Package org.opencv.ml

Class StatModel

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

Direct Known Subclasses:

ANN_MLP, DTrees, EM, KNearest, LogisticRegression, NormalBayesClassifier, SVM, SVMSGD

public class StatModel
extends Algorithm

Base class for statistical models in OpenCV ML.

Field Summary

Fields

Modifier and Type	Field	Description
static int	COMPRESSED_INPUT
static int	PREPROCESSED_INPUT
static int	RAW_OUTPUT
static int	UPDATE_MODEL

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	StatModel(long addr)

Method Summary

Modifier and Type	Method	Description
static StatModel	__fromPtr__(long addr)
float	calcError(TrainData data, boolean test, Mat resp)	Computes error on the training or test dataset
boolean	empty()	Returns true if the Algorithm is empty (e.g.
protected void	finalize()
int	getVarCount()	Returns the number of variables in training samples
boolean	isClassifier()	Returns true if the model is classifier
boolean	isTrained()	Returns true if the model is trained
float	predict(Mat samples)	Predicts response(s) for the provided sample(s)
float	predict(Mat samples, Mat results)	Predicts response(s) for the provided sample(s)
float	predict(Mat samples, Mat results, int flags)	Predicts response(s) for the provided sample(s)
boolean	train(Mat samples, int layout, Mat responses)	Trains the statistical model
boolean	train(TrainData trainData)	Trains the statistical model
boolean	train(TrainData trainData, int flags)	Trains the statistical model

Methods inherited from class org.opencv.core.Algorithm

clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

UPDATE_MODEL

public static final int UPDATE_MODEL

See Also:

Constant Field Values

RAW_OUTPUT

public static final int RAW_OUTPUT

See Also:

Constant Field Values

COMPRESSED_INPUT

public static final int COMPRESSED_INPUT

See Also:

Constant Field Values

PREPROCESSED_INPUT

public static final int PREPROCESSED_INPUT

See Also:

Constant Field Values

Constructor Detail

StatModel

protected StatModel(long addr)

Method Detail

__fromPtr__

public static StatModel __fromPtr__(long addr)

empty

public boolean empty()

Description copied from class: Algorithm

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

Overrides:

empty in class Algorithm

Returns:

automatically generated

isClassifier

public boolean isClassifier()

Returns true if the model is classifier

Returns:

automatically generated

isTrained

public boolean isTrained()

Returns true if the model is trained

Returns:

automatically generated

train

public boolean 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.

Returns:

automatically generated

train

public boolean 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).

Returns:

automatically generated

train

public boolean 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. new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Returns:

automatically generated

calcError

public float calcError(TrainData data,
                       boolean 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%).

Returns:

automatically generated

predict

public float predict(Mat samples,
                     Mat results,
                     int flags)

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.

Returns:

automatically generated

predict

public float predict(Mat samples,
                     Mat results)

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

Parameters:

samples - The input samples, floating-point matrix

results - The optional output matrix of results.

Returns:

automatically generated

predict

public float predict(Mat samples)

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

Parameters:

samples - The input samples, floating-point matrix

Returns:

automatically generated

getVarCount

public int getVarCount()

Returns the number of variables in training samples

Returns:

automatically generated

finalize

protected void finalize()
                 throws java.lang.Throwable

Overrides:

finalize in class Algorithm

Throws:

java.lang.Throwable