content: 
  //
  // This file is auto-generated. Please don't modify it!
  //
  package org.opencv.ml;
  
  import org.opencv.core.Mat;
  import org.opencv.core.TermCriteria;
  
  // C++: class LogisticRegression
  //javadoc: LogisticRegression
  public class LogisticRegression extends StatModel {
  
      protected LogisticRegression(long addr) {
          super(addr);
      }
  
  
      public static final int
              REG_DISABLE = -1,
              REG_L1 = 0,
              REG_L2 = 1,
              BATCH = 0,
              MINI_BATCH = 1;
  
  
      //
      // C++:  Mat get_learnt_thetas()
      //
  
      //javadoc: LogisticRegression::get_learnt_thetas()
      public Mat get_learnt_thetas() {
  
          Mat retVal = new Mat(get_learnt_thetas_0(nativeObj));
  
          return retVal;
      }
  
  
      //
      // C++: static Ptr_LogisticRegression create()
      //
  
      //javadoc: LogisticRegression::create()
      public static LogisticRegression create() {
  
          LogisticRegression retVal = new LogisticRegression(create_0());
  
          return retVal;
      }
  
  
      //
      // C++: static Ptr_LogisticRegression load(String filepath, String nodeName = String())
      //
  
      //javadoc: LogisticRegression::load(filepath, nodeName)
      public static LogisticRegression load(String filepath, String nodeName) {
  
          LogisticRegression retVal = new LogisticRegression(load_0(filepath, nodeName));
  
          return retVal;
      }
  
      //javadoc: LogisticRegression::load(filepath)
      public static LogisticRegression load(String filepath) {
  
          LogisticRegression retVal = new LogisticRegression(load_1(filepath));
  
          return retVal;
      }
  
  
      //
      // C++:  TermCriteria getTermCriteria()
      //
  
      //javadoc: LogisticRegression::getTermCriteria()
      public TermCriteria getTermCriteria() {
  
          TermCriteria retVal = new TermCriteria(getTermCriteria_0(nativeObj));
  
          return retVal;
      }
  
  
      //
      // C++:  double getLearningRate()
      //
  
      //javadoc: LogisticRegression::getLearningRate()
      public double getLearningRate() {
  
          double retVal = getLearningRate_0(nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  float predict(Mat samples, Mat& results = Mat(), int flags = 0)
      //
  
      //javadoc: LogisticRegression::predict(samples, results, flags)
      public float predict(Mat samples, Mat results, int flags) {
  
          float retVal = predict_0(nativeObj, samples.nativeObj, results.nativeObj, flags);
  
          return retVal;
      }
  
      //javadoc: LogisticRegression::predict(samples)
      public float predict(Mat samples) {
  
          float retVal = predict_1(nativeObj, samples.nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  int getIterations()
      //
  
      //javadoc: LogisticRegression::getIterations()
      public int getIterations() {
  
          int retVal = getIterations_0(nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  int getMiniBatchSize()
      //
  
      //javadoc: LogisticRegression::getMiniBatchSize()
      public int getMiniBatchSize() {
  
          int retVal = getMiniBatchSize_0(nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  int getRegularization()
      //
  
      //javadoc: LogisticRegression::getRegularization()
      public int getRegularization() {
  
          int retVal = getRegularization_0(nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  int getTrainMethod()
      //
  
      //javadoc: LogisticRegression::getTrainMethod()
      public int getTrainMethod() {
  
          int retVal = getTrainMethod_0(nativeObj);
  
          return retVal;
      }
  
  
      //
      // C++:  void setIterations(int val)
      //
  
      //javadoc: LogisticRegression::setIterations(val)
      public void setIterations(int val) {
  
          setIterations_0(nativeObj, val);
  
          return;
      }
  
  
      //
      // C++:  void setLearningRate(double val)
      //
  
      //javadoc: LogisticRegression::setLearningRate(val)
      public void setLearningRate(double val) {
  
          setLearningRate_0(nativeObj, val);
  
          return;
      }
  
  
      //
      // C++:  void setMiniBatchSize(int val)
      //
  
      //javadoc: LogisticRegression::setMiniBatchSize(val)
      public void setMiniBatchSize(int val) {
  
          setMiniBatchSize_0(nativeObj, val);
  
          return;
      }
  
  
      //
      // C++:  void setRegularization(int val)
      //
  
      //javadoc: LogisticRegression::setRegularization(val)
      public void setRegularization(int val) {
  
          setRegularization_0(nativeObj, val);
  
          return;
      }
  
  
      //
      // C++:  void setTermCriteria(TermCriteria val)
      //
  
      //javadoc: LogisticRegression::setTermCriteria(val)
      public void setTermCriteria(TermCriteria val) {
  
          setTermCriteria_0(nativeObj, val.type, val.maxCount, val.epsilon);
  
          return;
      }
  
  
      //
      // C++:  void setTrainMethod(int val)
      //
  
      //javadoc: LogisticRegression::setTrainMethod(val)
      public void setTrainMethod(int val) {
  
          setTrainMethod_0(nativeObj, val);
  
          return;
      }
  
  
      @Override
      protected void finalize() throws Throwable {
          delete(nativeObj);
      }
  
  
      // C++:  Mat get_learnt_thetas()
      private static native long get_learnt_thetas_0(long nativeObj);
  
      // C++: static Ptr_LogisticRegression create()
      private static native long create_0();
  
      // C++: static Ptr_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);
  
      // C++:  TermCriteria getTermCriteria()
      private static native double[] getTermCriteria_0(long nativeObj);
  
      // C++:  double getLearningRate()
      private static native double getLearningRate_0(long nativeObj);
  
      // C++:  float 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);
  
      // C++:  int getIterations()
      private static native int getIterations_0(long nativeObj);
  
      // C++:  int getMiniBatchSize()
      private static native int getMiniBatchSize_0(long nativeObj);
  
      // C++:  int getRegularization()
      private static native int getRegularization_0(long nativeObj);
  
      // C++:  int getTrainMethod()
      private static native int getTrainMethod_0(long nativeObj);
  
      // C++:  void setIterations(int val)
      private static native void setIterations_0(long nativeObj, int val);
  
      // C++:  void setLearningRate(double val)
      private static native void setLearningRate_0(long nativeObj, double val);
  
      // C++:  void setMiniBatchSize(int val)
      private static native void setMiniBatchSize_0(long nativeObj, int val);
  
      // C++:  void setRegularization(int val)
      private static native void setRegularization_0(long nativeObj, int val);
  
      // C++:  void setTermCriteria(TermCriteria val)
      private static native void setTermCriteria_0(long nativeObj, int val_type, int val_maxCount, double val_epsilon);
  
      // C++:  void setTrainMethod(int val)
      private static native void setTrainMethod_0(long nativeObj, int val);
  
      // native support for java finalize()
      private static native void delete(long nativeObj);
  
  }
  