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.. _moments_batch:
Batch Processing
================
- `Algorithm Input`_
- `Algorithm Parameters`_
- `Algorithm Output`_
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`.
.. list-table::
: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:
.. list-table::
:widths: 10 10 60
:header-rows: 1
* - 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``.
.. list-table::
:widths: 10 60
:header-rows: 1
* - 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