plssvm::model< T, U > Class Template Reference

Implements a class encapsulating the result of a call to the SVM fit function. A model is used to predict the labels of a new data set. More...

#include <model.hpp>

Collaboration diagram for plssvm::model< T, U >:

Public Types
using	real_type = T
	The type of the data points: either float or double.
using	label_type = U
	The type of the labels: any arithmetic type or std::string.
using	size_type = std::size_t
	The unsigned size type.

Public Member Functions
	model (const std::string &filename)
	Read a previously learned model from the LIBSVM model file filename. More...
void	save (const std::string &filename) const
	Save the model to a LIBSVM model file for later usage. More...
size_type	num_support_vectors () const noexcept
	The number of support vectors used in this model. More...
size_type	num_features () const noexcept
	The number of features of the support vectors used in this model. More...
const parameter &	get_params () const noexcept
	Return the SVM parameter that were used to learn this model. More...
const std::vector< std::vector< real_type > > &	support_vectors () const noexcept
	The support vectors representing the learned model. More...
const std::vector< label_type > &	labels () const noexcept
	Returns the labels of the support vectors. More...
size_type	num_different_labels () const noexcept
	Returns the number of different labels in this data set. More...
std::vector< label_type >	different_labels () const
	Returns the different labels of the support vectors. More...
const std::vector< real_type > &	weights () const noexcept
	The learned weights for the support vectors. More...
real_type	rho () const noexcept
	The bias value after learning. More...

Private Member Functions
	model (parameter params, data_set< real_type, label_type > data)
	Create a new model using the SVM parameter params and the data. More...

Private Attributes
parameter	params_ {}
	The SVM parameter used to learn this model.
data_set< real_type, label_type >	data_ {}
	The data (support vectors + respective label) used to learn this model.
size_type	num_support_vectors_ { 0 }
	The number of support vectors representing this model.
size_type	num_features_ { 0 }
	The number of features per support vector.
std::shared_ptr< std::vector< real_type > >	alpha_ptr_ { nullptr }
	The learned weights for each support vector.
real_type	rho_ { 0.0 }
	The bias after learning this model.
std::shared_ptr< std::vector< real_type > >	w_ { std::make_shared<std::vector<real_type>>() }
	A vector used to speedup the prediction in case of the linear kernel function. More...

Friends
class	csvm

Detailed Description

template<typename T, typename U = int>
class plssvm::model< T, U >

Implements a class encapsulating the result of a call to the SVM fit function. A model is used to predict the labels of a new data set.

Template Parameters

T	the floating point type of the data (must either be float or double)
U	the type of the used labels (must be an arithmetic type or std:string; default: int)

Examples

csvm_examples.cpp, and model_examples.cpp.

Constructor & Destructor Documentation

model() [1/2]

template<typename T , typename U >

plssvm::model< T, U >::model ( const std::string &  filename )

explicit

Read a previously learned model from the LIBSVM model file filename.

Parameters

[in]

filename

the model file to read

Exceptions

plssvm::invalid_file_format_exception

all exceptions thrown by plssvm::detail::io::parse_libsvm_model_header and plssvm::detail::io::parse_libsvm_data

model() [2/2]

template<typename T , typename U >

plssvm::model< T, U >::model ( parameter  params, data_set< real_type, label_type >  data  )

private

Create a new model using the SVM parameter params and the data.

Default initializes the weights, i.e., no weights have currently been learned.

Note

This constructor may only be used in the befriended base C-SVM class!

Parameters

[in]	params	the SVM parameters used to learn this model
[in]	data	the data used to learn this model

Member Function Documentation

save()

template<typename T , typename U >

void plssvm::model< T, U >::save

(

const std::string &

filename

)

const

Save the model to a LIBSVM model file for later usage.

Parameters

[in]

filename

the file to save the model to

Examples

model_examples.cpp.

num_support_vectors()

template<typename T , typename U = int>

size_type plssvm::model< T, U >::num_support_vectors ( ) const

inlinenoexcept

The number of support vectors used in this model.

Returns

the number of support vectors ([[nodiscard]])

num_features()

template<typename T , typename U = int>

size_type plssvm::model< T, U >::num_features ( ) const

inlinenoexcept

The number of features of the support vectors used in this model.

Returns

the number of features ([[nodiscard]])

get_params()

template<typename T , typename U = int>

const parameter& plssvm::model< T, U >::get_params ( ) const

inlinenoexcept

Return the SVM parameter that were used to learn this model.

Returns

the SVM parameter ([[nodiscard]])

support_vectors()

template<typename T , typename U = int>

const std::vector<std::vector<real_type> >& plssvm::model< T, U >::support_vectors ( ) const

inlinenoexcept

The support vectors representing the learned model.

The support vectors are of dimension num_support_vectors() x num_features().

Returns

the support vectors ([[nodiscard]])

labels()

template<typename T , typename U = int>

const std::vector<label_type>& plssvm::model< T, U >::labels ( ) const

inlinenoexcept

Returns the labels of the support vectors.

If the labels are present, they can be retrieved as std::vector using: dataset.labels()->get().

Returns

the labels ([[nodiscard]])

num_different_labels()

template<typename T , typename U = int>

size_type plssvm::model< T, U >::num_different_labels ( ) const

inlinenoexcept

Returns the number of different labels in this data set.

If the data set contains the labels std::vector<int>{ -1, 1, 1, -1, -1, 1 }, this function returns 2. It is the same as: model.different_labels().size()

Returns

the number of different labels ([[nodiscard]])

different_labels()

template<typename T , typename U = int>

std::vector<label_type> plssvm::model< T, U >::different_labels ( ) const

inline

Returns the different labels of the support vectors.

If the support vectors contain the labels std::vector<int>{ -1, 1, 1, -1, -1, 1 }, this function returns the labels { -1, 1 }.

Returns

all different labels ([[nodiscard]])

weights()

template<typename T , typename U = int>

const std::vector<real_type>& plssvm::model< T, U >::weights ( ) const

inlinenoexcept

The learned weights for the support vectors.

It is of size num_support_vectors().

Returns

the weights ([[nodiscard]])

rho()

template<typename T , typename U = int>

real_type plssvm::model< T, U >::rho ( ) const

inlinenoexcept

The bias value after learning.

Returns

the bias rho ([[nodiscard]])

Member Data Documentation

w_

template<typename T , typename U = int>

std::shared_ptr<std::vector<real_type> > plssvm::model< T, U >::w_ { std::make_shared<std::vector<real_type>>() }

private

A vector used to speedup the prediction in case of the linear kernel function.

Will be reused by subsequent calls to plssvm::csvm::fit/plssvm::csvm::score with the same plssvm::model.

Note

Must be initialized to an empty vector instead of a nullptr in order to be passable as const reference.

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

• include/plssvm/model.hpp