mirror of
https://github.com/opencv/opencv.git
synced 2024-12-18 11:28:02 +08:00
64 lines
3.4 KiB
ReStructuredText
64 lines
3.4 KiB
ReStructuredText
.. _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
|
|
|
|
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]]) -> <NormalBayesClassifier object>
|
|
|
|
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.
|