Normal Bayes Classifier

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]

Fukunaga. Introduction to Statistical Pattern Recognition. second ed., New York: Academic Press, 1990.

CvNormalBayesClassifier

class CvNormalBayesClassifier

Bayes classifier for normally distributed data.

CvNormalBayesClassifier::CvNormalBayesClassifier

Default and training constructors.

C++: CvNormalBayesClassifier::CvNormalBayesClassifier()

C++: CvNormalBayesClassifier::CvNormalBayesClassifier(const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() )

C++: CvNormalBayesClassifier::CvNormalBayesClassifier(const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )

Python: cv2.NormalBayesClassifier(trainData, responses[, varIdx[, sampleIdx]]) → <NormalBayesClassifier object>

The constructors follow conventions of CvStatModel::CvStatModel(). See CvStatModel::train() for parameters descriptions.

CvNormalBayesClassifier::train

Trains the model.

C++: bool CvNormalBayesClassifier::train(const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat(), bool update=false )

C++: bool CvNormalBayesClassifier::train(const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0, bool update=false )

Python: cv2.NormalBayesClassifier.train(trainData, responses[, varIdx[, sampleIdx[, update]]]) → retval

Parameters: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 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).

C++: float CvNormalBayesClassifier::predict(const Mat& samples, Mat* results=0 ) const

C++: float CvNormalBayesClassifier::predict(const CvMat* samples, CvMat* results=0 ) const

Python: 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.