cv::ml::StatModel Class Reference

Base class for statistical models in OpenCV ML. More...

#include <opencv2/ml.hpp>

Inheritance diagram for cv::ml::StatModel:

Public Types
enum	Flags {   UPDATE_MODEL = 1 ,   RAW_OUTPUT =1 ,   COMPRESSED_INPUT =2 ,   PREPROCESSED_INPUT =4 }

Public Member Functions
virtual float	calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
	Computes error on the training or test dataset.
virtual bool	empty () const CV_OVERRIDE
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual int	getVarCount () const =0
	Returns the number of variables in training samples.
virtual bool	isClassifier () const =0
	Returns true if the model is classifier.
virtual bool	isTrained () const =0
	Returns true if the model is trained.
virtual float	predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
	Predicts response(s) for the provided sample(s)
virtual bool	train (const Ptr< TrainData > &trainData, int flags=0)
	Trains the statistical model.
virtual bool	train (InputArray samples, int layout, InputArray responses)
	Trains the statistical model.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.

Static Public Member Functions
template<typename _Tp >
static Ptr< _Tp >	train (const Ptr< TrainData > &data, int flags=0)
	Create and train model with default parameters.
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

Base class for statistical models in OpenCV ML.

Member Enumeration Documentation

Flags

enum cv::ml::StatModel::Flags

Predict options

Enumerator
UPDATE_MODEL
RAW_OUTPUT	makes the method return the raw results (the sum), not the class label
COMPRESSED_INPUT
PREPROCESSED_INPUT

Member Function Documentation

calcError()

virtual float cv::ml::StatModel::calcError ( const Ptr< TrainData > &  data, bool  test, OutputArray  resp  ) const

virtual

Python:
	cv.ml.StatModel.calcError(	data, test[, resp]	) ->	retval, resp

Computes error on the training or test dataset.

Parameters

data	the training data
test	if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
resp	the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

empty()

virtual bool cv::ml::StatModel::empty ( ) const

virtual

Python:
	cv.ml.StatModel.empty(		) ->	retval

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

Reimplemented from cv::Algorithm.

getVarCount()

virtual int cv::ml::StatModel::getVarCount ( ) const

pure virtual

Python:
	cv.ml.StatModel.getVarCount(		) ->	retval

Returns the number of variables in training samples.

isClassifier()

virtual bool cv::ml::StatModel::isClassifier ( ) const

pure virtual

Python:
	cv.ml.StatModel.isClassifier(		) ->	retval

Returns true if the model is classifier.

isTrained()

virtual bool cv::ml::StatModel::isTrained ( ) const

pure virtual

Python:
	cv.ml.StatModel.isTrained(		) ->	retval

Returns true if the model is trained.

predict()

virtual float cv::ml::StatModel::predict ( InputArray  samples, OutputArray  results = noArray(), int  flags = 0  ) const

pure virtual

Python:
	cv.ml.StatModel.predict(	samples[, results[, flags]]	) ->	retval, results

Predicts response(s) for the provided sample(s)

Parameters

samples	The input samples, floating-point matrix
results	The optional output matrix of results.
flags	The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Implemented in cv::ml::EM, and cv::ml::LogisticRegression.

train() [1/3]

template<typename _Tp >

static Ptr< _Tp > cv::ml::StatModel::train ( const Ptr< TrainData > &  data, int  flags = 0  )

inlinestatic

Python:
	cv.ml.StatModel.train(	trainData[, flags]	) ->	retval
	cv.ml.StatModel.train(	samples, layout, responses	) ->	retval

Create and train model with default parameters.

The class must implement static create() method with no parameters or with all default parameter values

train() [2/3]

virtual bool cv::ml::StatModel::train ( const Ptr< TrainData > &  trainData, int  flags = 0  )

virtual

Python:
	cv.ml.StatModel.train(	trainData[, flags]	) ->	retval
	cv.ml.StatModel.train(	samples, layout, responses	) ->	retval

Trains the statistical model.

Parameters

trainData	training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flags	optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

train() [3/3]

virtual bool cv::ml::StatModel::train ( InputArray  samples, int  layout, InputArray  responses  )

virtual

Python:
	cv.ml.StatModel.train(	trainData[, flags]	) ->	retval
	cv.ml.StatModel.train(	samples, layout, responses	) ->	retval

Trains the statistical model.

Parameters

samples	training samples
layout	See ml::SampleTypes.
responses	vector of responses associated with the training samples.

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The documentation for this class was generated from the following file:

• opencv2/ml.hpp