//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
// C++: class KNearest
/**
* The class implements K-Nearest Neighbors model
*
* SEE: REF: ml_intro_knn
*/
public class KNearest extends StatModel {
protected KNearest(long addr) { super(addr); }
// internal usage only
public static KNearest __fromPtr__(long addr) { return new KNearest(addr); }
// C++: enum Types (cv.ml.KNearest.Types)
public static final int
BRUTE_FORCE = 1,
KDTREE = 2;
//
// C++: int cv::ml::KNearest::getDefaultK()
//
/**
* SEE: setDefaultK
* @return automatically generated
*/
public int getDefaultK() {
return getDefaultK_0(nativeObj);
}
//
// C++: void cv::ml::KNearest::setDefaultK(int val)
//
/**
* getDefaultK SEE: getDefaultK
* @param val automatically generated
*/
public void setDefaultK(int val) {
setDefaultK_0(nativeObj, val);
}
//
// C++: bool cv::ml::KNearest::getIsClassifier()
//
/**
* SEE: setIsClassifier
* @return automatically generated
*/
public boolean getIsClassifier() {
return getIsClassifier_0(nativeObj);
}
//
// C++: void cv::ml::KNearest::setIsClassifier(bool val)
//
/**
* getIsClassifier SEE: getIsClassifier
* @param val automatically generated
*/
public void setIsClassifier(boolean val) {
setIsClassifier_0(nativeObj, val);
}
//
// C++: int cv::ml::KNearest::getEmax()
//
/**
* SEE: setEmax
* @return automatically generated
*/
public int getEmax() {
return getEmax_0(nativeObj);
}
//
// C++: void cv::ml::KNearest::setEmax(int val)
//
/**
* getEmax SEE: getEmax
* @param val automatically generated
*/
public void setEmax(int val) {
setEmax_0(nativeObj, val);
}
//
// C++: int cv::ml::KNearest::getAlgorithmType()
//
/**
* SEE: setAlgorithmType
* @return automatically generated
*/
public int getAlgorithmType() {
return getAlgorithmType_0(nativeObj);
}
//
// C++: void cv::ml::KNearest::setAlgorithmType(int val)
//
/**
* getAlgorithmType SEE: getAlgorithmType
* @param val automatically generated
*/
public void setAlgorithmType(int val) {
setAlgorithmType_0(nativeObj, val);
}
//
// C++: float cv::ml::KNearest::findNearest(Mat samples, int k, Mat& results, Mat& neighborResponses = Mat(), Mat& dist = Mat())
//
/**
* 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 <number_of_samples> * 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 <number_of_samples>} elements.
* @param neighborResponses Optional output values for corresponding neighbors. It is a single-
* precision floating-point matrix of {@code <number_of_samples> * 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 <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.
* @return automatically generated
*/
public float findNearest(Mat samples, int k, Mat results, Mat neighborResponses, Mat dist) {
return findNearest_0(nativeObj, samples.nativeObj, k, results.nativeObj, neighborResponses.nativeObj, dist.nativeObj);
}
/**
* 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 <number_of_samples> * 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 <number_of_samples>} elements.
* @param neighborResponses Optional output values for corresponding neighbors. It is a single-
* precision floating-point matrix of {@code <number_of_samples> * k} size.
* is a single-precision floating-point matrix of {@code <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.
* @return automatically generated
*/
public float findNearest(Mat samples, int k, Mat results, Mat neighborResponses) {
return findNearest_1(nativeObj, samples.nativeObj, k, results.nativeObj, neighborResponses.nativeObj);
}
/**
* 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 <number_of_samples> * 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 <number_of_samples>} elements.
* precision floating-point matrix of {@code <number_of_samples> * k} size.
* is a single-precision floating-point matrix of {@code <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.
* @return automatically generated
*/
public float findNearest(Mat samples, int k, Mat results) {
return findNearest_2(nativeObj, samples.nativeObj, k, results.nativeObj);
}
//
// C++: static Ptr_KNearest cv::ml::KNearest::create()
//
/**
* Creates the empty model
*
* The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
* @return automatically generated
*/
public static KNearest create() {
return KNearest.__fromPtr__(create_0());
}
//
// C++: static Ptr_KNearest cv::ml::KNearest::load(String filepath)
//
/**
* 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
* @return automatically generated
*/
public static KNearest load(String filepath) {
return KNearest.__fromPtr__(load_0(filepath));
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: int cv::ml::KNearest::getDefaultK()
private static native int getDefaultK_0(long nativeObj);
// C++: void cv::ml::KNearest::setDefaultK(int val)
private static native void setDefaultK_0(long nativeObj, int val);
// C++: bool cv::ml::KNearest::getIsClassifier()
private static native boolean getIsClassifier_0(long nativeObj);
// C++: void cv::ml::KNearest::setIsClassifier(bool val)
private static native void setIsClassifier_0(long nativeObj, boolean val);
// C++: int cv::ml::KNearest::getEmax()
private static native int getEmax_0(long nativeObj);
// C++: void cv::ml::KNearest::setEmax(int val)
private static native void setEmax_0(long nativeObj, int val);
// C++: int cv::ml::KNearest::getAlgorithmType()
private static native int getAlgorithmType_0(long nativeObj);
// C++: void cv::ml::KNearest::setAlgorithmType(int val)
private static native void setAlgorithmType_0(long nativeObj, int val);
// C++: float cv::ml::KNearest::findNearest(Mat samples, int k, Mat& results, Mat& neighborResponses = Mat(), Mat& dist = Mat())
private static native float findNearest_0(long nativeObj, long samples_nativeObj, int k, long results_nativeObj, long neighborResponses_nativeObj, long dist_nativeObj);
private static native float findNearest_1(long nativeObj, long samples_nativeObj, int k, long results_nativeObj, long neighborResponses_nativeObj);
private static native float findNearest_2(long nativeObj, long samples_nativeObj, int k, long results_nativeObj);
// C++: static Ptr_KNearest cv::ml::KNearest::create()
private static native long create_0();
// C++: static Ptr_KNearest cv::ml::KNearest::load(String filepath)
private static native long load_0(String filepath);
// native support for java finalize()
private static native void delete(long nativeObj);
}
