Normal Bayes Classifier
=======================

.. highlight:: cpp

This is a simple classification model assuming that feature vectors from each class are normally distributed (though, not necessarily independently distributed), so the whole data distribution function is assumed to be a Gaussian mixture, one component per  class. Using the training data the algorithm estimates mean vectors and covariance matrices for every class, and then it uses them for prediction.

**[Fukunaga90] K. Fukunaga. Introduction to Statistical Pattern Recognition. second ed., New York: Academic Press, 1990.**

.. index:: CvNormalBayesClassifier

CvNormalBayesClassifier
-----------------------
.. c:type:: CvNormalBayesClassifier

Bayes classifier for normally distributed data. ::

    class CvNormalBayesClassifier : public CvStatModel
    {
    public:
        CvNormalBayesClassifier();
        virtual ~CvNormalBayesClassifier();

        CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
            const CvMat* _var_idx=0, const CvMat* _sample_idx=0 );

        virtual bool train( const CvMat* _train_data, const CvMat* _responses,
            const CvMat* _var_idx = 0, const CvMat* _sample_idx=0, bool update=false );

        virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
        virtual void clear();

        virtual void save( const char* filename, const char* name=0 );
        virtual void load( const char* filename, const char* name=0 );

        virtual void write( CvFileStorage* storage, const char* name );
        virtual void read( CvFileStorage* storage, CvFileNode* node );
    protected:
        ...
    };


.. index:: CvNormalBayesClassifier::train

.. _CvNormalBayesClassifier::train:

CvNormalBayesClassifier::train
------------------------------
.. c:function:: bool CvNormalBayesClassifier::train(  const CvMat* _train_data,  const CvMat* _responses,                 const CvMat* _var_idx =0,  const CvMat* _sample_idx=0,  bool update=false )

    Trains the model.

The method trains the Normal Bayes classifier. It follows the conventions of the generic ``train`` "method" with the following limitations: only CV_ROW_SAMPLE data layout is supported; the input variables are all ordered; the output variable is categorical (i.e. elements of ``_responses`` must be integer numbers, though the vector may have ``CV_32FC1`` type), and missing measurements are not supported.

In addition, there is an ``update`` flag that identifies whether the model should be trained from scratch ( ``update=false`` ) or should be updated using the new training data ( ``update=true`` ).

.. index:: CvNormalBayesClassifier::predict

.. _CvNormalBayesClassifier::predict:

CvNormalBayesClassifier::predict
--------------------------------
.. c:function:: float CvNormalBayesClassifier::predict(  const CvMat* samples,  CvMat* results=0 ) const

    Predicts the response for sample(s)

The method ``predict`` estimates the most probable classes for the input vectors. The input vectors (one or more) are stored as rows of the matrix ``samples`` . In the case of multiple input vectors, there should be one output vector ``results`` . The predicted class for a single input vector is returned by the method.