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:

CvNormalBayesClassifier
-----------------------

`id=0.525291936189 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/ml/CvNormalBayesClassifier>`__

.. ctype:: 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
------------------------------

`id=0.273092525817 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/ml/CvNormalBayesClassifier%3A%3Atrain>`__




.. cfunction:: 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
--------------------------------

`id=0.580189532088 Comments from the Wiki <http://opencv.willowgarage.com/wiki/documentation/cpp/ml/CvNormalBayesClassifier%3A%3Apredict>`__




.. cfunction:: 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.