Visible to Intel only — GUID: GUID-140E4E83-F7E9-41D5-9255-61F22129A4F6
basic_statistics_dense_batch.cpp
basic_statistics_dense_online.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cor_dense_online.cpp
cov_dense_batch.cpp
cov_dense_biased_batch.cpp
cov_dense_biased_online.cpp
cov_dense_online.cpp
csr_accessor.cpp
csr_table.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
heterogen_table.cpp
homogen_table.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
linear_regression_dense_online.cpp
logistic_regression_dense_batch.cpp
pca_cor_dense_batch.cpp
pca_cor_dense_online.cpp
pca_cov_dense_batch.cpp
pca_cov_dense_online.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
pca_svd_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
basic_statistics_dense_batch.cpp
basic_statistics_dense_online.cpp
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cor_dense_online.cpp
cov_dense_batch.cpp
cov_dense_biased_batch.cpp
cov_dense_biased_online.cpp
cov_dense_online.cpp
csr_accessor.cpp
csr_table.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
heterogen_table.cpp
homogen_table.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
linear_regression_dense_online.cpp
logloss_dense_batch.cpp
louvain_batch.cpp
pca_cor_dense_batch.cpp
pca_cor_dense_online.cpp
pca_cov_dense_batch.cpp
pca_cov_dense_online.cpp
pca_precomputed_dense_batch.cpp
pca_svd_dense_batch.cpp
pca_svd_dense_online.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
K-Means Clustering
Density-Based Spatial Clustering of Applications with Noise
Correlation and Variance-Covariance Matrices
Principal Component Analysis
Principal Components Analysis Transform
Singular Value Decomposition
Association Rules
Kernel Functions
Expectation-Maximization
Cholesky Decomposition
QR Decomposition
Outlier Detection
Distance Matrix
Distributions
Engines
Moments of Low Order
Quantile
Quality Metrics
Sorting
Normalization
Optimization Solvers
Decision Forest
Decision Trees
Gradient Boosted Trees
Stump
Linear and Ridge Regressions
LASSO and Elastic Net Regressions
k-Nearest Neighbors (kNN) Classifier
Implicit Alternating Least Squares
Logistic Regression
Naïve Bayes Classifier
Support Vector Machine Classifier
Multi-class Classifier
Boosting
Visible to Intel only — GUID: GUID-140E4E83-F7E9-41D5-9255-61F22129A4F6
Covariance
In statistics, covariance and correlation are two of the most fundamental measures of linear dependence between two random variables. The covariance and the correlation represent the joint variability of any two features. The correlation is dimensionless, while the covariance is measured in units obtained by multiplying the units of the two features. Another important distinction is that covariance can be affected by the higher variance of one feature, while correlation removes the effect of the variances by normalizing the covariance of two features by their square-root of variances. Their usage is application-dependent. The covariance algorithm computes the following:
Means
Covariance (sample and estimated by maximum likelihood method)
Correlation
Operation |
Computational methods |
Programming Interface |
||
Mathematical formulation
Refer to Developer Guide: Covariance.
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<typenameFloat=float,typenameMethod=method::by_default,typenameTask=task::by_default>classdescriptor
- Template Parameters
Constructors
descriptor()=default
Creates a new instance of the class with the default property values.
Properties
boolbias
Choose if result biased or not.
- Getter & Setter
-
bool get_bias() const
auto & set_bias(const bool &value)
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 the 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.
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 &value)
Result and Finalize 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&cor_matrix
The correlation matrix. Default value: table{}.
- Getter & Setter
-
const table & get_cor_matrix() const
auto & set_cor_matrix(const table &value)
consttable&cov_matrix
The covariance matrix. Default value: table{}.
- Getter & Setter
-
const table & get_cov_matrix() const
auto & set_cov_matrix(const table &value)
consttable&means
Means. Default value: table{}.
- Getter & Setter
-
const table & get_means() const
auto & set_means(const table &value)
constresult_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)
Operation
template<typenameDescriptor>covariance::compute_resultcompute(constDescriptor&desc, constcovariance::compute_input&input)
- Parameters
-
desc – Covariance algorithm descriptor covariance::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)
Properties
consttable&data
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &value)
constpartial_compute_result<Task>&prev
- Getter & Setter
-
const partial_compute_result< Task > & get_prev() const
auto & set_prev(const partial_compute_result< Task > &value)
Partial Result and Finalize Input
template<typenameTask=task::by_default>classpartial_compute_result
Constructors
partial_compute_result()
Properties
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_crossproduct
The crossproduct matrix. Default value: table{}.
- Getter & Setter
-
const table & get_partial_crossproduct() const
auto & set_partial_crossproduct(const table &value)
consttable&partial_sum
Sums. Default value: table{}.
- Getter & Setter
-
const table & get_partial_sum() const
auto & set_partial_sum(const table &value)
Finalize Training
Usage Example
Computing
void run_computing(const table& data) {
const auto cov_desc = dal::covariance::descriptor{};
const auto cov_desc = dal::covariance::descriptor{}.set_result_options(
dal::covariance::result_options::cor_matrix | dal::covariance::result_options::means);
const auto result = dal::compute(cov_desc, data);
std::cout << "Means:\n" << result.get_means() << std::endl;
std::cout << "Correlation:\n" << result.get_cor_matrix() << std::endl;
}