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++:  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++:  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);
  
  }
  