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73 lines
3.2 KiB
73 lines
3.2 KiB
.. _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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.. cpp: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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.. cpp: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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