org.opencv.ml

Class CvRTrees

java.lang.Object

org.opencv.ml.CvStatModel

org.opencv.ml.CvRTrees

Direct Known Subclasses:

CvERTrees

public class CvRTrees
extends CvStatModel

The class implements the random forest predictor as described in the beginning of this section.

See Also:

org.opencv.ml.CvRTrees : public CvStatModel

Constructor Summary

Constructors

Constructor and Description
CvRTrees()

Method Summary

Methods

Modifier and Type	Method and Description
void	clear()
Mat	getVarImportance() Returns the variable importance array.
float	predict_prob(Mat sample) Returns a fuzzy-predicted class label.
float	predict_prob(Mat sample, Mat missing) Returns a fuzzy-predicted class label.
float	predict(Mat sample) Predicts the output for an input sample.
float	predict(Mat sample, Mat missing) Predicts the output for an input sample.
boolean	train(Mat trainData, int tflag, Mat responses) Trains the Random Trees model.
boolean	train(Mat trainData, int tflag, Mat responses, Mat varIdx, Mat sampleIdx, Mat varType, Mat missingDataMask, CvRTParams params) Trains the Random Trees model.

Methods inherited from class org.opencv.ml.CvStatModel

load, load, save, save

Methods inherited from class java.lang.Object

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

Constructor Detail

CvRTrees

public CvRTrees()

Method Detail

clear

public void clear()

getVarImportance

public Mat getVarImportance()

Returns the variable importance array.

The method returns the variable importance vector, computed at the training stage when CvRTParams.calc_var_importance is set to true. If this flag was set to false, the NULL pointer is returned. This differs from the decision trees where variable importance can be computed anytime after the training.

See Also:

org.opencv.ml.CvRTrees.getVarImportance

predict_prob

public float predict_prob(Mat sample)

Returns a fuzzy-predicted class label.

The function works for binary classification problems only. It returns the number between 0 and 1. This number represents probability or confidence of the sample belonging to the second class. It is calculated as the proportion of decision trees that classified the sample to the second class.

Parameters:

sample - Sample for classification.

See Also:

org.opencv.ml.CvRTrees.predict_prob

predict_prob

public float predict_prob(Mat sample,
                 Mat missing)

Returns a fuzzy-predicted class label.

The function works for binary classification problems only. It returns the number between 0 and 1. This number represents probability or confidence of the sample belonging to the second class. It is calculated as the proportion of decision trees that classified the sample to the second class.

Parameters:

sample - Sample for classification.

missing - Optional missing measurement mask of the sample.

See Also:

org.opencv.ml.CvRTrees.predict_prob

predict

public float predict(Mat sample)

Predicts the output for an input sample.

The input parameters of the prediction method are the same as in "CvDTree.predict" but the return value type is different. This method returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates).

Parameters:

sample - Sample for classification.

See Also:

org.opencv.ml.CvRTrees.predict

predict

public float predict(Mat sample,
            Mat missing)

Predicts the output for an input sample.

The input parameters of the prediction method are the same as in "CvDTree.predict" but the return value type is different. This method returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates).

Parameters:

sample - Sample for classification.

missing - Optional missing measurement mask of the sample.

See Also:

org.opencv.ml.CvRTrees.predict

train

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

Trains the Random Trees model.

The method "CvRTrees.train" is very similar to the method "CvDTree.train" and follows the generic method "CvStatModel.train" conventions. All the parameters specific to the algorithm training are passed as a "CvRTParams" instance. The estimate of the training error (oob-error) is stored in the protected class member oob_error.

The function is parallelized with the TBB library.

Parameters:

trainData - a trainData

tflag - a tflag

responses - a responses

See Also:

org.opencv.ml.CvRTrees.train

train

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

Trains the Random Trees model.

The method "CvRTrees.train" is very similar to the method "CvDTree.train" and follows the generic method "CvStatModel.train" conventions. All the parameters specific to the algorithm training are passed as a "CvRTParams" instance. The estimate of the training error (oob-error) is stored in the protected class member oob_error.

The function is parallelized with the TBB library.

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.CvRTrees.train