dataset#
The structure consolidates the information of a multi-dimensional dataset.
Description
The dataset struct object is used in Summary Statistics Routines as a multi-dimensional data storage. dataset struct contains information about observations matrix and its size (dimensions x observations), observations weights and indices for dimensions (defines dimensions to be processed).
structure dataset (Buffer version)#
Syntax
namespace oneapi::math::stats {
template<layout ObservationsLayout, typename Type>
    struct dataset<ObservationsLayout, sycl::buffer<Type, 1>> {
    explicit dataset(std::int64_t n_dims_, std::int64_t n_observations_,
                sycl::buffer<Type, 1> observations_, sycl::buffer<Type, 1> weights_ = {0},
                sycl::buffer<std::int64_t, 1> indices_ = {0});
    std::int64_t n_dims;
    std::int64_t n_observations;
    sycl::buffer<Type, 1> observations;
    sycl::buffer<Type, 1> weights = {0};
    sycl::buffer<std::int64_t, 1> indices = {0};
    static constexpr layout layout = ObservationsLayout;
    };
}
Template parameters
- typename Type
 Type of the multi-dimensional data. Supported types:
floatdouble
- oneapi::math::stats::layout ObservationsLayout
 Type of the multi-dimensional data layout. Supported types:
oneapi::math::stats::layout::row_majoroneapi::math::stats::layout::col_major
Struct Members
Routine  | 
Description  | 
|---|---|
Constructor  | 
Constructors
explicit dataset::dataset(std::int64_t n_dims_, std::int64_t n_observations_,
    sycl::buffer<Type, 1> observations_,
    sycl::buffer<Type, 1> weights_ = {0},
    sycl::buffer<std::int64_t, 1> indices_ = {0});
Description
Constructor with parameters.
n_dims_ is the number of dimensions
n_observations_ is the number of observations
observations_ is the matrix of observations
weights_ is an optional parameter, represents an array of weights for observations (of size n_observations). If the parameter is not specified, each observation is assigned a weight equal 1.
indices_ is an optional parameter, represents an array of dimensions that are processed (of size n_dims). If the parameter is not specified, all dimensions are processed.
Throws
- oneapi::math::invalid_argument
 Exception is thrown when n_dims_ \(\leq 0\), or n_observations_ \(\leq 0\), or observations_.get_count() == 0
structure dataset (USM version)#
Syntax
namespace oneapi::math::stats {
template<layout ObservationsLayout, typename Type>
    struct dataset<Type*, ObservationsLayout> {
    explicit dataset(std::int64_t n_dims_, std::int64_t n_observations_, Type* observations_,
                Type* weights_ = nullptr, std::int64_t* indices_ = nullptr);
    std::int64_t n_dims;
    std::int64_t n_observations;
    Type* observations;
    Type* weights = nullptr;
    std::int64_t* indices = nullptr;
    static constexpr layout layout = ObservationsLayout;
    };
}
Template parameters
- typename Type
 Type of the multi-dimensional data. Supported types:
floatdouble
- oneapi::math::stats::layout ObservationsLayout
 Type of the multi-dimensional data layout. Supported types:
oneapi::math::stats::layout::row_majoroneapi::math::stats::layout::col_major
Struct Members
Routine  | 
Description  | 
|---|---|
Constructor  | 
Constructors
explicit dataset::dataset(std::int64_t n_dims_, std::int64_t n_observations_,
    Type* observations_,
    Type* weights_ = nullptr,
    std::int64_t* indices_ = nullptr);
Description
Constructor with parameters.
n_dims_ is the number of dimensions
n_observations_ is the number of observations
observations_ is the matrix of observations
weights_ is an optional parameter, represents an array of weights for observations (of size n_observations). If the parameter is not specified, each observation is assigned a weight equal 1.
indices_ is an optional parameter, represents an array of dimensions that are processed (of size n_dims). If the parameter is not specified, all dimensions are processed.
Throws
- oneapi::math::invalid_argument
 Exception is thrown when n_dims_ \(\leq 0\), or n_observations_ \(\leq 0\), or observations_ == nullptr
Parent topic: Summary Statistics