Package org.opencv.ml
Class DTrees
java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
org.opencv.ml.DTrees
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
FieldsModifier and TypeFieldDescriptionstatic final int
static final int
static final int
static final int
Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic 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
SEE: setCVFoldsint
SEE: setMaxCategoriesint
SEE: setMaxDepthint
SEE: setMinSampleCountSEE: setPriorsfloat
SEE: setRegressionAccuracyboolean
SEE: setTruncatePrunedTreeboolean
SEE: setUse1SERuleboolean
SEE: setUseSurrogatesstatic DTrees
Loads and creates a serialized DTrees from a file Use DTree::save to serialize and store an DTree to disk.static DTrees
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: getCVFoldsvoid
setMaxCategories
(int val) getMaxCategories SEE: getMaxCategoriesvoid
setMaxDepth
(int val) getMaxDepth SEE: getMaxDepthvoid
setMinSampleCount
(int val) getMinSampleCount SEE: getMinSampleCountvoid
getPriors SEE: getPriorsvoid
setRegressionAccuracy
(float val) getRegressionAccuracy SEE: getRegressionAccuracyvoid
setTruncatePrunedTree
(boolean val) getTruncatePrunedTree SEE: getTruncatePrunedTreevoid
setUse1SERule
(boolean val) getUse1SERule SEE: getUse1SERulevoid
setUseSurrogates
(boolean val) getUseSurrogates SEE: getUseSurrogatesMethods 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
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Field Details
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PREDICT_AUTO
public static final int PREDICT_AUTO- See Also:
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PREDICT_SUM
public static final int PREDICT_SUM- See Also:
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PREDICT_MAX_VOTE
public static final int PREDICT_MAX_VOTE- See Also:
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PREDICT_MASK
public static final int PREDICT_MASK- See Also:
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Constructor Details
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DTrees
protected DTrees(long addr)
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Method Details
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__fromPtr__
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getMaxCategories
public int getMaxCategories()SEE: setMaxCategories- Returns:
- automatically generated
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setMaxCategories
public void setMaxCategories(int val) getMaxCategories SEE: getMaxCategories- Parameters:
val
- automatically generated
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getMaxDepth
public int getMaxDepth()SEE: setMaxDepth- Returns:
- automatically generated
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setMaxDepth
public void setMaxDepth(int val) getMaxDepth SEE: getMaxDepth- Parameters:
val
- automatically generated
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getMinSampleCount
public int getMinSampleCount()SEE: setMinSampleCount- Returns:
- automatically generated
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setMinSampleCount
public void setMinSampleCount(int val) getMinSampleCount SEE: getMinSampleCount- Parameters:
val
- automatically generated
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getCVFolds
public int getCVFolds()SEE: setCVFolds- Returns:
- automatically generated
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setCVFolds
public void setCVFolds(int val) getCVFolds SEE: getCVFolds- Parameters:
val
- automatically generated
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getUseSurrogates
public boolean getUseSurrogates()SEE: setUseSurrogates- Returns:
- automatically generated
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setUseSurrogates
public void setUseSurrogates(boolean val) getUseSurrogates SEE: getUseSurrogates- Parameters:
val
- automatically generated
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getUse1SERule
public boolean getUse1SERule()SEE: setUse1SERule- Returns:
- automatically generated
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setUse1SERule
public void setUse1SERule(boolean val) getUse1SERule SEE: getUse1SERule- Parameters:
val
- automatically generated
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getTruncatePrunedTree
public boolean getTruncatePrunedTree()SEE: setTruncatePrunedTree- Returns:
- automatically generated
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setTruncatePrunedTree
public void setTruncatePrunedTree(boolean val) getTruncatePrunedTree SEE: getTruncatePrunedTree- Parameters:
val
- automatically generated
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getRegressionAccuracy
public float getRegressionAccuracy()SEE: setRegressionAccuracy- Returns:
- automatically generated
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setRegressionAccuracy
public void setRegressionAccuracy(float val) getRegressionAccuracy SEE: getRegressionAccuracy- Parameters:
val
- automatically generated
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getPriors
SEE: setPriors- Returns:
- automatically generated
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setPriors
getPriors SEE: getPriors- Parameters:
val
- automatically generated
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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
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
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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finalize
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