//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.ml;
import org.opencv.core.Mat;
import org.opencv.core.TermCriteria;
import org.opencv.ml.LogisticRegression;
import org.opencv.ml.StatModel;
// C++: class LogisticRegression
/**
* Implements Logistic Regression classifier.
*
* SEE: REF: ml_intro_lr
*/
public class LogisticRegression extends StatModel {
protected LogisticRegression(long addr) { super(addr); }
// internal usage only
public static LogisticRegression __fromPtr__(long addr) { return new LogisticRegression(addr); }
// C++: enum Methods (cv.ml.LogisticRegression.Methods)
public static final int
BATCH = 0,
MINI_BATCH = 1;
// C++: enum RegKinds (cv.ml.LogisticRegression.RegKinds)
public static final int
REG_DISABLE = -1,
REG_L1 = 0,
REG_L2 = 1;
//
// C++: double cv::ml::LogisticRegression::getLearningRate()
//
/**
* SEE: setLearningRate
* @return automatically generated
*/
public double getLearningRate() {
return getLearningRate_0(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setLearningRate(double val)
//
/**
* getLearningRate SEE: getLearningRate
* @param val automatically generated
*/
public void setLearningRate(double val) {
setLearningRate_0(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getIterations()
//
/**
* SEE: setIterations
* @return automatically generated
*/
public int getIterations() {
return getIterations_0(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setIterations(int val)
//
/**
* getIterations SEE: getIterations
* @param val automatically generated
*/
public void setIterations(int val) {
setIterations_0(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getRegularization()
//
/**
* SEE: setRegularization
* @return automatically generated
*/
public int getRegularization() {
return getRegularization_0(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setRegularization(int val)
//
/**
* getRegularization SEE: getRegularization
* @param val automatically generated
*/
public void setRegularization(int val) {
setRegularization_0(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getTrainMethod()
//
/**
* SEE: setTrainMethod
* @return automatically generated
*/
public int getTrainMethod() {
return getTrainMethod_0(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setTrainMethod(int val)
//
/**
* getTrainMethod SEE: getTrainMethod
* @param val automatically generated
*/
public void setTrainMethod(int val) {
setTrainMethod_0(nativeObj, val);
}
//
// C++: int cv::ml::LogisticRegression::getMiniBatchSize()
//
/**
* SEE: setMiniBatchSize
* @return automatically generated
*/
public int getMiniBatchSize() {
return getMiniBatchSize_0(nativeObj);
}
//
// C++: void cv::ml::LogisticRegression::setMiniBatchSize(int val)
//
/**
* getMiniBatchSize SEE: getMiniBatchSize
* @param val automatically generated
*/
public void setMiniBatchSize(int val) {
setMiniBatchSize_0(nativeObj, val);
}
//
// C++: TermCriteria cv::ml::LogisticRegression::getTermCriteria()
//
/**
* SEE: setTermCriteria
* @return automatically generated
*/
public TermCriteria getTermCriteria() {
return new TermCriteria(getTermCriteria_0(nativeObj));
}
//
// C++: void cv::ml::LogisticRegression::setTermCriteria(TermCriteria val)
//
/**
* getTermCriteria SEE: getTermCriteria
* @param val automatically generated
*/
public void setTermCriteria(TermCriteria val) {
setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
}
//
// C++: float cv::ml::LogisticRegression::predict(Mat samples, Mat& results = Mat(), int flags = 0)
//
/**
* Predicts responses for input samples and returns a float type.
*
* @param samples The input data for the prediction algorithm. Matrix [m x n], where each row
* contains variables (features) of one object being classified. Should have data type CV_32F.
* @param results Predicted labels as a column matrix of type CV_32S.
* @param flags Not used.
* @return automatically generated
*/
public float predict(Mat samples, Mat results, int flags) {
return predict_0(nativeObj, samples.nativeObj, results.nativeObj, flags);
}
/**
* Predicts responses for input samples and returns a float type.
*
* @param samples The input data for the prediction algorithm. Matrix [m x n], where each row
* contains variables (features) of one object being classified. Should have data type CV_32F.
* @param results Predicted labels as a column matrix of type CV_32S.
* @return automatically generated
*/
public float predict(Mat samples, Mat results) {
return predict_1(nativeObj, samples.nativeObj, results.nativeObj);
}
/**
* Predicts responses for input samples and returns a float type.
*
* @param samples The input data for the prediction algorithm. Matrix [m x n], where each row
* contains variables (features) of one object being classified. Should have data type CV_32F.
* @return automatically generated
*/
public float predict(Mat samples) {
return predict_2(nativeObj, samples.nativeObj);
}
//
// C++: Mat cv::ml::LogisticRegression::get_learnt_thetas()
//
/**
* This function returns the trained parameters arranged across rows.
*
* For a two class classification problem, it returns a row matrix. It returns learnt parameters of
* the Logistic Regression as a matrix of type CV_32F.
* @return automatically generated
*/
public Mat get_learnt_thetas() {
return new Mat(get_learnt_thetas_0(nativeObj));
}
//
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::create()
//
/**
* Creates empty model.
*
* Creates Logistic Regression model with parameters given.
* @return automatically generated
*/
public static LogisticRegression create() {
return LogisticRegression.__fromPtr__(create_0());
}
//
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::load(String filepath, String nodeName = String())
//
/**
* Loads and creates a serialized LogisticRegression from a file
*
* Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
* Load the LogisticRegression from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized LogisticRegression
* @param nodeName name of node containing the classifier
* @return automatically generated
*/
public static LogisticRegression load(String filepath, String nodeName) {
return LogisticRegression.__fromPtr__(load_0(filepath, nodeName));
}
/**
* Loads and creates a serialized LogisticRegression from a file
*
* Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
* Load the LogisticRegression from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized LogisticRegression
* @return automatically generated
*/
public static LogisticRegression load(String filepath) {
return LogisticRegression.__fromPtr__(load_1(filepath));
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: double cv::ml::LogisticRegression::getLearningRate()
private static native double getLearningRate_0(long nativeObj);
// C++: void cv::ml::LogisticRegression::setLearningRate(double val)
private static native void setLearningRate_0(long nativeObj, double val);
// C++: int cv::ml::LogisticRegression::getIterations()
private static native int getIterations_0(long nativeObj);
// C++: void cv::ml::LogisticRegression::setIterations(int val)
private static native void setIterations_0(long nativeObj, int val);
// C++: int cv::ml::LogisticRegression::getRegularization()
private static native int getRegularization_0(long nativeObj);
// C++: void cv::ml::LogisticRegression::setRegularization(int val)
private static native void setRegularization_0(long nativeObj, int val);
// C++: int cv::ml::LogisticRegression::getTrainMethod()
private static native int getTrainMethod_0(long nativeObj);
// C++: void cv::ml::LogisticRegression::setTrainMethod(int val)
private static native void setTrainMethod_0(long nativeObj, int val);
// C++: int cv::ml::LogisticRegression::getMiniBatchSize()
private static native int getMiniBatchSize_0(long nativeObj);
// C++: void cv::ml::LogisticRegression::setMiniBatchSize(int val)
private static native void setMiniBatchSize_0(long nativeObj, int val);
// C++: TermCriteria cv::ml::LogisticRegression::getTermCriteria()
private static native double[] getTermCriteria_0(long nativeObj);
// C++: void cv::ml::LogisticRegression::setTermCriteria(TermCriteria val)
private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
// C++: float cv::ml::LogisticRegression::predict(Mat samples, Mat& results = Mat(), int flags = 0)
private static native float predict_0(long nativeObj, long samples_nativeObj, long results_nativeObj, int flags);
private static native float predict_1(long nativeObj, long samples_nativeObj, long results_nativeObj);
private static native float predict_2(long nativeObj, long samples_nativeObj);
// C++: Mat cv::ml::LogisticRegression::get_learnt_thetas()
private static native long get_learnt_thetas_0(long nativeObj);
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::create()
private static native long create_0();
// C++: static Ptr_LogisticRegression cv::ml::LogisticRegression::load(String filepath, String nodeName = String())
private static native long load_0(String filepath, String nodeName);
private static native long load_1(String filepath);
// native support for java finalize()
private static native void delete(long nativeObj);
}
