Package org.opencv.ml
Class DTrees
- java.lang.Object
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- org.opencv.core.Algorithm
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- org.opencv.ml.StatModel
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- org.opencv.ml.DTrees
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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
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Field Summary
Fields Modifier and Type Field Description static intPREDICT_AUTOstatic intPREDICT_MASKstatic intPREDICT_MAX_VOTEstatic intPREDICT_SUM-
Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
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Constructor Summary
Constructors Modifier Constructor Description protectedDTrees(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static DTrees__fromPtr__(long addr)static DTreescreate()Creates the empty model The static method creates empty decision tree with the specified parameters.protected voidfinalize()intgetCVFolds()SEE: setCVFoldsintgetMaxCategories()SEE: setMaxCategoriesintgetMaxDepth()SEE: setMaxDepthintgetMinSampleCount()SEE: setMinSampleCountMatgetPriors()SEE: setPriorsfloatgetRegressionAccuracy()SEE: setRegressionAccuracybooleangetTruncatePrunedTree()SEE: setTruncatePrunedTreebooleangetUse1SERule()SEE: setUse1SERulebooleangetUseSurrogates()SEE: setUseSurrogatesstatic DTreesload(String filepath)Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk.static DTreesload(String filepath, String nodeName)Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk.voidsetCVFolds(int val)getCVFolds SEE: getCVFoldsvoidsetMaxCategories(int val)getMaxCategories SEE: getMaxCategoriesvoidsetMaxDepth(int val)getMaxDepth SEE: getMaxDepthvoidsetMinSampleCount(int val)getMinSampleCount SEE: getMinSampleCountvoidsetPriors(Mat val)getPriors SEE: getPriorsvoidsetRegressionAccuracy(float val)getRegressionAccuracy SEE: getRegressionAccuracyvoidsetTruncatePrunedTree(boolean val)getTruncatePrunedTree SEE: getTruncatePrunedTreevoidsetUse1SERule(boolean val)getUse1SERule SEE: getUse1SERulevoidsetUseSurrogates(boolean val)getUseSurrogates SEE: getUseSurrogates-
Methods inherited from class org.opencv.ml.StatModel
calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train
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Methods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save
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Field Detail
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PREDICT_AUTO
public static final int PREDICT_AUTO
- See Also:
- Constant Field Values
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PREDICT_SUM
public static final int PREDICT_SUM
- See Also:
- Constant Field Values
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PREDICT_MAX_VOTE
public static final int PREDICT_MAX_VOTE
- See Also:
- Constant Field Values
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PREDICT_MASK
public static final int PREDICT_MASK
- See Also:
- Constant Field Values
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Method Detail
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__fromPtr__
public static DTrees __fromPtr__(long addr)
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getPriors
public Mat getPriors()
SEE: setPriors- Returns:
- automatically generated
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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
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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 DTreenodeName- name of node containing the classifier- Returns:
- automatically generated
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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
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getTruncatePrunedTree
public boolean getTruncatePrunedTree()
SEE: setTruncatePrunedTree- Returns:
- automatically generated
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getUse1SERule
public boolean getUse1SERule()
SEE: setUse1SERule- Returns:
- automatically generated
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getUseSurrogates
public boolean getUseSurrogates()
SEE: setUseSurrogates- Returns:
- automatically generated
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getRegressionAccuracy
public float getRegressionAccuracy()
SEE: setRegressionAccuracy- Returns:
- automatically generated
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getCVFolds
public int getCVFolds()
SEE: setCVFolds- Returns:
- automatically generated
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getMaxCategories
public int getMaxCategories()
SEE: setMaxCategories- Returns:
- automatically generated
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getMaxDepth
public int getMaxDepth()
SEE: setMaxDepth- Returns:
- automatically generated
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getMinSampleCount
public int getMinSampleCount()
SEE: setMinSampleCount- Returns:
- automatically generated
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setCVFolds
public void setCVFolds(int val)
getCVFolds SEE: getCVFolds- Parameters:
val- automatically generated
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setMaxCategories
public void setMaxCategories(int val)
getMaxCategories SEE: getMaxCategories- Parameters:
val- automatically generated
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setMaxDepth
public void setMaxDepth(int val)
getMaxDepth SEE: getMaxDepth- Parameters:
val- automatically generated
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setMinSampleCount
public void setMinSampleCount(int val)
getMinSampleCount SEE: getMinSampleCount- Parameters:
val- automatically generated
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setPriors
public void setPriors(Mat val)
getPriors SEE: getPriors- Parameters:
val- automatically generated
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setRegressionAccuracy
public void setRegressionAccuracy(float val)
getRegressionAccuracy SEE: getRegressionAccuracy- Parameters:
val- automatically generated
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setTruncatePrunedTree
public void setTruncatePrunedTree(boolean val)
getTruncatePrunedTree SEE: getTruncatePrunedTree- Parameters:
val- automatically generated
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setUse1SERule
public void setUse1SERule(boolean val)
getUse1SERule SEE: getUse1SERule- Parameters:
val- automatically generated
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setUseSurrogates
public void setUseSurrogates(boolean val)
getUseSurrogates SEE: getUseSurrogates- Parameters:
val- automatically generated
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