org.bytedeco.opencv.opencv_ml

Class DTrees

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

All Implemented Interfaces:

AutoCloseable

Direct Known Subclasses:

Boost, RTrees

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

\brief The class represents a single decision tree or a collection of decision trees.

The current public interface of the class allows user to train only a single decision tree, however the class is capable of storing multiple decision trees and using them for prediction (by summing responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost) use this capability to implement decision tree ensembles.

See Also:

ml_intro_trees

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	DTrees.Node \brief The class represents a decision tree node.
static class	DTrees.Split \brief The class represents split in a decision tree.

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

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

Field Summary

Fields

Modifier and Type	Field and Description
static int	PREDICT_AUTO enum cv::ml::DTrees::Flags
static int	PREDICT_MASK enum cv::ml::DTrees::Flags
static int	PREDICT_MAX_VOTE enum cv::ml::DTrees::Flags
static int	PREDICT_SUM enum cv::ml::DTrees::Flags

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
DTrees(Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static DTrees	create() \brief Creates the empty model
int	getCVFolds()
int	getMaxCategories()
int	getMaxDepth()
int	getMinSampleCount()
DTrees.Node	getNodes() \brief Returns all the nodes
Mat	getPriors()
float	getRegressionAccuracy()
IntPointer	getRoots() \brief Returns indices of root nodes
DTrees.Split	getSplits() \brief Returns all the splits
IntPointer	getSubsets() \brief Returns all the bitsets for categorical splits
boolean	getTruncatePrunedTree()
boolean	getUse1SERule()
boolean	getUseSurrogates()
static DTrees	load(BytePointer filepath)
static DTrees	load(BytePointer filepath, BytePointer nodeName) \brief Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk.
static DTrees	load(String filepath)
static DTrees	load(String filepath, String nodeName)
void	setCVFolds(int val) \copybrief getCVFolds @see getCVFolds
void	setMaxCategories(int val) \copybrief getMaxCategories @see getMaxCategories
void	setMaxDepth(int val) \copybrief getMaxDepth @see getMaxDepth
void	setMinSampleCount(int val) \copybrief getMinSampleCount @see getMinSampleCount
void	setPriors(Mat val) \copybrief getPriors @see getPriors
void	setRegressionAccuracy(float val) \copybrief getRegressionAccuracy @see getRegressionAccuracy
void	setTruncatePrunedTree(boolean val) \copybrief getTruncatePrunedTree @see getTruncatePrunedTree
void	setUse1SERule(boolean val) \copybrief getUse1SERule @see getUse1SERule
void	setUseSurrogates(boolean val) \copybrief getUseSurrogates @see getUseSurrogates

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

Field Detail

PREDICT_AUTO

public static final int PREDICT_AUTO

enum cv::ml::DTrees::Flags

See Also:

Constant Field Values

PREDICT_SUM

public static final int PREDICT_SUM

enum cv::ml::DTrees::Flags

See Also:

Constant Field Values

PREDICT_MAX_VOTE

public static final int PREDICT_MAX_VOTE

enum cv::ml::DTrees::Flags

See Also:

Constant Field Values

PREDICT_MASK

public static final int PREDICT_MASK

enum cv::ml::DTrees::Flags

See Also:

Constant Field Values

Constructor Detail

DTrees

public DTrees(Pointer p)

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

Method Detail

getMaxCategories

public int getMaxCategories()

See Also:

setMaxCategories

setMaxCategories

public void setMaxCategories(int val)

\copybrief getMaxCategories @see getMaxCategories

getMaxDepth

public int getMaxDepth()

See Also:

setMaxDepth

setMaxDepth

public void setMaxDepth(int val)

\copybrief getMaxDepth @see getMaxDepth

getMinSampleCount

public int getMinSampleCount()

See Also:

setMinSampleCount

setMinSampleCount

public void setMinSampleCount(int val)

\copybrief getMinSampleCount @see getMinSampleCount

getCVFolds

public int getCVFolds()

See Also:

setCVFolds

setCVFolds

public void setCVFolds(int val)

\copybrief getCVFolds @see getCVFolds

getUseSurrogates

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

See Also:

setUseSurrogates

setUseSurrogates

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

\copybrief getUseSurrogates @see getUseSurrogates

getUse1SERule

@Cast(value="bool")
public boolean getUse1SERule()

See Also:

setUse1SERule

setUse1SERule

public void setUse1SERule(@Cast(value="bool")
                          boolean val)

\copybrief getUse1SERule @see getUse1SERule

getTruncatePrunedTree

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

See Also:

setTruncatePrunedTree

setTruncatePrunedTree

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

\copybrief getTruncatePrunedTree @see getTruncatePrunedTree

getRegressionAccuracy

public float getRegressionAccuracy()

See Also:

setRegressionAccuracy

setRegressionAccuracy

public void setRegressionAccuracy(float val)

\copybrief getRegressionAccuracy @see getRegressionAccuracy

getPriors

@ByVal
public Mat getPriors()

See Also:

setPriors

setPriors

public void setPriors(@Const @ByRef
                      Mat val)

\copybrief getPriors @see getPriors

getRoots

@StdVector
public IntPointer getRoots()

\brief Returns indices of root nodes

getNodes

@StdVector
public DTrees.Node getNodes()

\brief Returns all the nodes

all the node indices are indices in the returned vector

getSplits

@StdVector
public DTrees.Split getSplits()

\brief Returns all the splits

all the split indices are indices in the returned vector

getSubsets

@StdVector
public IntPointer getSubsets()

\brief Returns all the bitsets for categorical splits

Split::subsetOfs is an offset in the returned vector

create

@opencv_core.Ptr
public static DTrees create()

\brief Creates the empty model

The static method creates empty decision tree with the specified parameters. It should be then trained using train method (see StatModel::train). Alternatively, you can load the model from file using Algorithm::load\(filename).

load

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

\brief Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk. Load the DTree 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 DTree

nodeName - name of node containing the classifier

load

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

load

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

load

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