mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 06:10:02 +08:00
74 lines
3.2 KiB
ReStructuredText
74 lines
3.2 KiB
ReStructuredText
.. _Bayes Classifier:
|
|
|
|
Normal Bayes Classifier
|
|
=======================
|
|
|
|
This is a simple classification model assuming 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.
|
|
|
|
.. index:: CvNormalBayesClassifier
|
|
|
|
CvNormalBayesClassifier
|
|
-----------------------
|
|
.. c:type:: CvNormalBayesClassifier
|
|
|
|
Bayes classifier for normally distributed data ::
|
|
|
|
class CvNormalBayesClassifier : public CvStatModel
|
|
{
|
|
public:
|
|
CvNormalBayesClassifier();
|
|
virtual ~CvNormalBayesClassifier();
|
|
|
|
CvNormalBayesClassifier( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx=0, const CvMat* _sample_idx=0 );
|
|
|
|
virtual bool train( const CvMat* _train_data, const CvMat* _responses,
|
|
const CvMat* _var_idx = 0, const CvMat* _sample_idx=0, bool update=false );
|
|
|
|
virtual float predict( const CvMat* _samples, CvMat* results=0 ) const;
|
|
virtual void clear();
|
|
|
|
virtual void save( const char* filename, const char* name=0 );
|
|
virtual void load( const char* filename, const char* name=0 );
|
|
|
|
virtual void write( CvFileStorage* storage, const char* name );
|
|
virtual void read( CvFileStorage* storage, CvFileNode* node );
|
|
protected:
|
|
...
|
|
};
|
|
|
|
|
|
.. index:: CvNormalBayesClassifier::train
|
|
|
|
.. _CvNormalBayesClassifier::train:
|
|
|
|
CvNormalBayesClassifier::train
|
|
------------------------------
|
|
.. cpp:function:: bool CvNormalBayesClassifier::train( const CvMat* _train_data, const CvMat* _responses, const CvMat* _var_idx =0, const CvMat* _sample_idx=0, bool update=false )
|
|
|
|
Trains the model.
|
|
|
|
The method trains the Normal Bayes classifier. It follows the conventions of the generic ``train`` "method" 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.
|
|
|
|
In addition, there is an ``update`` flag that identifies whether the model should be trained from scratch ( ``update=false`` ) or should be updated using the new training data ( ``update=true`` ).
|
|
|
|
.. index:: CvNormalBayesClassifier::predict
|
|
|
|
.. _CvNormalBayesClassifier::predict:
|
|
|
|
CvNormalBayesClassifier::predict
|
|
--------------------------------
|
|
.. cpp:function:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
|
|
|
|
Predicts the response for sample(s).
|
|
|
|
The method ``predict`` 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.
|
|
|