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Online Processing
=================
Online processing computation mode assumes that data arrives in blocks :math:`i = 1, 2, 3, \ldots \text{nblocks}`.
Computation of low order moments in the online processing mode follows the general computation schema
for online processing described in :ref:`algorithms`.
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 (Online Processing)
:widths: 10 60
:header-rows: 1
* - Input ID
- Input
* - ``data``
- Pointer to the numeric table of size :math:`n_i \times p` that represents the current data block.
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 (Online 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:
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
* - ``initializationProcedure``
- Not applicable
- The procedure for setting initial parameters of the algorithm in the online processing mode.
By default, the algorithm does the following initialization:
- Sets ``nObservations``, ``partialSum``, and ``partialSumSquares`` to zero.
- Sets ``partialMinimum`` and ``partialMaximum`` to the first row of the input table.
* - ``estimatesToCompute``
- ``estimatesAll``
- Estimates to be computed by the algorithm:
- ``estimatesAll`` - all supported moments
- ``estimatesMinMax`` - minimum and maximum
- ``estimatesMeanVariance`` - mean and variance
Partial Results
***************
The low order moments algorithm in the online processing mode calculates partial results described below.
Pass the ``Result ID`` as a parameter to the methods that access the results of your algorithm.
For more details, see :ref:`algorithms`.
.. tabularcolumns:: |\Y{0.2}|\Y{0.8}|
.. list-table:: Partial Results for Low Order Moments (Online Processing)
:widths: 10 60
:header-rows: 1
* - Result ID
- Result
* - ``nObservations``
- Pointer to the :math:`1 \times 1` numeric table that contains the number of rows processed so far.
By default, this result is an object of the ``HomogenNumericTable`` class, but you can define the result
as an object of any class derived from ``NumericTable`` except ``CSRNumericTable``.
Partial characteristics computed so far, each in a :math:`1 \times p` numeric table.
By default, each table is an object of the ``HomogenNumericTable`` class, but you can define the tables as objects
of any class derived from ``NumericTable`` except ``PackedSymmetricMatrix``, ``PackedTriangularMatrix``, and ``CSRNumericTable``.
.. tabularcolumns:: |\Y{0.2}|\Y{0.8}|
.. list-table:: Partial Characteristics for Low Order Moments (Online Processing)
:widths: 10 60
:header-rows: 1
:class: longtable
* - Result ID
- Result
* - ``partialMinimum``
- Partial minimums
* - ``partialMaximum``
- Partial maximums
* - ``partialSum``
- Partial sums
* - ``partialSumSquares``
- Partial sums of squares
* - ``partialSumSquaresCentered``
- Partial sums of squared differences from the means
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 (Online 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