Package org.opencv.ml
Class StatModel
java.lang.Object
org.opencv.core.Algorithm
org.opencv.ml.StatModel
- Direct Known Subclasses:
ANN_MLP
,DTrees
,EM
,KNearest
,LogisticRegression
,NormalBayesClassifier
,SVM
,SVMSGD
Base class for statistical models in OpenCV ML.
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
static final int
static final int
static final int
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic StatModel
__fromPtr__
(long addr) float
Computes error on the training or test datasetboolean
empty()
Returns true if the Algorithm is empty (e.g.protected void
finalize()
int
Returns the number of variables in training samplesboolean
Returns true if the model is classifierboolean
Returns true if the model is trainedfloat
Predicts response(s) for the provided sample(s)float
Predicts response(s) for the provided sample(s)float
Predicts response(s) for the provided sample(s)boolean
Trains the statistical modelboolean
Trains the statistical modelboolean
Trains the statistical modelMethods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save
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Field Details
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UPDATE_MODEL
public static final int UPDATE_MODEL- See Also:
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RAW_OUTPUT
public static final int RAW_OUTPUT- See Also:
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COMPRESSED_INPUT
public static final int COMPRESSED_INPUT- See Also:
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PREPROCESSED_INPUT
public static final int PREPROCESSED_INPUT- See Also:
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Constructor Details
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StatModel
protected StatModel(long addr)
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Method Details
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__fromPtr__
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getVarCount
public int getVarCount()Returns the number of variables in training samples- Returns:
- automatically generated
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empty
public boolean empty()Description copied from class:Algorithm
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read -
isTrained
public boolean isTrained()Returns true if the model is trained- Returns:
- automatically generated
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isClassifier
public boolean isClassifier()Returns true if the model is classifier- Returns:
- automatically generated
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train
Trains the statistical model- Parameters:
trainData
- training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.flags
- optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).- Returns:
- automatically generated
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train
Trains the statistical model- Parameters:
trainData
- training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create. new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).- Returns:
- automatically generated
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train
Trains the statistical model- Parameters:
samples
- training sampleslayout
- See ml::SampleTypes.responses
- vector of responses associated with the training samples.- Returns:
- automatically generated
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calcError
Computes error on the training or test dataset- Parameters:
data
- the training datatest
- if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.resp
- the optional output responses. The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).- Returns:
- automatically generated
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predict
Predicts response(s) for the provided sample(s)- Parameters:
samples
- The input samples, floating-point matrixresults
- The optional output matrix of results.flags
- The optional flags, model-dependent. See cv::ml::StatModel::Flags.- Returns:
- automatically generated
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predict
Predicts response(s) for the provided sample(s)- Parameters:
samples
- The input samples, floating-point matrixresults
- The optional output matrix of results.- Returns:
- automatically generated
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predict
Predicts response(s) for the provided sample(s)- Parameters:
samples
- The input samples, floating-point matrix- Returns:
- automatically generated
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finalize
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