org.bytedeco.opencv.opencv_ml

Class RTrees

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.opencv.opencv_core.Algorithm

org.bytedeco.opencv.opencv_ml.AbstractStatModel

org.bytedeco.opencv.opencv_ml.StatModel

org.bytedeco.opencv.opencv_ml.DTrees

org.bytedeco.opencv.opencv_ml.RTrees

All Implemented Interfaces:

AutoCloseable

@Namespace(value="cv::ml")
 @Properties(inherit=opencv_ml.class)
public class RTrees
extends DTrees

\brief The class implements the random forest predictor.

See Also:

ml_intro_rtrees

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.opencv.opencv_ml.DTrees

DTrees.Node, DTrees.Split

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields inherited from class org.bytedeco.opencv.opencv_ml.DTrees

PREDICT_AUTO, PREDICT_MASK, PREDICT_MAX_VOTE, PREDICT_SUM

Fields inherited from class org.bytedeco.opencv.opencv_ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
RTrees(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static RTrees	create() Creates the empty model.
int	getActiveVarCount()
boolean	getCalculateVarImportance()
TermCriteria	getTermCriteria()
Mat	getVarImportance() Returns the variable importance array.
void	getVotes(GpuMat samples, GpuMat results, int flags)
void	getVotes(Mat samples, Mat results, int flags) Returns the result of each individual tree in the forest.
void	getVotes(UMat samples, UMat results, int flags)
static RTrees	load(BytePointer filepath)
static RTrees	load(BytePointer filepath, BytePointer nodeName) \brief 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)
static RTrees	load(String filepath, String nodeName)
void	setActiveVarCount(int val) \copybrief getActiveVarCount @see getActiveVarCount
void	setCalculateVarImportance(boolean val) \copybrief getCalculateVarImportance @see getCalculateVarImportance
void	setTermCriteria(TermCriteria val) \copybrief getTermCriteria @see getTermCriteria

Methods inherited from class org.bytedeco.opencv.opencv_ml.DTrees

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

Methods inherited from class org.bytedeco.opencv.opencv_ml.StatModel

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

Methods inherited from class org.bytedeco.opencv.opencv_ml.AbstractStatModel

loadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVM

Methods inherited from class org.bytedeco.opencv.opencv_core.Algorithm

clear, getDefaultName, position, read, save, save, write, write, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

RTrees

public RTrees(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

getCalculateVarImportance

@Cast(value="bool")
public boolean getCalculateVarImportance()

See Also:

setCalculateVarImportance

setCalculateVarImportance

public void setCalculateVarImportance(@Cast(value="bool")
                                      boolean val)

\copybrief getCalculateVarImportance @see getCalculateVarImportance

getActiveVarCount

public int getActiveVarCount()

See Also:

setActiveVarCount

setActiveVarCount

public void setActiveVarCount(int val)

\copybrief getActiveVarCount @see getActiveVarCount

getTermCriteria

@ByVal
public TermCriteria getTermCriteria()

See Also:

setTermCriteria

setTermCriteria

public void setTermCriteria(@Const @ByRef
                            TermCriteria val)

\copybrief getTermCriteria @see getTermCriteria

getVarImportance

@ByVal
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.

getVotes

public void getVotes(@ByVal
                     Mat samples,
                     @ByVal
                     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.

getVotes

public void getVotes(@ByVal
                     UMat samples,
                     @ByVal
                     UMat results,
                     int flags)

getVotes

public void getVotes(@ByVal
                     GpuMat samples,
                     @ByVal
                     GpuMat results,
                     int flags)

create

@opencv_core.Ptr
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.

load

@opencv_core.Ptr
public static RTrees load(@opencv_core.Str
                                           BytePointer filepath,
                                           @opencv_core.Str
                                           BytePointer nodeName)

\brief 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

load

@opencv_core.Ptr
public static RTrees load(@opencv_core.Str
                                           BytePointer filepath)

load

@opencv_core.Ptr
public static RTrees load(@opencv_core.Str
                                           String filepath,
                                           @opencv_core.Str
                                           String nodeName)

load

@opencv_core.Ptr
public static RTrees load(@opencv_core.Str
                                           String filepath)