Visible to Intel only — GUID: GUID-355DF53A-C29D-4AAA-86C1-82D699705237
Visible to Intel only — GUID: GUID-355DF53A-C29D-4AAA-86C1-82D699705237
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 |
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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<typenameFloat=detail::descriptor_base<>::float_t,typenameMethod=detail::descriptor_base<>::method_t,typenameTask=detail::descriptor_base<>::task_t>classdescriptor
- Template Parameters
Properties
result_option_idresult_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
structdense
Tag-type that denotes dense computational method.
structsparse
Tag-type that denotes sparse computational method.
usingby_default=dense
Alias tag-type for dense computational method.
Task tags
structcompute
Tag-type that parameterizes entities that are used to compute statistics.
usingby_default=compute
Alias tag-type for the compute task.
Training compute(...)
Input
template<typenameTask=task::by_default>classcompute_input
- Template Parameters
-
Task – Tag-type that specifies the type of the problem to solve. Can be task::compute.
Constructors
compute_input()
compute_input(consttable&data)
Creates a new instance of the class with the given data property value.
compute_input(consttable&data, consttable&weights)
Properties
consttable&data
An 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)
consttable&weights
- Getter & Setter
-
const table & get_weights() const
auto & set_weights(const table &weights)
Result
template<typenameTask=task::by_default>classcompute_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
constresult_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)
consttable&min
A table, where element is the minimum result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_min() const
auto & set_min(const table &value)
consttable&standard_deviation
A table, where element is the standard_deviation result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_standard_deviation() const
auto & set_standard_deviation(const table &value)
consttable&mean
A table, where element is the mean result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_mean() const
auto & set_mean(const table &value)
consttable&variance
A table, where element is the variance result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_variance() const
auto & set_variance(const table &value)
consttable&sum_squares_centered
A table, where element is the sum_squares_centered result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_sum_squares_centered() const
auto & set_sum_squares_centered(const table &value)
consttable&variation
A table, where element is the variation result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_variation() const
auto & set_variation(const table &value)
consttable&sum
A table, where element is the sum result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_sum() const
auto & set_sum(const table &value)
consttable&max
A table, where element is the maximum result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_max() const
auto & set_max(const table &value)
consttable&sum_squares
A table, where element is the sum_squares result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_sum_squares() const
auto & set_sum_squares(const table &value)
consttable&second_order_raw_moment
A table, where element is the second_order_raw_moment result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_second_order_raw_moment() const
auto & set_second_order_raw_moment(const table &value)
Operation
template<typenameDescriptor>basic_statistics::compute_resultcompute(constDescriptor&desc, constbasic_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
Partial Training
Partial Input
template<typenameTask=task::by_default>classpartial_compute_input
Constructors
partial_compute_input()
partial_compute_input(consttable&data)
partial_compute_input(constpartial_compute_result<Task>&prev, consttable&data)
partial_compute_input(constpartial_compute_result<Task>&prev, consttable&data, consttable&weights)
Properties
constpartial_compute_result<Task>&prev
- Getter & Setter
-
const partial_compute_result< Task > & get_prev() const
auto & set_prev(const partial_compute_result< Task > &value)
consttable&data
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &value)
consttable&weights
- Getter & Setter
-
const table & get_weights() const
auto & set_weights(const table &value)
Partial Result and Finalize Input
template<typenameTask=task::by_default>classpartial_compute_result
Constructors
partial_compute_result()
Properties
consttable&partial_min
A table, where element is the minimum current result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_partial_min() const
auto & set_partial_min(const table &value)
consttable&partial_sum_squares_centered
A table, where element is the sum_squares_centered result of current blocks for feature . Default value: table{}.
- Getter & Setter
-
const table & get_partial_sum_squares_centered() const
auto & set_partial_sum_squares_centered(const table &value)
consttable&partial_n_rows
The nobs value. Default value: table{}.
- Getter & Setter
-
const table & get_partial_n_rows() const
auto & set_partial_n_rows(const table &value)
consttable&partial_max
A table, where element is the maximum current result for feature . Default value: table{}.
- Getter & Setter
-
const table & get_partial_max() const
auto & set_partial_max(const table &value)
consttable&partial_sum_squares
A table, where element is the sum_squares result of current blocks for feature . Default value: table{}.
- Getter & Setter
-
const table & get_partial_sum_squares() const
auto & set_partial_sum_squares(const table &value)
consttable&partial_sum
A table, where element is the sum result of current blocks for feature . Default value: table{}.
- Getter & Setter
-
const table & get_partial_sum() const
auto & set_partial_sum(const table &value)
Finalize Training