.. ****************************************************************************** .. * Copyright 2020 Intel Corporation .. * .. * Licensed under the Apache License, Version 2.0 (the "License"); .. * you may not use this file except in compliance with the License. .. * You may obtain a copy of the License at .. * .. * http://www.apache.org/licenses/LICENSE-2.0 .. * .. * Unless required by applicable law or agreed to in writing, software .. * distributed under the License is distributed on an "AS IS" BASIS, .. * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. .. * See the License for the specific language governing permissions and .. * limitations under the License. .. *******************************************************************************/ Batch and Online Processing =========================== Online processing computation mode assumes that the data arrives in blocks :math:`i = 1, 2, 3, \ldots \text{nblocks}`. Algorithm Input *************** The SVD algorithm accepts the input described below. Pass the ``Input ID`` as a parameter to the methods that provide input for your algorithm. .. tabularcolumns:: |\Y{0.2}|\Y{0.8}| .. list-table:: Algorithm Input for Singular Value Decomposition (Batch and Online Processing) :header-rows: 1 :align: left :widths: 10 60 * - Input ID - Input * - ``data`` - Pointer to the numeric table that represents: - For batch processing, the entire :math:`n \times p` matrix :math:`X` to be factorized. - For online processing, the :math:`n_i \times p` submatrix of :math:`X` that represents the current data block in the online processing mode. The input can be an object of any class derived from ``NumericTable``. Algorithm Parameters ******************** The SVD algorithm has the following parameters: .. tabularcolumns:: |\Y{0.2}|\Y{0.2}|\Y{0.6}| .. list-table:: Algorithm Parameters for Singular Value Decomposition (Batch and Online Processing) :header-rows: 1 :align: left :widths: 10 20 30 :class: longtable * - Parameter - Default Value - Description * - ``algorithmFPType`` - ``float`` - The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``. * - ``method`` - ``defaultDense`` - Performance-oriented computation method, the only method supported by the algorithm. * - ``leftSingularMatrix`` - ``requiredInPackedForm`` - Specifies whether the matrix of left singular vectors is required. Can be: - ``notRequired`` - the matrix is not required - ``requiredInPackedForm`` - the matrix in the packed format is required * - ``rightSingularMatrix`` - ``requiredInPackedForm`` - Specifies whether the matrix of left singular vectors is required. Can be: - ``notRequired`` - the matrix is not required - ``requiredInPackedForm`` - the matrix in the packed format is required Algorithm Output **************** The SVD algorithm calculates the results described below. Pass the ``Result ID`` as a parameter to the methods that access the results of your algorithm. .. tabularcolumns:: |\Y{0.2}|\Y{0.8}| .. list-table:: Algorithm Output for Singular Value Decomposition (Batch and Online Processing) :header-rows: 1 :align: left :widths: 10 60 :class: longtable * - Result ID - Result * - ``singularValues`` - Pointer to the :math:`1 \times p` numeric table with singular values (the diagonal of the matrix :math:`\Sigma`). * - ``leftSingularMatrix`` - Pointer to the :math:`n \times p` numeric table with left singular vectors (matrix :math:`U`). Pass ``NULL`` if left singular vectors are not required. * - ``rightSingularMatrix`` - Pointer to the :math:`p \times p` numeric table with right singular vectors (matrix :math:`V`). Pass ``NULL`` if right singular vectors are not required. .. note:: By default, these results are objects of the ``HomogenNumericTable`` class, but you can define the result as an object of any class derived from ``NumericTable`` except ``PackedSymmetricMatrix``, ``PackedTriangularMatrix``, and ``CSRNumericTable``.