Intel® oneAPI Data Analytics Library Developer Guide and Reference

Developer Guide and Reference

  • 2021.4
  • 09/27/2021
  • Public Content
Contents

Covariance

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Covariance algorithm computes the following set of quantitative dataset characteristics:
  • means
  • covariance
  • correlation
Operation
Computational methods
Programming Interface

Mathematical formulation

Programming Interface

All types and functions in this section are declared in the
oneapi::dal::covariance
 namespace and are available via inclusion of the
oneapi/dal/algo/covariance.hpp
 header file.
Descriptor
template<typename
Float
 = float, typename
Method
 = method::by_default, typename
Task
 = task::by_default>
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::dense
    .
  • Task
     – Tag-type that specifies the type of the problem to solve. Can be
    task::compute
    .
Constructors
descriptor
() = default
Creates a new instance of the class with the default property values.
Properties
result_option_id
result_options
Choose which results should be computed and returned.
Getter & Setter


result_option_id get_result_options() const
auto & set_result_options(const result_option_id &value)

Method tags
struct
dense
Tag-type that denotes dense computational method.
using
by_default
 = dense
Alias tag-type for the dense computational method.
Task tags
struct
compute
Tag-type that parameterizes entities that are used to compute statistics.
using
by_default
 = compute
Alias tag-type for the compute task.
Training
compute(...)
Input
template<typename
Task
 = task::by_default>
class
compute_input
Template Parameters
Task
 – Tag-type that specifies the type of the problem to solve. Can be
task::compute
.
Constructors
compute_input
(
const
 table &
data
)
Creates a new instance of the class with the given
data
 property value.
Properties
const
 table &
data
An LaTex Math image. table with the training data, where each row stores one feature vector.
Default value
: table{}.
Getter & Setter


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

Result
template<typename
Task
 = task::by_default>
class
compute_result
Template Parameters
Task
 – Tag-type that specifies the type of the problem to solve. Can be
task::compute
.
Constructors
compute_result
()
Creates a new instance of the class with the default property values.
Properties
const
 table &
cov_matrix
The covariance matrix.
Default value
: table{}.
Getter & Setter


const table & get_cov_matrix() const
auto & set_cov_matrix(const table &value)

const
 table &
means
Means.
Default value
: table{}.
Getter & Setter


const table & get_means() const
auto & set_means(const table &value)

const
 result_option_id &
result_options
Result options that indicates availability of the properties.
Default value
: default_result_options<Task>.
Getter & Setter


const result_option_id & get_result_options() const
auto & set_result_options(const result_option_id &value)

const
 table &
cor_matrix
The correlation matrix.
Default value
: table{}.
Getter & Setter


const table & get_cor_matrix() const
auto & set_cor_matrix(const table &value)

Operation
template<typename
Descriptor
> covariance::compute_result
compute
(
const
 Descriptor &
desc
,
const
 covariance::compute_input &
input
)
Parameters
  • desc
     – Covariance algorithm descriptor
    covariance::descriptor
  • input
     – Input data for the computing operation
Preconditions


input.data.is_empty == false

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.