class cv::ml::NormalBayesClassifier
Overview
Bayes classifier for normally distributed data. Moreā¦
#include <ml.hpp> class NormalBayesClassifier: public cv::ml::StatModel { public: // methods virtual float predictProb( InputArray inputs, OutputArray outputs, OutputArray outputProbs, int flags = 0 ) const = 0; static Ptr<NormalBayesClassifier> create(); static Ptr<NormalBayesClassifier> load( const String& filepath, const String& nodeName = String() ); };
Inherited Members
public: // enums enum Flags; // methods virtual void clear(); virtual bool empty() const; virtual String getDefaultName() const; virtual void read(const FileNode& fn); virtual void save(const String& filename) const; virtual void write(FileStorage& fs) const; template <typename _Tp> static Ptr<_Tp> load( const String& filename, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> loadFromString( const String& strModel, const String& objname = String() ); template <typename _Tp> static Ptr<_Tp> read(const FileNode& fn); virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const; virtual bool empty() const; virtual int getVarCount() const = 0; virtual bool isClassifier() const = 0; virtual bool isTrained() const = 0; virtual float predict( InputArray samples, OutputArray results = noArray(), int flags = 0 ) const = 0; virtual bool train( const Ptr<TrainData>& trainData, int flags = 0 ); virtual bool train( InputArray samples, int layout, InputArray responses ); template <typename _Tp> static Ptr<_Tp> train( const Ptr<TrainData>& data, int flags = 0 ); protected: // methods void writeFormat(FileStorage& fs) const;
Detailed Documentation
Bayes classifier for normally distributed data.
See also:
Methods
virtual float predictProb( InputArray inputs, OutputArray outputs, OutputArray outputProbs, int flags = 0 ) const = 0
Predicts the response for sample(s).
The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output vector outputs. The predicted class for a single input vector is returned by the method. The vector outputProbs contains the output probabilities corresponding to each element of result.
static Ptr<NormalBayesClassifier> create()
Creates empty model Use StatModel::train to train the model after creation.
static Ptr<NormalBayesClassifier> load( const String& filepath, const String& nodeName = String() )
Loads and creates a serialized NormalBayesClassifier from a file.
Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier
Parameters:
filepath | path to serialized NormalBayesClassifier |
nodeName | name of node containing the classifier |