Package org.opencv.ml

Class DTrees

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

org.opencv.ml.DTrees

Direct Known Subclasses:

Boost, RTrees

public class DTrees
extends StatModel

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: REF: ml_intro_trees

Field Summary

Fields

Modifier and Type	Field	Description
static int	PREDICT_AUTO
static int	PREDICT_MASK
static int	PREDICT_MAX_VOTE
static int	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	DTrees(long addr)

Method Summary

Modifier and Type	Method	Description
static DTrees	__fromPtr__(long addr)
static DTrees	create()	Creates the empty model The static method creates empty decision tree with the specified parameters.
protected void	finalize()
int	getCVFolds()	SEE: setCVFolds
int	getMaxCategories()	SEE: setMaxCategories
int	getMaxDepth()	SEE: setMaxDepth
int	getMinSampleCount()	SEE: setMinSampleCount
Mat	getPriors()	SEE: setPriors
float	getRegressionAccuracy()	SEE: setRegressionAccuracy
boolean	getTruncatePrunedTree()	SEE: setTruncatePrunedTree
boolean	getUse1SERule()	SEE: setUse1SERule
boolean	getUseSurrogates()	SEE: setUseSurrogates
static DTrees	load(String filepath)	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, String nodeName)	Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk.
void	setCVFolds(int val)	getCVFolds SEE: getCVFolds
void	setMaxCategories(int val)	getMaxCategories SEE: getMaxCategories
void	setMaxDepth(int val)	getMaxDepth SEE: getMaxDepth
void	setMinSampleCount(int val)	getMinSampleCount SEE: getMinSampleCount
void	setPriors(Mat val)	getPriors SEE: getPriors
void	setRegressionAccuracy(float val)	getRegressionAccuracy SEE: getRegressionAccuracy
void	setTruncatePrunedTree(boolean val)	getTruncatePrunedTree SEE: getTruncatePrunedTree
void	setUse1SERule(boolean val)	getUse1SERule SEE: getUse1SERule
void	setUseSurrogates(boolean val)	getUseSurrogates SEE: getUseSurrogates

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

Field Details

PREDICT_AUTO

public static final int PREDICT_AUTO

See Also:

Constant Field Values

PREDICT_SUM

public static final int PREDICT_SUM

See Also:

Constant Field Values

PREDICT_MAX_VOTE

public static final int PREDICT_MAX_VOTE

See Also:

Constant Field Values

PREDICT_MASK

public static final int PREDICT_MASK

See Also:

Constant Field Values

Constructor Details

DTrees

protected DTrees(long addr)

Method Details

__fromPtr__

public static DTrees __fromPtr__(long addr)

getMaxCategories

public int getMaxCategories()

SEE: setMaxCategories

Returns:

automatically generated

setMaxCategories

public void setMaxCategories(int val)

getMaxCategories SEE: getMaxCategories

Parameters:

val - automatically generated

getMaxDepth

public int getMaxDepth()

SEE: setMaxDepth

Returns:

automatically generated

setMaxDepth

public void setMaxDepth(int val)

getMaxDepth SEE: getMaxDepth

Parameters:

val - automatically generated

getMinSampleCount

public int getMinSampleCount()

SEE: setMinSampleCount

Returns:

automatically generated

setMinSampleCount

public void setMinSampleCount(int val)

getMinSampleCount SEE: getMinSampleCount

Parameters:

val - automatically generated

getCVFolds

public int getCVFolds()

SEE: setCVFolds

Returns:

automatically generated

setCVFolds

public void setCVFolds(int val)

getCVFolds SEE: getCVFolds

Parameters:

val - automatically generated

getUseSurrogates

public boolean getUseSurrogates()

SEE: setUseSurrogates

Returns:

automatically generated

setUseSurrogates

public void setUseSurrogates(boolean val)

getUseSurrogates SEE: getUseSurrogates

Parameters:

val - automatically generated

getUse1SERule

public boolean getUse1SERule()

SEE: setUse1SERule

Returns:

automatically generated

setUse1SERule

public void setUse1SERule(boolean val)

getUse1SERule SEE: getUse1SERule

Parameters:

val - automatically generated

getTruncatePrunedTree

public boolean getTruncatePrunedTree()

SEE: setTruncatePrunedTree

Returns:

automatically generated

setTruncatePrunedTree

public void setTruncatePrunedTree(boolean val)

getTruncatePrunedTree SEE: getTruncatePrunedTree

Parameters:

val - automatically generated

getRegressionAccuracy

public float getRegressionAccuracy()

SEE: setRegressionAccuracy

Returns:

automatically generated

setRegressionAccuracy

public void setRegressionAccuracy(float val)

getRegressionAccuracy SEE: getRegressionAccuracy

Parameters:

val - automatically generated

getPriors

public Mat getPriors()

SEE: setPriors

Returns:

automatically generated

setPriors

public void setPriors(Mat val)

getPriors SEE: getPriors

Parameters:

val - automatically generated

create

public static DTrees create()

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<DTrees>(filename).

Returns:

automatically generated

load

public static DTrees load(String filepath, String nodeName)

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

Returns:

automatically generated

load

public static DTrees load(String filepath)

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

Returns:

automatically generated

finalize

protected void finalize() throws Throwable

Overrides:

finalize in class StatModel

Throws:

Throwable