Normal Bayes Classifier

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.



Bayes classifier for normally distributed data.

class CvNormalBayesClassifier : public CvStatModel
    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 );


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 ).


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.

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