Visible to Intel only — GUID: GUID-CF824BDE-5E66-47F3-946A-C20EEC8BE12C
basic_statistics_dense_batch.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cov_dense_batch.cpp
dbscan_brute_force_batch.cpp
df_cls_hist_batch.cpp
df_cls_hist_batch_random.cpp
df_cls_traverse_model.cpp
df_reg_hist_batch.cpp
df_reg_hist_batch_random.cpp
df_reg_traverse_model.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_reg_brute_force_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
pca_cor_dense_batch.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
basic_statistics_dense_batch.cpp
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cov_dense_batch.cpp
csr_accessor.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
jaccard_batch.cpp
jaccard_batch_app.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_cls_kd_tree_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
logloss_dense_batch.cpp
louvain_batch.cpp
pca_dense_batch.cpp
pca_precomputed_dense_batch.cpp
polynomial_kernel_dense_batch.cpp
rbf_kernel_dense_batch.cpp
shortest_paths_batch.cpp
sigmoid_kernel_dense_batch.cpp
subgraph_isomorphism_batch.cpp
svm_multi_class_thunder_csr_batch.cpp
svm_multi_class_thunder_dense_batch.cpp
svm_nu_cls_thunder_csr_batch.cpp
svm_nu_cls_thunder_dense_batch.cpp
svm_nu_reg_thunder_csr_batch.cpp
svm_nu_reg_thunder_dense_batch.cpp
svm_reg_thunder_csr_batch.cpp
svm_reg_thunder_dense_batch.cpp
svm_two_class_smo_csr_batch.cpp
svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_csr_batch.cpp
svm_two_class_thunder_dense_batch.cpp
triangle_counting_batch.cpp
Mathematical Notations
Mathematical Notations
Correlation and Variance-Covariance Matrices
Mathematical Notations
Principal Components Analysis Transform
Singular Value Decomposition
Association Rules
Mathematical Notations
Mathematical Notations
Cholesky Decomposition
QR Decomposition
Outlier Detection
Distance Matrix
Distributions
Engines
Moments of Low Order
Mathematical Notations
Quality Metrics
Sorting
Normalization
Mathematical Notations
Visible to Intel only — GUID: GUID-CF824BDE-5E66-47F3-946A-C20EEC8BE12C
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<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.
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(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 \(n \times p\) 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)
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
consttable&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)
consttable&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)
consttable&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)
consttable&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)
consttable&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)
consttable&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)
consttable&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)
consttable&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)
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&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)
consttable&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)
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
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
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing: