.. ****************************************************************************** .. * Copyright 2020-2021 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. .. *******************************************************************************/ .. _moments_batch: Batch Processing ================ Algorithm Input *************** The low order moments algorithm accepts the input described below. Pass the ``Input ID`` as a parameter to the methods that provide input for your algorithm. For more details, see :ref:`algorithms`. .. tabularcolumns:: |\Y{0.2}|\Y{0.8}| .. list-table:: Algorithm Input for Low Order Moments (Batch Processing) :widths: 10 60 :header-rows: 1 * - Input ID - Input * - ``data`` - Pointer to the numeric table of size :math:`n \times p` to compute moments for. While the input for ``defaultDense``, ``singlePassDense``, or ``sumDense`` method can be an object of any class derived from ``NumericTable``, the input for ``fastCSR``, ``singlePassCSR``, or ``sumCSR`` method can only be an object of the ``CSRNumericTable`` class. Algorithm Parameters ******************** The low order moments algorithm has the following parameters: .. tabularcolumns:: |\Y{0.15}|\Y{0.15}|\Y{0.7}| .. list-table:: Algorithm Parameters for Low Order Moments (Batch Processing) :widths: 10 10 60 :header-rows: 1 :class: longtable * - Parameter - Default Valude - Description * - ``algorithmFPType`` - ``float`` - The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``. * - ``method`` - ``defaultDense`` - Available methods for computation of low order moments: For CPU: - ``defaultDense`` - default performance-oriented method - ``singlePassDense`` - implementation of the single-pass algorithm proposed by D.H.D. West - ``sumDense`` - implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; returns an error if pre-computed sums are not defined - ``fastCSR`` - performance-oriented method for CSR numeric tables - ``singlePassCSR`` - implementation of the single-pass algorithm proposed by D.H.D. West; optimized for CSR numeric tables - ``sumCSR`` - implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; optimized for CSR numeric tables; returns an error if pre-computed sums are not defined For GPU: - ``defaultDense`` - default performance-oriented method * - ``estimatesToCompute`` - ``estimatesAll`` - Estimates to be computed by the algorithm: - ``estimatesAll`` - all supported moments - ``estimatesMinMax`` - minimum and maximum - ``estimatesMeanVariance`` - mean and variance Algorithm Output **************** The low order moments algorithm calculates the results described in the following table. Pass the ``Result ID`` as a parameter to the methods that access the results of your algorithm. For more details, see :ref:`algorithms`. .. note:: Each result is a pointer to the :math:`1 \times p` numeric table that contains characteristics for each feature in the data set. By default, the tables are objects of the ``HomogenNumericTable`` class, but you can define each table as an object of any class derived from ``NumericTable`` except ``PackedSymmetricMatrix``, ``PackedTriangularMatrix``, and ``CSRNumericTable``. .. tabularcolumns:: |\Y{0.2}|\Y{0.8}| .. list-table:: Algorithm Output for Low Order Moments (Batch Processing) :widths: 10 60 :header-rows: 1 :class: longtable * - Result ID - Characteristic * - ``minimum`` - Minimums * - ``maximum`` - Maximums * - ``sum`` - Sums * - ``sumSquares`` - Sums of squares * - ``sumSquaresCentered`` - Sums of squared differences from the means * - ``mean`` - Estimates for the means * - ``secondOrderRawMoment`` - Estimates for the second order raw moments * - ``variance`` - Estimates for the variances * - ``standardDeviation`` - Estimates for the standard deviations * - ``variation`` - Estimates for the variations .. include:: ../../../opt-notice.rst