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64 lines
3.6 KiB
ReStructuredText
64 lines
3.6 KiB
ReStructuredText
.. _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, Mat* results_prob=0 ) const
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.. ocv:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0, CvMat* results_prob=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. The vector ``results_prob`` contains the output probabilities coresponding to each element of ``result``.
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The function is parallelized with the TBB library.
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