org.opencv.ml.CvKNearest Maven / Gradle / Ivy
//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
// C++: class CvKNearest
/**
* The class implements K-Nearest Neighbors model as described in the beginning
* of this section.
*
* @see org.opencv.ml.CvKNearest : public CvStatModel
*/
public class CvKNearest extends CvStatModel {
protected CvKNearest(long addr) { super(addr); }
//
// C++: CvKNearest::CvKNearest()
//
/**
* Default and training constructors.
*
* See "CvKNearest.train" for additional parameters descriptions.
*
* @see org.opencv.ml.CvKNearest.CvKNearest
*/
public CvKNearest()
{
super( CvKNearest_0() );
return;
}
//
// C++: CvKNearest::CvKNearest(Mat trainData, Mat responses, Mat sampleIdx = cv::Mat(), bool isRegression = false, int max_k = 32)
//
/**
* Default and training constructors.
*
* See "CvKNearest.train" for additional parameters descriptions.
*
* @param trainData a trainData
* @param responses a responses
* @param sampleIdx a sampleIdx
* @param isRegression a isRegression
* @param max_k a max_k
*
* @see org.opencv.ml.CvKNearest.CvKNearest
*/
public CvKNearest(Mat trainData, Mat responses, Mat sampleIdx, boolean isRegression, int max_k)
{
super( CvKNearest_1(trainData.nativeObj, responses.nativeObj, sampleIdx.nativeObj, isRegression, max_k) );
return;
}
/**
* Default and training constructors.
*
* See "CvKNearest.train" for additional parameters descriptions.
*
* @param trainData a trainData
* @param responses a responses
*
* @see org.opencv.ml.CvKNearest.CvKNearest
*/
public CvKNearest(Mat trainData, Mat responses)
{
super( CvKNearest_2(trainData.nativeObj, responses.nativeObj) );
return;
}
//
// C++: float CvKNearest::find_nearest(Mat samples, int k, Mat& results, Mat& neighborResponses, Mat& dists)
//
/**
* Finds the neighbors and predicts responses for input vectors.
*
* 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.
*
* @param samples Input samples stored by rows. It is a single-precision
* floating-point matrix of number_of_samples x number_of_features
* size.
* @param k Number of used nearest neighbors. It must satisfy constraint: k
* <= "CvKNearest.get_max_k".
* @param 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.
* @param neighborResponses Optional output values for corresponding
* neighbors. It is a single-precision floating-point matrix of
* number_of_samples x k size.
* @param dists a dists
*
* @see org.opencv.ml.CvKNearest.find_nearest
*/
public float find_nearest(Mat samples, int k, Mat results, Mat neighborResponses, Mat dists)
{
float retVal = find_nearest_0(nativeObj, samples.nativeObj, k, results.nativeObj, neighborResponses.nativeObj, dists.nativeObj);
return retVal;
}
//
// C++: bool CvKNearest::train(Mat trainData, Mat responses, Mat sampleIdx = cv::Mat(), bool isRegression = false, int maxK = 32, bool updateBase = false)
//
/**
* Trains the model.
*
* The method trains the K-Nearest model. It follows the conventions of the
* generic "CvStatModel.train" approach with the following limitations:
*
* - Only
CV_ROW_SAMPLE data layout is supported.
* - Input variables are all ordered.
*
- Output variables can be either categorical (
is_regression=false)
* or ordered (is_regression=true).
* - Variable subsets (
var_idx) and missing measurements are
* not supported.
*
*
* @param trainData a trainData
* @param responses a responses
* @param sampleIdx a sampleIdx
* @param isRegression Type of the problem: true for regression and
* false for classification.
* @param maxK Number of maximum neighbors that may be passed to the method
* "CvKNearest.find_nearest".
* @param updateBase Specifies whether the model is trained from scratch
* (update_base=false), or it is updated using the new training
* data (update_base=true). In the latter case, the parameter
* maxK must not be larger than the original value.
*
* @see org.opencv.ml.CvKNearest.train
*/
public boolean train(Mat trainData, Mat responses, Mat sampleIdx, boolean isRegression, int maxK, boolean updateBase)
{
boolean retVal = train_0(nativeObj, trainData.nativeObj, responses.nativeObj, sampleIdx.nativeObj, isRegression, maxK, updateBase);
return retVal;
}
/**
* Trains the model.
*
* The method trains the K-Nearest model. It follows the conventions of the
* generic "CvStatModel.train" approach with the following limitations:
*
* - Only
CV_ROW_SAMPLE data layout is supported.
* - Input variables are all ordered.
*
- Output variables can be either categorical (
is_regression=false)
* or ordered (is_regression=true).
* - Variable subsets (
var_idx) and missing measurements are
* not supported.
*
*
* @param trainData a trainData
* @param responses a responses
*
* @see org.opencv.ml.CvKNearest.train
*/
public boolean train(Mat trainData, Mat responses)
{
boolean retVal = train_1(nativeObj, trainData.nativeObj, responses.nativeObj);
return retVal;
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: CvKNearest::CvKNearest()
private static native long CvKNearest_0();
// C++: CvKNearest::CvKNearest(Mat trainData, Mat responses, Mat sampleIdx = cv::Mat(), bool isRegression = false, int max_k = 32)
private static native long CvKNearest_1(long trainData_nativeObj, long responses_nativeObj, long sampleIdx_nativeObj, boolean isRegression, int max_k);
private static native long CvKNearest_2(long trainData_nativeObj, long responses_nativeObj);
// C++: float CvKNearest::find_nearest(Mat samples, int k, Mat& results, Mat& neighborResponses, Mat& dists)
private static native float find_nearest_0(long nativeObj, long samples_nativeObj, int k, long results_nativeObj, long neighborResponses_nativeObj, long dists_nativeObj);
// C++: bool CvKNearest::train(Mat trainData, Mat responses, Mat sampleIdx = cv::Mat(), bool isRegression = false, int maxK = 32, bool updateBase = false)
private static native boolean train_0(long nativeObj, long trainData_nativeObj, long responses_nativeObj, long sampleIdx_nativeObj, boolean isRegression, int maxK, boolean updateBase);
private static native boolean train_1(long nativeObj, long trainData_nativeObj, long responses_nativeObj);
// native support for java finalize()
private static native void delete(long nativeObj);
}