Covariance
- means
- covariance
- correlation
Operation | Computational methods | Programming Interface |
Mathematical formulation
Programming Interface
- 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- 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)
- structdense
- Tag-type that denotes dense computational method.
- Alias tag-type for the dense computational method.
- structcompute
- Tag-type that parameterizes entities that are used to compute statistics.
- Alias tag-type for the compute task.
- Template Parameters
- Task– Tag-type that specifies the type of the problem to solve. Can betask::compute.
Constructors- Creates a new instance of the class with the givendataproperty value.
Properties- 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)
- Template Parameters
- Task– Tag-type that specifies the type of the problem to solve. Can betask::compute.
Constructors- compute_result()
- Creates a new instance of the class with the default property values.
Properties- The correlation matrix.Default value: table{}.
- Getter & Setter
const table & get_cor_matrix() const
auto & set_cor_matrix(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)
- Means.Default value: table{}.
- Getter & Setter
const table & get_means() const
auto & set_means(const table &value)
- The covariance matrix.Default value: table{}.
- Getter & Setter
const table & get_cov_matrix() const
auto & set_cov_matrix(const table &value)
- template<typenameDescriptor> covariance::compute_resultcompute(constDescriptor &desc,constcovariance::compute_input &input)
- Parameters
- desc– Covariance algorithm descriptorcovariance::descriptor
- input– Input data for the computing operation
- Preconditions
input.data.is_empty == false