@Namespace(value="cv::ml") @Properties(inherit=opencv_ml.class) public class NormalBayesClassifier extends StatModel
ml_intro_bayesPointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounterCOMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL| Constructor and Description |
|---|
NormalBayesClassifier(Pointer p)
Pointer cast constructor.
|
| Modifier and Type | Method and Description |
|---|---|
static NormalBayesClassifier |
create()
Creates empty model
Use StatModel::train to train the model after creation.
|
static NormalBayesClassifier |
load(BytePointer filepath) |
static NormalBayesClassifier |
load(BytePointer filepath,
BytePointer nodeName)
\brief Loads and creates a serialized NormalBayesClassifier from a file
Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
|
static NormalBayesClassifier |
load(String filepath) |
static NormalBayesClassifier |
load(String filepath,
String nodeName) |
float |
predictProb(GpuMat inputs,
GpuMat outputs,
GpuMat outputProbs) |
float |
predictProb(GpuMat inputs,
GpuMat outputs,
GpuMat outputProbs,
int flags) |
float |
predictProb(Mat inputs,
Mat outputs,
Mat outputProbs) |
float |
predictProb(Mat inputs,
Mat outputs,
Mat outputProbs,
int flags)
\brief Predicts the response for sample(s).
|
float |
predictProb(UMat inputs,
UMat outputs,
UMat outputProbs) |
float |
predictProb(UMat inputs,
UMat outputs,
UMat outputProbs,
int flags) |
calcError, calcError, calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, predict, predict, predict, train, 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, write, write, writeaddress, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zeropublic NormalBayesClassifier(Pointer p)
Pointer.Pointer(Pointer).public float predictProb(@ByVal Mat inputs, @ByVal Mat outputs, @ByVal 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 UMat inputs, @ByVal UMat outputs, @ByVal UMat outputProbs, int flags)
public float predictProb(@ByVal UMat inputs, @ByVal UMat outputs, @ByVal UMat outputProbs)
public float predictProb(@ByVal GpuMat inputs, @ByVal GpuMat outputs, @ByVal GpuMat outputProbs, int flags)
public float predictProb(@ByVal GpuMat inputs, @ByVal GpuMat outputs, @ByVal GpuMat outputProbs)
@opencv_core.Ptr public static NormalBayesClassifier create()
@opencv_core.Ptr public static NormalBayesClassifier load(@opencv_core.Str BytePointer filepath, @opencv_core.Str BytePointer nodeName)
filepath - path to serialized NormalBayesClassifiernodeName - name of node containing the classifier@opencv_core.Ptr public static NormalBayesClassifier load(@opencv_core.Str BytePointer filepath)
@opencv_core.Ptr public static NormalBayesClassifier load(@opencv_core.Str String filepath, @opencv_core.Str String nodeName)
@opencv_core.Ptr public static NormalBayesClassifier load(@opencv_core.Str String filepath)
Copyright © 2020. All rights reserved.