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Online Processing
*****************

You can use the Naïve Bayes classifier algorithm in the online processing mode only at the training stage.

This computation mode assumes that the data arrives in blocks :math:`i = 1, 2, 3, \ldots, \text{nblocks}`.

Training
--------

Naïve Bayes classifier training in the online processing mode follows the general workflow described in :ref:`classification_usage_model`.

Naïve Bayes classifier in the online processing mode 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:: Training Input for Naïve Bayes Classifier (Online Processing)
   :widths: 10 60
   :header-rows: 1
   :class: longtable

   * - Input ID
     - Input
   * - ``data``
     - Pointer to the :math:`n_i \times p` numeric table that represents the current data block.
   * - ``labels``
     - Pointer to the :math:`n_i \times 1` numeric table with class labels associated with the current data block.

.. note:: These tables can be objects of any class derived from ``NumericTable``.

Naïve Bayes classifier in the online processing mode has the following parameters:

.. tabularcolumns::  |\Y{0.15}|\Y{0.15}|\Y{0.7}|

.. list-table:: Training Parameters for Naïve Bayes Classifier (Online Processing)
   :widths: 10 10 60
   :header-rows: 1
   :align: left
   :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``
     - Available computation methods for the Naïve Bayes classifier:

       - ``defaultDense`` - default performance-oriented method
       - ``fastCSR`` - performance-oriented method for CSR numeric tables

   * - ``nClasses``
     - Not applicable
     - The number of classes. A required parameter.
   * - ``priorClassEstimates``
     - :math:`1/\text{nClasses}`
     - Vector of size ``nClasses`` that contains prior class estimates. The default value applies to each vector element.
   * - ``alpha``
     - :math:`1`
     - Vector of size :math:`p` that contains the imagined occurrences of features. The default value applies to each vector element.

For a description of the output, refer to :ref:`classification_usage_model`.