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

Public Member Functions
float	calcError (TrainData data, bool test, Mat resp)
	Computes error on the training or test dataset.

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

int	getVarCount ()
	Returns the number of variables in training samples.

bool	isClassifier ()
	Returns true if the model is classifier.

bool	isTrained ()
	Returns true if the model is trained.

virtual float	predict (Mat samples)
	Predicts response(s) for the provided sample(s)

virtual float	predict (Mat samples, Mat results)
	Predicts response(s) for the provided sample(s)

virtual float	predict (Mat samples, Mat results, int flags)
	Predicts response(s) for the provided sample(s)

bool	train (Mat samples, int layout, Mat responses)
	Trains the statistical model.

bool	train (TrainData trainData)
	Trains the statistical model.

bool	train (TrainData trainData, int flags)
	Trains the statistical model.

Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
virtual void	clear ()
	Clears the algorithm state.

virtual string	getDefaultName ()

IntPtr	getNativeObjAddr ()

void	save (string filename)

Public Member Functions inherited from OpenCVForUnity.DisposableObject
void	Dispose ()

void	ThrowIfDisposed ()

Static Public Member Functions
static new StatModel	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
static Algorithm	__fromPtr__ (IntPtr addr)

Static Public Member Functions inherited from OpenCVForUnity.DisposableObject
static IntPtr	ThrowIfNullIntPtr (IntPtr ptr)

Static Public Attributes
const int	COMPRESSED_INPUT = 2

const int	PREPROCESSED_INPUT = 4

const int	RAW_OUTPUT = 1

const int	UPDATE_MODEL = 1

Protected Member Functions
override void	Dispose (bool disposing)

Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject
	DisposableOpenCVObject ()

	DisposableOpenCVObject (bool isEnabledDispose)

	DisposableOpenCVObject (IntPtr ptr)

	DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose)

Protected Member Functions inherited from OpenCVForUnity.DisposableObject
	DisposableObject ()

	DisposableObject (bool isEnabledDispose)

Additional Inherited Members
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject
Properties inherited from OpenCVForUnity.DisposableObject
bool	IsDisposed `[get, protected set]`

bool	IsEnabledDispose `[get, set]`

Detailed Description

Base class for statistical models in OpenCV ML.

Member Function Documentation

◆ fromPtr()

static new StatModel OpenCVForUnity.MlModule.StatModel.__fromPtr__ ( IntPtr addr )

static

◆ calcError()

float OpenCVForUnity.MlModule.StatModel.calcError	(	TrainData	data,
		bool	test,
		Mat	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%).

◆ Dispose()

override void OpenCVForUnity.MlModule.StatModel.Dispose ( bool disposing )

protectedvirtual

Reimplemented from OpenCVForUnity.CoreModule.Algorithm.

Reimplemented in OpenCVForUnity.MlModule.SVM, and OpenCVForUnity.MlModule.SVMSGD.

◆ empty()

override bool OpenCVForUnity.MlModule.StatModel.empty ( )

virtual

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

Reimplemented from OpenCVForUnity.CoreModule.Algorithm.

◆ getVarCount()

int OpenCVForUnity.MlModule.StatModel.getVarCount ( )

Returns the number of variables in training samples.

◆ isClassifier()

bool OpenCVForUnity.MlModule.StatModel.isClassifier ( )

Returns true if the model is classifier.

◆ isTrained()

bool OpenCVForUnity.MlModule.StatModel.isTrained ( )

Returns true if the model is trained.

◆ predict() [1/3]

virtual float OpenCVForUnity.MlModule.StatModel.predict ( Mat samples )

virtual

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.

Reimplemented in OpenCVForUnity.MlModule.EM, and OpenCVForUnity.MlModule.LogisticRegression.

◆ predict() [2/3]

virtual float OpenCVForUnity.MlModule.StatModel.predict	(	Mat	samples,
		Mat	results )

virtual

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.

Reimplemented in OpenCVForUnity.MlModule.EM, and OpenCVForUnity.MlModule.LogisticRegression.

◆ predict() [3/3]

virtual float OpenCVForUnity.MlModule.StatModel.predict	(	Mat	samples,
		Mat	results,
		int	flags )

virtual

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.

Reimplemented in OpenCVForUnity.MlModule.EM, and OpenCVForUnity.MlModule.LogisticRegression.

◆ train() [1/3]

bool OpenCVForUnity.MlModule.StatModel.train	(	Mat	samples,
		int	layout,
		Mat	responses )

Trains the statistical model.

Parameters

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

◆ train() [2/3]

bool OpenCVForUnity.MlModule.StatModel.train ( TrainData trainData )

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]

bool OpenCVForUnity.MlModule.StatModel.train	(	TrainData	trainData,
		int	flags )

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).

Member Data Documentation

◆ COMPRESSED_INPUT

const int OpenCVForUnity.MlModule.StatModel.COMPRESSED_INPUT = 2

static

◆ PREPROCESSED_INPUT

const int OpenCVForUnity.MlModule.StatModel.PREPROCESSED_INPUT = 4

static

◆ RAW_OUTPUT

const int OpenCVForUnity.MlModule.StatModel.RAW_OUTPUT = 1

static

◆ UPDATE_MODEL

const int OpenCVForUnity.MlModule.StatModel.UPDATE_MODEL = 1

static

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

OpenCVForUnity/Assets/OpenCVForUnity/org/opencv/ml/StatModel.cs

Public Member Functions

Static Public Member Functions

Static Public Attributes

Protected Member Functions

Additional Inherited Members

Detailed Description

Member Function Documentation

◆ __fromPtr__()

◆ calcError()

◆ Dispose()

◆ empty()

◆ getVarCount()

◆ isClassifier()

◆ isTrained()

◆ predict() [1/3]

◆ predict() [2/3]

◆ predict() [3/3]

◆ train() [1/3]

◆ train() [2/3]

◆ train() [3/3]

Member Data Documentation

◆ COMPRESSED_INPUT

◆ PREPROCESSED_INPUT

◆ RAW_OUTPUT

◆ UPDATE_MODEL

◆ fromPtr()