@Namespace(value="cv::ml") public static class opencv_ml.NormalBayesClassifier extends opencv_ml.StatModel
\sa \ref ml_intro_bayes
Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocatorCOMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL| Constructor and Description |
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NormalBayesClassifier(Pointer p)
Pointer cast constructor.
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| Modifier and Type | Method and Description |
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static opencv_ml.NormalBayesClassifier |
create()
Creates empty model
Use StatModel::train to train the model after creation.
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float |
predictProb(opencv_core.Mat inputs,
opencv_core.Mat outputs,
opencv_core.Mat outputProbs) |
float |
predictProb(opencv_core.Mat inputs,
opencv_core.Mat outputs,
opencv_core.Mat outputProbs,
int flags)
\brief Predicts the response for sample(s).
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float |
predictProb(opencv_core.UMat inputs,
opencv_core.UMat outputs,
opencv_core.UMat outputProbs) |
float |
predictProb(opencv_core.UMat inputs,
opencv_core.UMat outputs,
opencv_core.UMat outputProbs,
int flags) |
calcError, calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, predict, train, train, train, trainloadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVMclear, getDefaultName, position, read, save, save, writeaddress, asBuffer, asByteBuffer, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, physicalBytes, position, put, realloc, setNull, sizeof, toString, totalBytes, withDeallocator, zeropublic NormalBayesClassifier(Pointer p)
Pointer.Pointer(Pointer).public float predictProb(@ByVal opencv_core.Mat inputs, @ByVal opencv_core.Mat outputs, @ByVal opencv_core.Mat outputProbs, int flags)
The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output vector outputs. The predicted class for a single input vector is returned by the method. The vector outputProbs contains the output probabilities corresponding to each element of result.
public float predictProb(@ByVal opencv_core.Mat inputs, @ByVal opencv_core.Mat outputs, @ByVal opencv_core.Mat outputProbs)
public float predictProb(@ByVal opencv_core.UMat inputs, @ByVal opencv_core.UMat outputs, @ByVal opencv_core.UMat outputProbs, int flags)
public float predictProb(@ByVal opencv_core.UMat inputs, @ByVal opencv_core.UMat outputs, @ByVal opencv_core.UMat outputProbs)
@opencv_core.Ptr public static opencv_ml.NormalBayesClassifier create()
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