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63 lines
3.4 KiB
63 lines
3.4 KiB
.. _Bayes Classifier: |
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Normal Bayes Classifier |
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======================= |
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.. highlight:: cpp |
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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. |
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.. [Fukunaga90] K. Fukunaga. *Introduction to Statistical Pattern Recognition*. second ed., New York: Academic Press, 1990. |
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CvNormalBayesClassifier |
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----------------------- |
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.. ocv:class:: CvNormalBayesClassifier : public CvStatModel |
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Bayes classifier for normally distributed data. |
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CvNormalBayesClassifier::CvNormalBayesClassifier |
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------------------------------------------------ |
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Default and training constructors. |
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.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier() |
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.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() ) |
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.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 ) |
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.. ocv:pyfunction:: cv2.NormalBayesClassifier([trainData, responses[, varIdx[, sampleIdx]]]) -> <NormalBayesClassifier object> |
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The constructors follow conventions of :ocv:func:`CvStatModel::CvStatModel`. See :ocv:func:`CvStatModel::train` for parameters descriptions. |
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CvNormalBayesClassifier::train |
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------------------------------ |
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Trains the model. |
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.. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false ) |
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.. ocv:function:: bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false ) |
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.. ocv:pyfunction:: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) -> retval |
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:param update: 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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The method trains the Normal Bayes classifier. It follows the conventions of the generic :ocv:func:`CvStatModel::train` approach 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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CvNormalBayesClassifier::predict |
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-------------------------------- |
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Predicts the response for sample(s). |
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.. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const |
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.. ocv:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const |
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.. ocv:pyfunction:: cv2.NormalBayesClassifier.predict(samples) -> retval, results |
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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. |
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The function is parallelized with the TBB library.
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