org.bytedeco.opencv.opencv_ml.KNearest Maven / Gradle / Ivy
// Targeted by JavaCPP version 1.5.7: DO NOT EDIT THIS FILE
package org.bytedeco.opencv.opencv_ml;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;
import static org.bytedeco.javacpp.presets.javacpp.*;
import static org.bytedeco.openblas.global.openblas_nolapack.*;
import static org.bytedeco.openblas.global.openblas.*;
import org.bytedeco.opencv.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_ml.*;
/****************************************************************************************\
* K-Nearest Neighbour Classifier *
\****************************************************************************************/
/** \brief The class implements K-Nearest Neighbors model
@see \ref ml_intro_knn
*/
@Namespace("cv::ml") @Properties(inherit = org.bytedeco.opencv.presets.opencv_ml.class)
public class KNearest extends StatModel {
static { Loader.load(); }
/** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
public KNearest(Pointer p) { super(p); }
/** Default number of neighbors to use in predict method. */
/** @see setDefaultK */
public native int getDefaultK();
/** \copybrief getDefaultK @see getDefaultK */
public native void setDefaultK(int val);
/** Whether classification or regression model should be trained. */
/** @see setIsClassifier */
public native @Cast("bool") boolean getIsClassifier();
/** \copybrief getIsClassifier @see getIsClassifier */
public native void setIsClassifier(@Cast("bool") boolean val);
/** Parameter for KDTree implementation. */
/** @see setEmax */
public native int getEmax();
/** \copybrief getEmax @see getEmax */
public native void setEmax(int val);
/** %Algorithm type, one of KNearest::Types. */
/** @see setAlgorithmType */
public native int getAlgorithmType();
/** \copybrief getAlgorithmType @see getAlgorithmType */
public native void setAlgorithmType(int val);
/** \brief Finds the neighbors and predicts responses for input vectors.
@param samples Input samples stored by rows. It is a single-precision floating-point matrix of
{@code * k} size.
@param k Number of used nearest neighbors. Should be greater than 1.
@param results Vector with results of prediction (regression or classification) for each input
sample. It is a single-precision floating-point vector with {@code } elements.
@param neighborResponses Optional output values for corresponding neighbors. It is a single-
precision floating-point matrix of {@code * k} size.
@param dist Optional output distances from the input vectors to the corresponding neighbors. It
is a single-precision floating-point matrix of {@code * 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.
*/
public native float findNearest( @ByVal Mat samples, int k,
@ByVal Mat results,
@ByVal(nullValue = "cv::OutputArray(cv::noArray())") Mat neighborResponses,
@ByVal(nullValue = "cv::OutputArray(cv::noArray())") Mat dist );
public native float findNearest( @ByVal Mat samples, int k,
@ByVal Mat results );
public native float findNearest( @ByVal UMat samples, int k,
@ByVal UMat results,
@ByVal(nullValue = "cv::OutputArray(cv::noArray())") UMat neighborResponses,
@ByVal(nullValue = "cv::OutputArray(cv::noArray())") UMat dist );
public native float findNearest( @ByVal UMat samples, int k,
@ByVal UMat results );
public native float findNearest( @ByVal GpuMat samples, int k,
@ByVal GpuMat results,
@ByVal(nullValue = "cv::OutputArray(cv::noArray())") GpuMat neighborResponses,
@ByVal(nullValue = "cv::OutputArray(cv::noArray())") GpuMat dist );
public native float findNearest( @ByVal GpuMat samples, int k,
@ByVal GpuMat results );
/** \brief Implementations of KNearest algorithm
*/
/** enum cv::ml::KNearest::Types */
public static final int
BRUTE_FORCE = 1,
KDTREE = 2;
/** \brief Creates the empty model
The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
*/
public static native @Ptr KNearest create();
/** \brief Loads and creates a serialized knearest from a file
*
* Use KNearest::save to serialize and store an KNearest to disk.
* Load the KNearest from this file again, by calling this function with the path to the file.
*
* @param filepath path to serialized KNearest
*/
public static native @Ptr KNearest load(@Str BytePointer filepath);
public static native @Ptr KNearest load(@Str String filepath);
}