Package org.opencv.ml

Class RTrees

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

org.opencv.ml.DTrees

org.opencv.ml.RTrees

public class RTrees
extends DTrees

The class implements the random forest predictor. SEE: REF: ml_intro_rtrees

Field Summary

Fields inherited from class org.opencv.ml.DTrees

PREDICT_AUTO, PREDICT_MASK, PREDICT_MAX_VOTE, PREDICT_SUM

Fields inherited from class org.opencv.ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	RTrees(long addr)

Method Summary

Modifier and Type	Method	Description
static RTrees	__fromPtr__(long addr)
static RTrees	create()	Creates the empty model.
protected void	finalize()
int	getActiveVarCount()	SEE: setActiveVarCount
boolean	getCalculateVarImportance()	SEE: setCalculateVarImportance
double	getOOBError()	Returns the OOB error value, computed at the training stage when calcOOBError is set to true.
TermCriteria	getTermCriteria()	SEE: setTermCriteria
Mat	getVarImportance()	Returns the variable importance array.
void	getVotes(Mat samples, Mat results, int flags)	Returns the result of each individual tree in the forest.
static RTrees	load(String filepath)	Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk.
static RTrees	load(String filepath, String nodeName)	Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk.
void	setActiveVarCount(int val)	getActiveVarCount SEE: getActiveVarCount
void	setCalculateVarImportance(boolean val)	getCalculateVarImportance SEE: getCalculateVarImportance
void	setTermCriteria(TermCriteria val)	getTermCriteria SEE: getTermCriteria

Methods inherited from class org.opencv.ml.DTrees

getCVFolds, getMaxCategories, getMaxDepth, getMinSampleCount, getPriors, getRegressionAccuracy, getTruncatePrunedTree, getUse1SERule, getUseSurrogates, setCVFolds, setMaxCategories, setMaxDepth, setMinSampleCount, setPriors, setRegressionAccuracy, setTruncatePrunedTree, setUse1SERule, setUseSurrogates

Methods inherited from class org.opencv.ml.StatModel

calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train

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

Constructor Details

RTrees

protected RTrees(long addr)

Method Details

__fromPtr__

public static RTrees __fromPtr__(long addr)

getCalculateVarImportance

public boolean getCalculateVarImportance()

SEE: setCalculateVarImportance

Returns:

automatically generated

setCalculateVarImportance

public void setCalculateVarImportance(boolean val)

getCalculateVarImportance SEE: getCalculateVarImportance

Parameters:

val - automatically generated

getActiveVarCount

public int getActiveVarCount()

SEE: setActiveVarCount

Returns:

automatically generated

setActiveVarCount

public void setActiveVarCount(int val)

getActiveVarCount SEE: getActiveVarCount

Parameters:

val - automatically generated

getTermCriteria

public TermCriteria getTermCriteria()

SEE: setTermCriteria

Returns:

automatically generated

setTermCriteria

public void setTermCriteria(TermCriteria val)

getTermCriteria SEE: getTermCriteria

Parameters:

val - automatically generated

getVarImportance

public Mat getVarImportance()

Returns the variable importance array. The method returns the variable importance vector, computed at the training stage when CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is returned.

Returns:

automatically generated

getVotes

public void getVotes(Mat samples, Mat results, int flags)

Returns the result of each individual tree in the forest. In case the model is a regression problem, the method will return each of the trees' results for each of the sample cases. If the model is a classifier, it will return a Mat with samples + 1 rows, where the first row gives the class number and the following rows return the votes each class had for each sample.

Parameters:

samples - Array containing the samples for which votes will be calculated.

results - Array where the result of the calculation will be written.

flags - Flags for defining the type of RTrees.

getOOBError

public double getOOBError()

Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.

Returns:

automatically generated

create

public static RTrees create()

Creates the empty model. Use StatModel::train to train the model, StatModel::train to create and train the model, Algorithm::load to load the pre-trained model.

Returns:

automatically generated

load

public static RTrees load(String filepath, String nodeName)

Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters:

filepath - path to serialized RTree

nodeName - name of node containing the classifier

Returns:

automatically generated

load

public static RTrees load(String filepath)

Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk. Load the RTree from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters:

filepath - path to serialized RTree

Returns:

automatically generated

finalize

protected void finalize() throws Throwable

Overrides:

finalize in class DTrees

Throws:

Throwable