Mathematical formulation

Programming Interface

template<typename

Float

= float, typename

Method

= method::by_default, typename

Task

= task::by_default, typename

Kernel

= linear_kernel::descriptor<Float>>

class

descriptor

Template Parameters

Float
– The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
Method
– Tag-type that specifies an implementation of algorithm. Can be
method::thunder
or
method::smo
.
Task
– Tag-type that specifies the type of the problem to solve. Can be
task::classification
,
task::nu_classification
,
task::regression
, or
task::nu_regression
.

Constructors

descriptor

(

const

Kernel &

kernel

= kernel_t{})

Creates a new instance of the class with the given descriptor of the kernel function.

Properties

bool

shrinking

A flag that enables the use of a shrinking optimization technique. Used with

method::smo

split-finding method only.

Default value

: true.

Getter & Setter

bool get_shrinking() const
auto & set_shrinking(bool value)

double

tau

The threshold parameter for computing the quadratic coefficient.

Default value

: 1e-6.

Getter & Setter

double get_tau() const
auto & set_tau(double value)

Invariants

tau > 0.0

double

accuracy_threshold

The threshold for the stop condition.

Default value

: 0.0.

Getter & Setter

double get_accuracy_threshold() const
auto & set_accuracy_threshold(double value)

Invariants

accuracy_threshold >= 0.0

double

c

The upper bound in constraints of the quadratic optimization problem. Used with

task::classification

,

task::regression

, and

task::nu_regression

.

Default value

: 1.0.

Getter & Setter

template > double get_c() const
template > auto & set_c(double value)

Invariants

c > 0

std::int64_t

max_iteration_count

The maximum number of iterations .

Default value

: 100000.

Getter & Setter

std::int64_t get_max_iteration_count() const
auto & set_max_iteration_count(std::int64_t value)

Invariants

max_iteration_count >= 0

double

epsilon

The epsilon. Used with

task::regression

only.

Default value

: 0.1.

Getter & Setter

template > double get_epsilon() const
template > auto & set_epsilon(double value)

Invariants

epsilon >= 0

const

Kernel &

kernel

The descriptor of kernel function . Can be

linear_kernel::descriptor

or

polynomial_kernel::descriptor

or

rbf_kernel::descriptor

or

sigmoid_kernel::descriptor

.

Getter & Setter

const Kernel & get_kernel() const
auto & set_kernel(const Kernel &kernel)

std::int64_t

class_count

The number of classes. Used with

task::classification

and

task::nu_classification

.

Default value

: 2.

Getter & Setter

template > std::int64_t get_class_count() const
template > auto & set_class_count(std::int64_t value)

Invariants

class_count >= 2

double

nu

The nu. Used with

task::nu_classification

and

task::nu_regression

.

Default value

: 0.5.

Getter & Setter

template > double get_nu() const
template > auto & set_nu(double value)

Invariants

0 < nu <= 1

double

cache_size

The size of cache (in megabytes) for storing the values of the kernel matrix.

Default value

: 200.0.

Getter & Setter

double get_cache_size() const
auto & set_cache_size(double value)

Invariants

cache_size >= 0.0

struct

smo

Tag-type that denotes SMO computational method.

struct

thunder

Tag-type that denotes Thunder computational method.

using

by_default

= thunder

Alias tag-type for Thunder computational method.

struct

classification

Tag-type that parameterizes entities that are used for solving classification problem.

struct

nu_classification

Tag-type that parameterizes entities that are used for solving nu-classification problem.

struct

nu_regression

Tag-type that parameterizes entities used for solving nu-regression problem.

struct

regression

Tag-type that parameterizes entities used for solving regression problem.

using

by_default

= classification

Alias tag-type for classification task.

template<typename

Task

= task::by_default>

class

model

Template Parameters

Task

– Tag-type that specifies the type of the problem to solve. Can be

task::classification

,

task::nu_classification

,

task::regression

, or

task::nu_regression

.

Constructors

model

()

Creates a new instance of the class with the default property values.

Public Methods

std::int64_t

get_support_vector_count

()

const

The number of support vectors.

Properties

double

bias

The bias.

Default value

: 0.0.

Getter & Setter

double get_bias() const
auto & set_bias(double value)

std::int64_t

second_class_response

The second unique value in class responses. Used with

task::classification

and

task::nu_classification

.

Getter & Setter

std::int64_t get_second_class_response() const
template > auto & set_second_class_response(std::int64_t value)

const

table &

biases

A table for

task::classification

and

task::nu_classification

and a table for

task::regression

and

task::nu_regression

containing constants in decision function.

Getter & Setter

const table & get_biases() const
auto & set_biases(const table &value)

const

table &

coeffs

A table for

task::classification

and

task::nu_classification

and a table for

task::regression

and

task::nu_regression

containing coefficients of Lagrange multiplier.

Default value

: table{}.

Getter & Setter

const table & get_coeffs() const
auto & set_coeffs(const table &value)

const

table &

support_vectors

A table containing support vectors. Where - number of support vectors.

Default value

: table{}.

Getter & Setter

const table & get_support_vectors() const
auto & set_support_vectors(const table &value)

std::int64_t

second_class_label

The second unique value in class labels. Used with

task::classification

and

task::nu_classification

.

Getter & Setter

std::int64_t get_second_class_label() const
template > auto & set_second_class_label(std::int64_t value)

std::int64_t

first_class_response

The first unique value in class responses. Used with

task::classification

and

task::nu_classification

.

Getter & Setter

std::int64_t get_first_class_response() const
template > auto & set_first_class_response(std::int64_t value)

std::int64_t

first_class_label

The first unique value in class labels. Used with

task::classification

and

task::nu_classification

.

Getter & Setter

std::int64_t get_first_class_label() const
template > auto & set_first_class_label(std::int64_t value)

template<typename

Task

= task::by_default>

class

train_input

Template Parameters

Task

– Tag-type that specifies the type of the problem to solve. Can be

oneapi::dal::svm::task::classification

,

oneapi::dal::svm::task::nu_classification

,

oneapi::dal::svm::task::regression

, or

oneapi::dal::svm::task::nu_regression

.

Constructors

train_input

(

const

table &

data

,

const

table &

responses

,

const

table &

weights

= table{})

Creates a new instance of the class with the given

data

,

responses

and

weights

.

Properties

const

table &

weights

The vector of weights for the training set .

Default value

: table{}.

Getter & Setter

const table & get_weights() const
auto & set_weights(const table &value)

const

table &

responses

The vector of responses for the training set .

Default value

: table{}.

Getter & Setter

const table & get_responses() const
auto & set_responses(const table &value)

const

table &

labels

The vector of labels for the training set .

Default value

: table{}.

Getter & Setter

const table & get_labels() const
auto & set_labels(const table &value)

const

table &

data

The training set .

Default value

: table{}.

Getter & Setter

const table & get_data() const
auto & set_data(const table &value)

template<typename

Task

= task::by_default>

class

train_result

Template Parameters

Task

– Tag-type that specifies the type of the problem to solve. Can be

oneapi::dal::svm::task::classification

,

oneapi::dal::svm::task::nu_classification

,

oneapi::dal::svm::task::regression

, or

oneapi::dal::svm::task::nu_regression

.

Constructors

train_result

()

Creates a new instance of the class with the default property values.

Public Methods

std::int64_t

get_support_vector_count

()

const

The number of support vectors.

Properties

double

bias

The bias.

Default value

: 0.0.

Getter & Setter

double get_bias() const
auto & set_bias(double value)

const

table &

biases

A table for

task::classification

and

task::classification

and table for

task::regression

and

task::nu_regression

containing constants in decision function.

Getter & Setter

const table & get_biases() const
auto & set_biases(const table &value)

const

table &

coeffs

A table for

task::classification

and

task::classification

and table for

task::regression

and

task::nu_regression

containing coefficients of Lagrange multiplier.

Default value

: table{}.

Getter & Setter

const table & get_coeffs() const
auto & set_coeffs(const table &value)

const

table &

support_vectors

A table containing support vectors, where is the number of support vectors.

Default value

: table{}.

Getter & Setter

const table & get_support_vectors() const
auto & set_support_vectors(const table &value)

const

model<Task> &

model

The trained SVM model.

Default value

: model<Task>{}.

Getter & Setter

const model< Task > & get_model() const
auto & set_model(const model< Task > &value)

const

table &

support_indices

A table containing support indices.

Default value

: table{}.

Getter & Setter

const table & get_support_indices() const
auto & set_support_indices(const table &value)

template<typename

Descriptor

> svm::train_result

train

(

const

Descriptor &

desc

,

const

svm::train_input &

input

)

Parameters

desc
– SVM algorithm descriptor
svm::descriptor
.
input
– Input data for the training operation

Preconditions

input.data.is_empty == false
input.labels.is_empty == false
input.labels.column_count == 1
input.data.row_count == input.labels.row_count

template<typename

Task

= task::by_default>

class

infer_input

Template Parameters

Task

– Tag-type that specifies the type of the problem to solve. Can be

oneapi::dal::svm::task::classification

,

oneapi::dal::svm::task::nu_classification

,

oneapi::dal::svm::task::regression

, or

oneapi::dal::svm::task::nu_regression

.

Constructors

infer_input

(

const

model<Task> &

trained_model

,

const

table &

data

)

Creates a new instance of the class with the given

model

and

data

property values.

Properties

const

model<Task> &

model

The trained SVM model.

Default value

: model<Task>{}.

Getter & Setter

const model< Task > & get_model() const
auto & set_model(const model< Task > &value)

const

table &

data

The dataset for inference .

Default value

: table{}.

Getter & Setter

const table & get_data() const
auto & set_data(const table &value)

template<typename

Task

= task::by_default>

class

infer_result

Template Parameters

Task

– Tag-type that specifies the type of the problem to solve. Can be

oneapi::dal::svm::task::classification

,

oneapi::dal::svm::task::nu_classification

,

oneapi::dal::svm::task::regression

, or

oneapi::dal::svm::task::nu_regression

.

Constructors

infer_result

()

Creates a new instance of the class with the default property values.

Properties

const

table &

decision_function

The table with the predicted class. Used with

oneapi::dal::svm::task::classification

and

oneapi::dal::svm::task::nu_classification

. decision function for each observation.

Default value

: table{}.

Getter & Setter

const table & get_decision_function() const
template > auto & set_decision_function(const table &value)

const

table &

labels

The table with the predicted labels.

Default value

: table{}.

Getter & Setter

const table & get_labels() const
auto & set_labels(const table &value)

const

table &

responses

The table with the predicted responses.

Default value

: table{}.

Getter & Setter

const table & get_responses() const
auto & set_responses(const table &value)

template<typename

Descriptor

> svm::infer_result

infer

(

const

Descriptor &

desc

,

const

svm::infer_input &

input

)

Parameters

desc
– SVM algorithm descriptor
svm::descriptor
.
input
– Input data for the inference operation

Preconditions

input.data.is_empty == false

Examples

svm_two_class_thunder_dense_batch.cpp

svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
svm_reg_thunder_dense_batch.cpp
svm_multi_class_thunder_dense_batch.cpp
svm_nu_cls_thunder_dense_batch.cpp
svm_nu_reg_thunder_dense_batch.cpp

svm_batch.py