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 |
||
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:
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)
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()#
-
compute_input(const table &data)#
Creates a new instance of the class with the given
data
property value.
Properties
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 &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 &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 &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 &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 &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 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)
-
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 &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 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)
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
Usage Example#
Computing#
void run_computing(const table& data) {
const auto bs_desc = dal::basic_statistics::descriptor{};
const auto result = dal::compute(bs_desc, data);
std::cout << "Minimum:\n" << result.get_min() << std::endl;
std::cout << "Maximum:\n" << result.get_max() << std::endl;
std::cout << "Sum:\n" << result.get_sum() << std::endl;
std::cout << "Sum of squares:\n" << result.get_sum_squares() << std::endl;
std::cout << "Sum of squared difference from the means:\n"
<< result.get_sum_squares_centered() << std::endl;
std::cout << "Mean:\n" << result.get_mean() << std::endl;
std::cout << "Second order raw moment:\n" << result.get_second_order_raw_moment() << std::endl;
std::cout << "Variance:\n" << result.get_variance() << std::endl;
std::cout << "Standard deviation:\n" << result.get_standard_deviation() << std::endl;
std::cout << "Variation:\n" << result.get_variation() << std::endl;
}
Examples#
Batch Processing:
Batch Processing: