74 lines
3.2 KiB
ReStructuredText
74 lines
3.2 KiB
ReStructuredText
.. _Bayes Classifier:
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Normal Bayes Classifier
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=======================
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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.
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[Fukunaga90] K. Fukunaga. *Introduction to Statistical Pattern Recognition*. second ed., New York: Academic Press, 1990.
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.. index:: CvNormalBayesClassifier
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CvNormalBayesClassifier
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-----------------------
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.. c:type:: CvNormalBayesClassifier
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Bayes classifier for normally distributed data ::
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class CvNormalBayesClassifier : public CvStatModel
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{
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public:
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CvNormalBayesClassifier();
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virtual ~CvNormalBayesClassifier();
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CvNormalBayesClassifier( const Mat& _train_data, const Mat& _responses,
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const Mat& _var_idx=Mat(), const Mat& _sample_idx=Mat() );
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virtual bool train( const Mat& _train_data, const Mat& _responses,
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const Mat& _var_idx=Mat(), const Mat& _sample_idx=Mat(), bool update=false );
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virtual float predict( const Mat& _samples, Mat* results=0 ) const;
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virtual void clear();
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virtual void save( const char* filename, const char* name=0 );
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virtual void load( const char* filename, const char* name=0 );
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virtual void write( CvFileStorage* storage, const char* name );
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virtual void read( CvFileStorage* storage, CvFileNode* node );
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protected:
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...
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};
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.. index:: CvNormalBayesClassifier::train
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.. _CvNormalBayesClassifier::train:
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CvNormalBayesClassifier::train
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------------------------------
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.. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& _train_data, const Mat& _responses, const Mat& _var_idx =Mat(), const Mat& _sample_idx=Mat(), bool update=false )
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Trains the model.
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The method trains the Normal Bayes classifier. It follows the conventions of the generic ``train`` "method" with the following limitations:
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* Only ``CV_ROW_SAMPLE`` data layout is supported.
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* Input variables are all ordered.
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* Output variable is categorical , which means that elements of ``_responses`` must be integer numbers, though the vector may have the ``CV_32FC1`` type.
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* Missing measurements are not supported.
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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`` ).
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.. index:: CvNormalBayesClassifier::predict
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.. _CvNormalBayesClassifier::predict:
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CvNormalBayesClassifier::predict
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--------------------------------
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.. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const
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Predicts the response for sample(s).
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The method ``predict`` estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``samples`` . In 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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