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Updated Normal Bayes Classifier docs

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Ilya Lysenkov
2011-06-27 10:02:04 +00:00
parent 2835fe88ac
commit 831857994c
1 changed files with 18 additions and 27 deletions
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modules/ml/doc/normal_bayes_classifier.rst
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@@ -13,53 +13,44 @@ CvNormalBayesClassifier
-----------------------
.. ocv:class:: CvNormalBayesClassifier
Bayes classifier for normally distributed data. ::
Bayes classifier for normally distributed data.
class CvNormalBayesClassifier : public CvStatModel
{
public:
CvNormalBayesClassifier();
virtual ~CvNormalBayesClassifier();
CvNormalBayesClassifier::CvNormalBayesClassifier
------------------------------------------------
Default and training constructors.
CvNormalBayesClassifier( const Mat& _train_data, const Mat& _responses,
const Mat& _var_idx=Mat(), const Mat& _sample_idx=Mat() );
.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier()
virtual bool train( const Mat& _train_data, const Mat& _responses,
const Mat& _var_idx=Mat(), const Mat& _sample_idx=Mat(), bool update=false );
.. ocv:function:: CvNormalBayesClassifier::CvNormalBayesClassifier( const Mat& trainData, const Mat& responses, const Mat& varIdx=Mat(), const Mat& sampleIdx=Mat() )
virtual float predict( const Mat& _samples, Mat* 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:
...
};
.. ocv:cfunction:: CvNormalBayesClassifier::CvNormalBayesClassifier( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx=0, const CvMat* sampleIdx=0 )
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& _train_data, const Mat& _responses, const Mat& _var_idx =Mat(), const Mat& _sample_idx=Mat(), bool update=false )
.. ocv:function:: bool CvNormalBayesClassifier::train( const Mat& trainData, const Mat& responses, const Mat& varIdx = Mat(), const Mat& sampleIdx=Mat(), bool update=false )
The method trains the Normal Bayes classifier. It follows the conventions of the generic ``train`` approach with the following limitations:
.. ocv:cfunction:: bool CvNormalBayesClassifier::train( const CvMat* trainData, const CvMat* responses, const CvMat* varIdx = 0, const CvMat* sampleIdx=0, bool update=false )
: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.
* 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`` ).
CvNormalBayesClassifier::predict
--------------------------------
Predicts the response for sample(s).
.. ocv:function:: float CvNormalBayesClassifier::predict( const Mat& samples, Mat* results=0 ) const
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.
.. ocv:cfunction:: float CvNormalBayesClassifier::predict( const CvMat* samples, CvMat* results=0 ) const
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.