Contents

Basic Statistics

Basic statistics algorithm computes the following set of quantitative dataset characteristics:

  • minimums/maximums

  • sums

  • means

  • sums of squares

  • sums of squared differences from the means

  • second order raw moments

  • variances

  • standard deviations

  • variations

Operation

Computational methods

Programming Interface

dense

dense

compute(…)

compute_input

compute_result

Mathematical formulation

Refer to Developer Guide: Basic statistics.

Programming Interface

All types and functions in this section are declared in the oneapi::dal::basic_statistics namespace and are available via inclusion of the oneapi/dal/algo/basic_statistics.hpp header file.

Descriptor

template<typename Float = detail::descriptor_base<>::float_t, typename Method = detail::descriptor_base<>::method_t, typename Task = detail::descriptor_base<>::task_t>
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.

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 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 \(n \times p\) 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 &data)

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 &min

A \(1 \times p\) table, where element \(j\) is the minimum result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_min() const
auto & set_min(const table &value)
const table &mean

A \(1 \times p\) table, where element \(j\) is the mean result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_mean() const
auto & set_mean(const table &value)
const table &sum_squares_centered

A \(1 \times p\) table, where element \(j\) is the sum_squares_centered result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_sum_squares_centered() const
auto & set_sum_squares_centered(const table &value)
const table &sum

A \(1 \times p\) table, where element \(j\) is the sum result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_sum() const
auto & set_sum(const table &value)
const table &sum_squares

A \(1 \times p\) table, where element \(j\) is the sum_squares result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_sum_squares() const
auto & set_sum_squares(const table &value)
const table &variation

A \(1 \times p\) table, where element \(j\) is the variation result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_variation() const
auto & set_variation(const table &value)
const table &variance

A \(1 \times p\) table, where element \(j\) is the variance result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_variance() const
auto & set_variance(const table &value)
const result_option_id &result_options

Result options that indicates availability of the properties. Default value: full set of.

Getter & Setter
const result_option_id & get_result_options() const
auto & set_result_options(const result_option_id &value)
const table &second_order_raw_moment

A \(1 \times p\) table, where element \(j\) is the second_order_raw_moment result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_second_order_raw_moment() const
auto & set_second_order_raw_moment(const table &value)
const table &max

A \(1 \times p\) table, where element \(j\) is the maximum result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_max() const
auto & set_max(const table &value)
const table &standard_deviation

A \(1 \times p\) table, where element \(j\) is the standard_deviation result for feature \(j\). Default value: table{}.

Getter & Setter
const table & get_standard_deviation() const
auto & set_standard_deviation(const table &value)

Operation

template<typename Descriptor>
basic_statistics::compute_result compute(const Descriptor &desc, const basic_statistics::compute_input &input)
Parameters

  • desc – Basic statistics algorithm descriptor basic_statistics::descriptor

  • input – Input data for the computing operation

Preconditions
input.data.is_empty == false

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