org.bytedeco.javacpp

Class opencv_ml.KNearest

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.opencv_core.Algorithm

org.bytedeco.javacpp.helper.opencv_ml.AbstractStatModel

org.bytedeco.javacpp.opencv_ml.StatModel

org.bytedeco.javacpp.opencv_ml.KNearest

All Implemented Interfaces:

AutoCloseable

Enclosing class:

opencv_ml

@Namespace(value="cv::ml")
public static class opencv_ml.KNearest
extends opencv_ml.StatModel

\brief The class implements K-Nearest Neighbors model

\sa \ref ml_intro_knn

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.Pointer.CustomDeallocator, org.bytedeco.javacpp.Pointer.Deallocator, org.bytedeco.javacpp.Pointer.NativeDeallocator

Field Summary

Fields

Modifier and Type	Field and Description
static int	BRUTE_FORCE enum cv::ml::KNearest::Types
static int	KDTREE enum cv::ml::KNearest::Types

Fields inherited from class org.bytedeco.javacpp.opencv_ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
KNearest(org.bytedeco.javacpp.Pointer p) Pointer cast constructor.

Method Summary

Modifier and Type	Method and Description
static opencv_ml.KNearest	create() \brief Creates the empty model
float	findNearest(opencv_core.GpuMat samples, int k, opencv_core.GpuMat results)
float	findNearest(opencv_core.GpuMat samples, int k, opencv_core.GpuMat results, opencv_core.GpuMat neighborResponses, opencv_core.GpuMat dist)
float	findNearest(opencv_core.Mat samples, int k, opencv_core.Mat results)
float	findNearest(opencv_core.Mat samples, int k, opencv_core.Mat results, opencv_core.Mat neighborResponses, opencv_core.Mat dist) \brief Finds the neighbors and predicts responses for input vectors.
float	findNearest(opencv_core.UMat samples, int k, opencv_core.UMat results)
float	findNearest(opencv_core.UMat samples, int k, opencv_core.UMat results, opencv_core.UMat neighborResponses, opencv_core.UMat dist)
int	getAlgorithmType()
int	getDefaultK()
int	getEmax()
boolean	getIsClassifier()
void	setAlgorithmType(int val) \copybrief getAlgorithmType @see getAlgorithmType
void	setDefaultK(int val) \copybrief getDefaultK @see getDefaultK
void	setEmax(int val) \copybrief getEmax @see getEmax
void	setIsClassifier(boolean val) \copybrief getIsClassifier @see getIsClassifier

Methods inherited from class org.bytedeco.javacpp.opencv_ml.StatModel

calcError, calcError, calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, predict, predict, predict, train, train, train, train, train

Methods inherited from class org.bytedeco.javacpp.helper.opencv_ml.AbstractStatModel

loadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVM

Methods inherited from class org.bytedeco.javacpp.opencv_core.Algorithm

clear, getDefaultName, position, read, save, save, write, write, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

BRUTE_FORCE

public static final int BRUTE_FORCE

enum cv::ml::KNearest::Types

See Also:

Constant Field Values

KDTREE

public static final int KDTREE

enum cv::ml::KNearest::Types

See Also:

Constant Field Values

Constructor Detail

KNearest

public KNearest(org.bytedeco.javacpp.Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

getDefaultK

public int getDefaultK()

See Also:

setDefaultK

setDefaultK

public void setDefaultK(int val)

\copybrief getDefaultK @see getDefaultK

getIsClassifier

@Cast(value="bool")
public boolean getIsClassifier()

See Also:

setIsClassifier

setIsClassifier

public void setIsClassifier(@Cast(value="bool")
                            boolean val)

\copybrief getIsClassifier @see getIsClassifier

getEmax

public int getEmax()

See Also:

setEmax

setEmax

public void setEmax(int val)

\copybrief getEmax @see getEmax

getAlgorithmType

public int getAlgorithmType()

See Also:

setAlgorithmType

setAlgorithmType

public void setAlgorithmType(int val)

\copybrief getAlgorithmType @see getAlgorithmType

findNearest

public float findNearest(@ByVal
                         opencv_core.Mat samples,
                         int k,
                         @ByVal
                         opencv_core.Mat results,
                         @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                         opencv_core.Mat neighborResponses,
                         @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                         opencv_core.Mat dist)

\brief Finds the neighbors and predicts responses for input vectors.

Parameters:

samples - Input samples stored by rows. It is a single-precision floating-point matrix of <number_of_samples> * k size.

k - Number of used nearest neighbors. Should be greater than 1.

results - Vector with results of prediction (regression or classification) for each input sample. It is a single-precision floating-point vector with <number_of_samples> elements.

neighborResponses - Optional output values for corresponding neighbors. It is a single- precision floating-point matrix of <number_of_samples> * k size.

dist - Optional output distances from the input vectors to the corresponding neighbors. It is a single-precision floating-point matrix of <number_of_samples> * k size.

For each input vector (a row of the matrix samples), the method finds the k nearest neighbors. In case of regression, the predicted result is a mean value of the particular vector's neighbor responses. In case of classification, the class is determined by voting.

For each input vector, the neighbors are sorted by their distances to the vector.

In case of C++ interface you can use output pointers to empty matrices and the function will allocate memory itself.

If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.

The function is parallelized with the TBB library.

findNearest

public float findNearest(@ByVal
                         opencv_core.Mat samples,
                         int k,
                         @ByVal
                         opencv_core.Mat results)

findNearest

public float findNearest(@ByVal
                         opencv_core.UMat samples,
                         int k,
                         @ByVal
                         opencv_core.UMat results,
                         @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                         opencv_core.UMat neighborResponses,
                         @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                         opencv_core.UMat dist)

findNearest

public float findNearest(@ByVal
                         opencv_core.UMat samples,
                         int k,
                         @ByVal
                         opencv_core.UMat results)

findNearest

public float findNearest(@ByVal
                         opencv_core.GpuMat samples,
                         int k,
                         @ByVal
                         opencv_core.GpuMat results,
                         @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                         opencv_core.GpuMat neighborResponses,
                         @ByVal(nullValue="cv::OutputArray(cv::noArray())")
                         opencv_core.GpuMat dist)

findNearest

public float findNearest(@ByVal
                         opencv_core.GpuMat samples,
                         int k,
                         @ByVal
                         opencv_core.GpuMat results)

create

@opencv_core.Ptr
public static opencv_ml.KNearest create()

\brief Creates the empty model

The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.