Package org.opencv.ml
Class RTrees
- 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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- org.opencv.ml.RTrees
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public class RTrees extends DTrees
The class implements the random forest predictor. SEE: REF: ml_intro_rtrees
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Field Summary
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Fields inherited from class org.opencv.ml.DTrees
PREDICT_AUTO, PREDICT_MASK, PREDICT_MAX_VOTE, PREDICT_SUM
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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 protected
RTrees(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static RTrees
__fromPtr__(long addr)
static RTrees
create()
Creates the empty model.protected void
finalize()
int
getActiveVarCount()
SEE: setActiveVarCountboolean
getCalculateVarImportance()
SEE: setCalculateVarImportancedouble
getOOBError()
Returns the OOB error value, computed at the training stage when calcOOBError is set to true.TermCriteria
getTermCriteria()
SEE: setTermCriteriaMat
getVarImportance()
Returns the variable importance array.void
getVotes(Mat samples, Mat results, int flags)
Returns the result of each individual tree in the forest.static RTrees
load(java.lang.String filepath)
Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk.static RTrees
load(java.lang.String filepath, java.lang.String nodeName)
Loads and creates a serialized RTree from a file Use RTree::save to serialize and store an RTree to disk.void
setActiveVarCount(int val)
getActiveVarCount SEE: getActiveVarCountvoid
setCalculateVarImportance(boolean val)
getCalculateVarImportance SEE: getCalculateVarImportancevoid
setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteria-
Methods inherited from class org.opencv.ml.DTrees
getCVFolds, getMaxCategories, getMaxDepth, getMinSampleCount, getPriors, getRegressionAccuracy, getTruncatePrunedTree, getUse1SERule, getUseSurrogates, setCVFolds, setMaxCategories, setMaxDepth, setMinSampleCount, setPriors, setRegressionAccuracy, setTruncatePrunedTree, setUse1SERule, setUseSurrogates
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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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Method Detail
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__fromPtr__
public static RTrees __fromPtr__(long addr)
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getCalculateVarImportance
public boolean getCalculateVarImportance()
SEE: setCalculateVarImportance- Returns:
- automatically generated
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setCalculateVarImportance
public void setCalculateVarImportance(boolean val)
getCalculateVarImportance SEE: getCalculateVarImportance- Parameters:
val
- automatically generated
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getActiveVarCount
public int getActiveVarCount()
SEE: setActiveVarCount- Returns:
- automatically generated
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setActiveVarCount
public void setActiveVarCount(int val)
getActiveVarCount SEE: getActiveVarCount- Parameters:
val
- automatically generated
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getTermCriteria
public TermCriteria getTermCriteria()
SEE: setTermCriteria- Returns:
- automatically generated
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setTermCriteria
public void setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteria- Parameters:
val
- automatically generated
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getVarImportance
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.- Returns:
- automatically generated
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getVotes
public void getVotes(Mat samples, 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.
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getOOBError
public double getOOBError()
Returns the OOB error value, computed at the training stage when calcOOBError is set to true. If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.- Returns:
- automatically generated
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create
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.- Returns:
- automatically generated
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load
public static RTrees load(java.lang.String filepath, java.lang.String nodeName)
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 RTreenodeName
- name of node containing the classifier- Returns:
- automatically generated
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load
public static RTrees load(java.lang.String filepath)
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- Returns:
- automatically generated
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