org.opencv.ml
Class CvBoost

java.lang.Object
  org.opencv.ml.CvStatModel
      org.opencv.ml.CvBoost

public class CvBoost
extends CvStatModel

Boosted tree classifier derived from "CvStatModel".

See Also:

org.opencv.ml.CvBoost : public CvStatModel

Field Summary

static int	DEFAULT
static int	DISCRETE
static int	GENTLE
static int	GINI
static int	LOGIT
static int	MISCLASS
static int	REAL
static int	SQERR

Fields inherited from class org.opencv.ml.CvStatModel

nativeObj

Constructor Summary

	CvBoost() Default and training constructors.
protected	CvBoost(long addr)
	CvBoost(Mat trainData, int tflag, Mat responses) Default and training constructors.
	CvBoost(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvBoostParams params) Default and training constructors.

Method Summary

void	clear()
protected void	finalize()
float	predict(Mat sample) Predicts a response for an input sample.
float	predict(Mat sample, Mat missing, Range slice, boolean rawMode, boolean returnSum) Predicts a response for an input sample.
void	prune(Range slice) Removes the specified weak classifiers.
boolean	train(Mat trainData, int tflag, Mat responses) Trains a boosted tree classifier.
boolean	train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvBoostParams params, boolean update) Trains a boosted tree classifier.

Methods inherited from class org.opencv.ml.CvStatModel

load, load, save, save

Methods inherited from class java.lang.Object

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

Field Detail

DEFAULT

public static final int DEFAULT

See Also:

Constant Field Values

DISCRETE

public static final int DISCRETE

See Also:

Constant Field Values

GENTLE

public static final int GENTLE

See Also:

Constant Field Values

GINI

public static final int GINI

See Also:

Constant Field Values

LOGIT

public static final int LOGIT

See Also:

Constant Field Values

MISCLASS

public static final int MISCLASS

See Also:

Constant Field Values

REAL

public static final int REAL

See Also:

Constant Field Values

SQERR

public static final int SQERR

See Also:

Constant Field Values

Constructor Detail

CvBoost

public CvBoost()

Default and training constructors.

The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

See Also:

org.opencv.ml.CvBoost.CvBoost

CvBoost

protected CvBoost(long addr)

CvBoost

public CvBoost(Mat trainData,
               int tflag,
               Mat responses)

Default and training constructors.

The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

See Also:

org.opencv.ml.CvBoost.CvBoost

CvBoost

public CvBoost(Mat trainData,
               int tflag,
               Mat responses,
               Mat varIdx,
               Mat sampleIdx,
               Mat varType,
               Mat missingDataMask,
               CvBoostParams params)

Default and training constructors.

The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

varIdx - a varIdx

sampleIdx - a sampleIdx

varType - a varType

missingDataMask - a missingDataMask

params - a params

See Also:

org.opencv.ml.CvBoost.CvBoost

Method Detail

clear

public void clear()

finalize

protected void finalize()
                 throws java.lang.Throwable

Overrides:

finalize in class CvStatModel

Throws:

java.lang.Throwable

predict

public float predict(Mat sample)

Predicts a response for an input sample.

The method runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting.

Parameters:

sample - Input sample.

See Also:

org.opencv.ml.CvBoost.predict

predict

public float predict(Mat sample,
                     Mat missing,
                     Range slice,
                     boolean rawMode,
                     boolean returnSum)

Predicts a response for an input sample.

The method runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting.

Parameters:

sample - Input sample.

missing - Optional mask of missing measurements. To handle missing measurements, the weak classifiers must include surrogate splits (see CvDTreeParams.use_surrogates).

slice - Continuous subset of the sequence of weak classifiers to be used for prediction. By default, all the weak classifiers are used.

rawMode - a rawMode

returnSum - a returnSum

See Also:

org.opencv.ml.CvBoost.predict

prune

public void prune(Range slice)

Removes the specified weak classifiers.

The method removes the specified weak classifiers from the sequence.

Note: Do not confuse this method with the pruning of individual decision trees, which is currently not supported.

Parameters:

slice - Continuous subset of the sequence of weak classifiers to be removed.

See Also:

org.opencv.ml.CvBoost.prune

train

public boolean train(Mat trainData,
                     int tflag,
                     Mat responses)

Trains a boosted tree classifier.

The train method follows the common template of "CvStatModel.train". The responses must be categorical, which means that boosted trees cannot be built for regression, and there should be two classes.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

See Also:

org.opencv.ml.CvBoost.train

train

public boolean train(Mat trainData,
                     int tflag,
                     Mat responses,
                     Mat varIdx,
                     Mat sampleIdx,
                     Mat varType,
                     Mat missingDataMask,
                     CvBoostParams params,
                     boolean update)

Trains a boosted tree classifier.

The train method follows the common template of "CvStatModel.train". The responses must be categorical, which means that boosted trees cannot be built for regression, and there should be two classes.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

varIdx - a varIdx

sampleIdx - a sampleIdx

varType - a varType

missingDataMask - a missingDataMask

params - a params

update - Specifies whether the classifier needs to be updated (true, the new weak tree classifiers added to the existing ensemble) or the classifier needs to be rebuilt from scratch (false).

See Also:

org.opencv.ml.CvBoost.train