C# Class OpenCvSharp.ML.StatModel

Base class for statistical models in ML

Inheritance: Algorithm

Exibir arquivo Open project: shimat/opencvsharp

Public Methods

Method	Description
CalcError ( OpenCvSharp.ML.TrainData data, bool test, OutputArray resp ) : float	Computes error on the training or test dataset
Empty ( ) : bool
GetVarCount ( ) : int	Returns the number of variables in training samples
IsClassifier ( ) : bool	Returns true if the model is classifier
IsTrained ( ) : bool	Returns true if the model is trained
Predict ( InputArray samples, OutputArray results = null, Flags flags ) : float	Predicts response(s) for the provided sample(s)
Train ( InputArray samples, SampleTypes layout, InputArray responses ) : bool	Trains the statistical model
Train ( TrainData trainData, int flags ) : bool	Trains the statistical model

Method	Description
StatModel ( ) : System	Default constructor

Computes error on the training or test dataset

public CalcError ( OpenCvSharp.ML.TrainData data, bool test, OutputArray resp ) : float
data	OpenCvSharp.ML.TrainData	the training data
test	bool	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	OutputArray	the optional output responses.
return	float

public Empty ( ) : bool
return	bool

Returns the number of variables in training samples

public GetVarCount ( ) : int
return	int

Returns true if the model is classifier

public IsClassifier ( ) : bool
return	bool

Returns true if the model is trained

public IsTrained ( ) : bool
return	bool

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

public Predict ( InputArray samples, OutputArray results = null, Flags flags ) : float
samples	InputArray	The input samples, floating-point matrix
results	OutputArray	The optional output matrix of results.
flags	Flags	The optional flags, model-dependent.
return	float

Default constructor

protected StatModel ( ) : System
return	System

Trains the statistical model

public Train ( InputArray samples, SampleTypes layout, InputArray responses ) : bool
samples	InputArray	training samples
layout	SampleTypes	SampleTypes value
responses	InputArray	vector of responses associated with the training samples.
return	bool

Trains the statistical model

public Train ( TrainData trainData, int flags ) : bool
trainData	TrainData	training data that can be loaded from file using TrainData::loadFromCSV /// or created with TrainData::create.
flags	int	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).
return	bool