.. _Bayes Classifier: Normal Bayes Classifier ======================= .. highlight:: cpp This simple classification model assumes 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. CvNormalBayesClassifier ----------------------- .. ocv:class:: CvNormalBayesClassifier : public CvStatModel Bayes classifier for normally distributed data. CvNormalBayesClassifier::CvNormalBayesClassifier ------------------------------------------------ Default and training constructors. .. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier() .. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() ) .. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 ) .. ocv:pyfunction:: cv2.NormalBayesClassifier([trainData, responses[, varIdx[, sampleIdx]]]) -> The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions. CvNormalBayesClassifier::train ------------------------------ Trains the model. .. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false ) .. ocv:function:: bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false ) .. ocv:pyfunction:: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retval :param update: Identifies whether the model should be trained from scratch (``update=false``) or should be updated using the new training data (``update=true``). The method trains the Normal Bayes classifier. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach with the following limitations: * Only ``CV_ROW_SAMPLE`` data layout is supported. * Input variables are all ordered. * Output variable is categorical , which means that elements of ``responses`` must be integer numbers, though the vector may have the ``CV_32FC1`` type. * Missing measurements are not supported. CvNormalBayesClassifier::predict -------------------------------- Predicts the response for sample(s). .. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const .. ocv:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const .. ocv:pyfunction:: cv2.NormalBayesClassifier.predict(samples) -> retval, results The method 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. The function is parallelized with the TBB library.