitiessvm_classifier::PredictionResult
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
    + + diff --git a/structsvm__classifier_1_1PredictionResult.html b/structsvm__classifier_1_1PredictionResult.html new file mode 100644 index 0000000..449eae6 --- /dev/null +++ b/structsvm__classifier_1_1PredictionResult.html @@ -0,0 +1,190 @@ + + + + + + + +SVM Classifier C++: svm_classifier::PredictionResult Struct Reference + + + + + + + + + +
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    SVM Classifier C++ 1.0.0 +
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    svm_classifier::PredictionResult Struct Reference
    +
    +
    + +

    Prediction result structure. + More...

    + +

    #include <types.hpp>

    + + + + + + + + + + + + + + +

    +Public Attributes

    std::vector< int > predictions
     Predicted class labels.
     
    std::vector< std::vector< double > > probabilities
     Class probabilities (if available)
     
    std::vector< std::vector< double > > decision_values
     Decision function values.
     
    bool has_probabilities = false
     Whether probabilities are available.
     
    +

    Detailed Description

    +

    Prediction result structure.

    + +

    Definition at line 49 of file types.hpp.

    +

    Member Data Documentation

    + +

    ◆ decision_values

    + +
    +
    + + + + +
    std::vector<std::vector<double> > svm_classifier::PredictionResult::decision_values
    +
    + +

    Decision function values.

    + +

    Definition at line 52 of file types.hpp.

    + +
    +
    + +

    ◆ has_probabilities

    + +
    +
    + + + + +
    bool svm_classifier::PredictionResult::has_probabilities = false
    +
    + +

    Whether probabilities are available.

    + +

    Definition at line 53 of file types.hpp.

    + +
    +
    + +

    ◆ predictions

    + +
    +
    + + + + +
    std::vector<int> svm_classifier::PredictionResult::predictions
    +
    + +

    Predicted class labels.

    + +

    Definition at line 50 of file types.hpp.

    + +
    +
    + +

    ◆ probabilities

    + +
    +
    + + + + +
    std::vector<std::vector<double> > svm_classifier::PredictionResult::probabilities
    +
    + +

    Class probabilities (if available)

    + +

    Definition at line 51 of file types.hpp.

    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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    This is the complete list of members for svm_classifier::TrainingMetrics, including all inherited members.

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    iterationssvm_classifier::TrainingMetrics
    objective_valuesvm_classifier::TrainingMetrics
    status (defined in svm_classifier::TrainingMetrics)svm_classifier::TrainingMetrics
    support_vectorssvm_classifier::TrainingMetrics
    training_timesvm_classifier::TrainingMetrics
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
    + + diff --git a/structsvm__classifier_1_1TrainingMetrics.html b/structsvm__classifier_1_1TrainingMetrics.html new file mode 100644 index 0000000..b29546e --- /dev/null +++ b/structsvm__classifier_1_1TrainingMetrics.html @@ -0,0 +1,208 @@ + + + + + + + +SVM Classifier C++: svm_classifier::TrainingMetrics Struct Reference + + + + + + + + + +
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    SVM Classifier C++ 1.0.0 +
    +
    High-performance Support Vector Machine classifier with scikit-learn compatible API
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    svm_classifier::TrainingMetrics Struct Reference
    +
    +
    + +

    Training metrics structure. + More...

    + +

    #include <types.hpp>

    + + + + + + + + + + + + + + + + +

    +Public Attributes

    double training_time = 0.0
     Training time in seconds.
     
    int support_vectors = 0
     Number of support vectors.
     
    int iterations = 0
     Number of iterations.
     
    double objective_value = 0.0
     Final objective value.
     
    TrainingStatus status = TrainingStatus::SUCCESS
     
    +

    Detailed Description

    +

    Training metrics structure.

    + +

    Definition at line 59 of file types.hpp.

    +

    Member Data Documentation

    + +

    ◆ iterations

    + +
    +
    + + + + +
    int svm_classifier::TrainingMetrics::iterations = 0
    +
    + +

    Number of iterations.

    + +

    Definition at line 62 of file types.hpp.

    + +
    +
    + +

    ◆ objective_value

    + +
    +
    + + + + +
    double svm_classifier::TrainingMetrics::objective_value = 0.0
    +
    + +

    Final objective value.

    + +

    Definition at line 63 of file types.hpp.

    + +
    +
    + +

    ◆ status

    + +
    +
    + + + + +
    TrainingStatus svm_classifier::TrainingMetrics::status = TrainingStatus::SUCCESS
    +
    + +

    Definition at line 64 of file types.hpp.

    + +
    +
    + +

    ◆ support_vectors

    + +
    +
    + + + + +
    int svm_classifier::TrainingMetrics::support_vectors = 0
    +
    + +

    Number of support vectors.

    + +

    Definition at line 61 of file types.hpp.

    + +
    +
    + +

    ◆ training_time

    + +
    +
    + + + + +
    double svm_classifier::TrainingMetrics::training_time = 0.0
    +
    + +

    Training time in seconds.

    + +

    Definition at line 60 of file types.hpp.

    + +
    +
    +
    The documentation for this struct was generated from the following file: +
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
    + + diff --git a/svm__classifier_8hpp_source.html b/svm__classifier_8hpp_source.html new file mode 100644 index 0000000..7fdeec4 --- /dev/null +++ b/svm__classifier_8hpp_source.html @@ -0,0 +1,238 @@ + + + + + + + +SVM Classifier C++: include/svm_classifier/svm_classifier.hpp Source File + + + + + + + + + +
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    SVM Classifier C++ 1.0.0 +
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    svm_classifier.hpp
    +
    +
    +
    1#pragma once
    +
    2
    +
    3#include "types.hpp"
    +
    4#include "kernel_parameters.hpp"
    +
    5#include "data_converter.hpp"
    +
    6#include "multiclass_strategy.hpp"
    +
    7#include <torch/torch.h>
    +
    8#include <nlohmann/json.hpp>
    +
    9#include <memory>
    +
    10#include <string>
    +
    11
    +
    12namespace svm_classifier {
    +
    13
    +
    +
    21 class SVMClassifier {
    +
    22 public:
    +
    26 SVMClassifier();
    +
    27
    +
    32 explicit SVMClassifier(const nlohmann::json& config);
    +
    33
    +
    40 SVMClassifier(KernelType kernel,
    +
    41 double C = 1.0,
    +
    42 MulticlassStrategy multiclass_strategy = MulticlassStrategy::ONE_VS_REST);
    +
    43
    +
    47 ~SVMClassifier();
    +
    48
    +
    52 SVMClassifier(const SVMClassifier&) = delete;
    +
    53
    +
    57 SVMClassifier& operator=(const SVMClassifier&) = delete;
    +
    58
    +
    62 SVMClassifier(SVMClassifier&&) noexcept;
    +
    63
    +
    67 SVMClassifier& operator=(SVMClassifier&&) noexcept;
    +
    68
    +
    77 TrainingMetrics fit(const torch::Tensor& X, const torch::Tensor& y);
    +
    78
    +
    85 torch::Tensor predict(const torch::Tensor& X);
    +
    86
    +
    93 torch::Tensor predict_proba(const torch::Tensor& X);
    +
    94
    +
    101 torch::Tensor decision_function(const torch::Tensor& X);
    +
    102
    +
    110 double score(const torch::Tensor& X, const torch::Tensor& y_true);
    +
    111
    +
    118 EvaluationMetrics evaluate(const torch::Tensor& X, const torch::Tensor& y_true);
    +
    119
    +
    125 void set_parameters(const nlohmann::json& config);
    +
    126
    +
    131 nlohmann::json get_parameters() const;
    +
    132
    +
    137 bool is_fitted() const { return is_fitted_; }
    +
    138
    +
    143 int get_n_classes() const;
    +
    144
    +
    149 std::vector<int> get_classes() const;
    +
    150
    +
    155 int get_n_features() const { return n_features_; }
    +
    156
    +
    161 TrainingMetrics get_training_metrics() const { return training_metrics_; }
    +
    162
    +
    167 bool supports_probability() const;
    +
    168
    +
    174 void save_model(const std::string& filename) const;
    +
    175
    +
    181 void load_model(const std::string& filename);
    +
    182
    +
    187 KernelType get_kernel_type() const { return params_.get_kernel_type(); }
    +
    188
    +
    193 MulticlassStrategy get_multiclass_strategy() const { return params_.get_multiclass_strategy(); }
    +
    194
    +
    199 SVMLibrary get_svm_library() const { return get_svm_library(params_.get_kernel_type()); }
    +
    200
    +
    208 std::vector<double> cross_validate(const torch::Tensor& X,
    +
    209 const torch::Tensor& y,
    +
    210 int cv = 5);
    +
    211
    +
    220 nlohmann::json grid_search(const torch::Tensor& X,
    +
    221 const torch::Tensor& y,
    +
    222 const nlohmann::json& param_grid,
    +
    223 int cv = 5);
    +
    224
    +
    230 torch::Tensor get_feature_importance() const;
    +
    231
    +
    235 void reset();
    +
    236
    +
    237 private:
    +
    238 KernelParameters params_;
    +
    239 std::unique_ptr<MulticlassStrategyBase> multiclass_strategy_;
    +
    240 std::unique_ptr<DataConverter> data_converter_;
    +
    241
    +
    242 bool is_fitted_;
    +
    243 int n_features_;
    +
    244 TrainingMetrics training_metrics_;
    +
    245
    +
    252 void validate_input(const torch::Tensor& X,
    +
    253 const torch::Tensor& y = torch::Tensor(),
    +
    254 bool check_fitted = false);
    +
    255
    +
    259 void initialize_multiclass_strategy();
    +
    260
    +
    267 std::vector<std::vector<int>> calculate_confusion_matrix(const std::vector<int>& y_true,
    +
    268 const std::vector<int>& y_pred);
    +
    269
    +
    275 std::tuple<double, double, double> calculate_metrics_from_confusion_matrix(
    +
    276 const std::vector<std::vector<int>>& confusion_matrix);
    +
    277
    +
    286 std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor>
    +
    287 split_for_cv(const torch::Tensor& X, const torch::Tensor& y, int fold, int n_folds);
    +
    288
    +
    294 std::vector<nlohmann::json> generate_param_combinations(const nlohmann::json& param_grid);
    +
    295 };
    +
    +
    296
    +
    297} // namespace svm_classifier
    +
    svm_classifier::SVMClassifier
    Support Vector Machine Classifier with scikit-learn compatible API.
    Definition svm_classifier.hpp:21
    +
    svm_classifier::SVMClassifier::score
    double score(const torch::Tensor &X, const torch::Tensor &y_true)
    Calculate accuracy score on test data.
    +
    svm_classifier::SVMClassifier::get_training_metrics
    TrainingMetrics get_training_metrics() const
    Get training metrics from last fit.
    Definition svm_classifier.hpp:161
    +
    svm_classifier::SVMClassifier::get_multiclass_strategy
    MulticlassStrategy get_multiclass_strategy() const
    Get multiclass strategy.
    Definition svm_classifier.hpp:193
    +
    svm_classifier::SVMClassifier::operator=
    SVMClassifier & operator=(const SVMClassifier &)=delete
    Copy assignment (deleted - models are not copyable)
    +
    svm_classifier::SVMClassifier::~SVMClassifier
    ~SVMClassifier()
    Destructor.
    +
    svm_classifier::SVMClassifier::get_feature_importance
    torch::Tensor get_feature_importance() const
    Get feature importance (for linear kernels only)
    +
    svm_classifier::SVMClassifier::SVMClassifier
    SVMClassifier(const nlohmann::json &config)
    Constructor with JSON parameters.
    +
    svm_classifier::SVMClassifier::SVMClassifier
    SVMClassifier(const SVMClassifier &)=delete
    Copy constructor (deleted - models are not copyable)
    +
    svm_classifier::SVMClassifier::get_svm_library
    SVMLibrary get_svm_library() const
    Get SVM library being used.
    Definition svm_classifier.hpp:199
    +
    svm_classifier::SVMClassifier::evaluate
    EvaluationMetrics evaluate(const torch::Tensor &X, const torch::Tensor &y_true)
    Calculate detailed evaluation metrics.
    +
    svm_classifier::SVMClassifier::SVMClassifier
    SVMClassifier()
    Default constructor with default parameters.
    +
    svm_classifier::SVMClassifier::supports_probability
    bool supports_probability() const
    Check if the current model supports probability prediction.
    +
    svm_classifier::SVMClassifier::cross_validate
    std::vector< double > cross_validate(const torch::Tensor &X, const torch::Tensor &y, int cv=5)
    Perform cross-validation.
    +
    svm_classifier::SVMClassifier::get_kernel_type
    KernelType get_kernel_type() const
    Get kernel type.
    Definition svm_classifier.hpp:187
    +
    svm_classifier::SVMClassifier::load_model
    void load_model(const std::string &filename)
    Load model from file.
    +
    svm_classifier::SVMClassifier::predict
    torch::Tensor predict(const torch::Tensor &X)
    Predict class labels for samples.
    +
    svm_classifier::SVMClassifier::is_fitted
    bool is_fitted() const
    Check if the model is fitted/trained.
    Definition svm_classifier.hpp:137
    +
    svm_classifier::SVMClassifier::get_n_classes
    int get_n_classes() const
    Get the number of classes.
    +
    svm_classifier::SVMClassifier::get_n_features
    int get_n_features() const
    Get the number of features.
    Definition svm_classifier.hpp:155
    +
    svm_classifier::SVMClassifier::get_parameters
    nlohmann::json get_parameters() const
    Get current parameters as JSON.
    +
    svm_classifier::SVMClassifier::fit
    TrainingMetrics fit(const torch::Tensor &X, const torch::Tensor &y)
    Train the SVM classifier.
    +
    svm_classifier::SVMClassifier::SVMClassifier
    SVMClassifier(KernelType kernel, double C=1.0, MulticlassStrategy multiclass_strategy=MulticlassStrategy::ONE_VS_REST)
    Constructor with explicit parameters.
    +
    svm_classifier::SVMClassifier::reset
    void reset()
    Reset the classifier (clear trained model)
    +
    svm_classifier::SVMClassifier::predict_proba
    torch::Tensor predict_proba(const torch::Tensor &X)
    Predict class probabilities for samples.
    +
    svm_classifier::SVMClassifier::save_model
    void save_model(const std::string &filename) const
    Save model to file.
    +
    svm_classifier::SVMClassifier::decision_function
    torch::Tensor decision_function(const torch::Tensor &X)
    Get decision function values.
    +
    svm_classifier::SVMClassifier::set_parameters
    void set_parameters(const nlohmann::json &config)
    Set parameters from JSON configuration.
    +
    svm_classifier::SVMClassifier::SVMClassifier
    SVMClassifier(SVMClassifier &&) noexcept
    Move constructor.
    +
    svm_classifier::SVMClassifier::get_classes
    std::vector< int > get_classes() const
    Get unique class labels.
    +
    svm_classifier::SVMClassifier::grid_search
    nlohmann::json grid_search(const torch::Tensor &X, const torch::Tensor &y, const nlohmann::json &param_grid, int cv=5)
    Find optimal hyperparameters using grid search.
    +
    svm_classifier::EvaluationMetrics
    Model evaluation metrics.
    Definition types.hpp:70
    +
    svm_classifier::TrainingMetrics
    Training metrics structure.
    Definition types.hpp:59
    +
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
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    SVM Classifier C++ 1.0.0 +
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    High-performance Support Vector Machine classifier with scikit-learn compatible API
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    types.hpp
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    +
    +
    1#pragma once
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    2
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    3#include <string>
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    4#include <vector>
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    5#include <memory>
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    6
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    7namespace svm_classifier {
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    8
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    12 enum class KernelType {
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    13 LINEAR,
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    14 RBF,
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    15 POLYNOMIAL,
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    16 SIGMOID
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    17 };
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    18
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    22 enum class MulticlassStrategy {
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    23 ONE_VS_REST,
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    24 ONE_VS_ONE
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    25 };
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    26
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    30 enum class SVMLibrary {
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    31 LIBLINEAR,
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    32 LIBSVM
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    33 };
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    34
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    38 enum class TrainingStatus {
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    39 SUCCESS,
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    40 INVALID_PARAMETERS,
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    41 INSUFFICIENT_DATA,
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    42 MEMORY_ERROR,
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    43 CONVERGENCE_ERROR
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    44 };
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    45
    +
    +
    49 struct PredictionResult {
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    50 std::vector<int> predictions;
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    51 std::vector<std::vector<double>> probabilities;
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    52 std::vector<std::vector<double>> decision_values;
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    53 bool has_probabilities = false;
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    54 };
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    +
    55
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    +
    59 struct TrainingMetrics {
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    60 double training_time = 0.0;
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    61 int support_vectors = 0;
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    62 int iterations = 0;
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    63 double objective_value = 0.0;
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    64 TrainingStatus status = TrainingStatus::SUCCESS;
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    65 };
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    +
    66
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    +
    70 struct EvaluationMetrics {
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    71 double accuracy = 0.0;
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    72 double precision = 0.0;
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    73 double recall = 0.0;
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    74 double f1_score = 0.0;
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    75 std::vector<std::vector<int>> confusion_matrix;
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    76 };
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    +
    77
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    81 inline std::string kernel_type_to_string(KernelType kernel)
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    82 {
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    83 switch (kernel) {
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    84 case KernelType::LINEAR: return "linear";
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    85 case KernelType::RBF: return "rbf";
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    86 case KernelType::POLYNOMIAL: return "polynomial";
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    87 case KernelType::SIGMOID: return "sigmoid";
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    88 default: return "unknown";
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    89 }
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    90 }
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    91
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    95 inline KernelType string_to_kernel_type(const std::string& kernel_str)
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    96 {
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    97 if (kernel_str == "linear") return KernelType::LINEAR;
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    98 if (kernel_str == "rbf") return KernelType::RBF;
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    99 if (kernel_str == "polynomial" || kernel_str == "poly") return KernelType::POLYNOMIAL;
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    100 if (kernel_str == "sigmoid") return KernelType::SIGMOID;
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    101 throw std::invalid_argument("Unknown kernel type: " + kernel_str);
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    102 }
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    103
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    107 inline std::string multiclass_strategy_to_string(MulticlassStrategy strategy)
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    108 {
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    109 switch (strategy) {
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    110 case MulticlassStrategy::ONE_VS_REST: return "ovr";
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    111 case MulticlassStrategy::ONE_VS_ONE: return "ovo";
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    112 default: return "unknown";
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    113 }
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    114 }
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    115
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    119 inline MulticlassStrategy string_to_multiclass_strategy(const std::string& strategy_str)
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    120 {
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    121 if (strategy_str == "ovr" || strategy_str == "one_vs_rest") {
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    122 return MulticlassStrategy::ONE_VS_REST;
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    123 }
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    124 if (strategy_str == "ovo" || strategy_str == "one_vs_one") {
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    125 return MulticlassStrategy::ONE_VS_ONE;
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    126 }
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    127 throw std::invalid_argument("Unknown multiclass strategy: " + strategy_str);
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    128 }
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    129
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    133 inline SVMLibrary get_svm_library(KernelType kernel)
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    134 {
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    135 return (kernel == KernelType::LINEAR) ? SVMLibrary::LIBLINEAR : SVMLibrary::LIBSVM;
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    136 }
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    137
    +
    138} // namespace svm_classifier
    +
    svm_classifier::EvaluationMetrics
    Model evaluation metrics.
    Definition types.hpp:70
    +
    svm_classifier::EvaluationMetrics::f1_score
    double f1_score
    Macro-averaged F1-score.
    Definition types.hpp:74
    +
    svm_classifier::EvaluationMetrics::recall
    double recall
    Macro-averaged recall.
    Definition types.hpp:73
    +
    svm_classifier::EvaluationMetrics::confusion_matrix
    std::vector< std::vector< int > > confusion_matrix
    Confusion matrix.
    Definition types.hpp:75
    +
    svm_classifier::EvaluationMetrics::accuracy
    double accuracy
    Classification accuracy.
    Definition types.hpp:71
    +
    svm_classifier::EvaluationMetrics::precision
    double precision
    Macro-averaged precision.
    Definition types.hpp:72
    +
    svm_classifier::PredictionResult
    Prediction result structure.
    Definition types.hpp:49
    +
    svm_classifier::PredictionResult::predictions
    std::vector< int > predictions
    Predicted class labels.
    Definition types.hpp:50
    +
    svm_classifier::PredictionResult::decision_values
    std::vector< std::vector< double > > decision_values
    Decision function values.
    Definition types.hpp:52
    +
    svm_classifier::PredictionResult::probabilities
    std::vector< std::vector< double > > probabilities
    Class probabilities (if available)
    Definition types.hpp:51
    +
    svm_classifier::PredictionResult::has_probabilities
    bool has_probabilities
    Whether probabilities are available.
    Definition types.hpp:53
    +
    svm_classifier::TrainingMetrics
    Training metrics structure.
    Definition types.hpp:59
    +
    svm_classifier::TrainingMetrics::objective_value
    double objective_value
    Final objective value.
    Definition types.hpp:63
    +
    svm_classifier::TrainingMetrics::support_vectors
    int support_vectors
    Number of support vectors.
    Definition types.hpp:61
    +
    svm_classifier::TrainingMetrics::iterations
    int iterations
    Number of iterations.
    Definition types.hpp:62
    +
    svm_classifier::TrainingMetrics::training_time
    double training_time
    Training time in seconds.
    Definition types.hpp:60
    +
    + +
    +Generated on Sun Jun 22 2025 11:25:27 for SVM Classifier C++ by doxygen 1.9.8 +
    + +