content: /*
   * Copyright (c) 1997 - 2016
   * Actelion Pharmaceuticals Ltd.
   * Gewerbestrasse 16
   * CH-4123 Allschwil, Switzerland
   *
   * All rights reserved.
   *
   * Redistribution and use in source and binary forms, with or without
   * modification, are permitted provided that the following conditions are met:
   *
   * 1. Redistributions of source code must retain the above copyright notice, this
   *    list of conditions and the following disclaimer.
   * 2. Redistributions in binary form must reproduce the above copyright notice,
   *    this list of conditions and the following disclaimer in the documentation
   *    and/or other materials provided with the distribution.
   * 3. Neither the name of the the copyright holder nor the
   *    names of its contributors may be used to endorse or promote products
   *    derived from this software without specific prior written permission.
   *
   * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
   * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
   * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
   * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
   * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
   * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
   * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
   * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
   * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
   *
   * @author Modest v. Korff
   */
  
  package com.actelion.research.calc.regression.svm;
  
  import com.actelion.research.calc.Matrix;
  import com.actelion.research.calc.statistics.ModelStatisticsOverview;
  import com.actelion.research.calc.statistics.StatisticsOverview;
  import com.actelion.research.util.Formatter;
  import com.actelion.research.util.datamodel.DoubleArray;
  import com.actelion.research.util.datamodel.ModelXYIndex;
  import com.actelion.research.calc.regression.ModelError;
  import com.actelion.research.calc.regression.knn.KNNRegression;
  import org.machinelearning.svm.libsvm.svm_parameter;
  
  /**
   * AnalyticalParameterCalculatorSVM
   * <p>Modest v. Korff</p>
   * <p>
   * Created by korffmo1 on 01.11.19.
   */
  public class AnalyticalParameterCalculatorSVM {
  
      public static ParameterSVM calculate(ModelXYIndex modelXYTrain){
  
  
          System.out.println("AnalyticalParameterCalculatorSVM");
  
          //
          // Parameter C
          //
  
          if(modelXYTrain.Y.cols() != 1) {
              throw new RuntimeException("Only single col allowed for Y!");
          }
  
  
          double rows = modelXYTrain.X.rows();
  
          System.out.println("Rows X train " + (int)rows);
  
          DoubleArray daY = new DoubleArray(modelXYTrain.Y.getColAsDouble(0));
  
          StatisticsOverview statisticsOverview = new StatisticsOverview(daY);
  
          ModelStatisticsOverview modelStatisticsOverviewY = statisticsOverview.evaluate();
  
          double sigmaY = modelStatisticsOverviewY.sdv;
  
          System.out.println("Sigma in y " + Formatter.format3(sigmaY));
  
          double avr = modelStatisticsOverviewY.avr;
  
          System.out.println("Average in y " + Formatter.format3(avr));
  
          //
          // Cherkassky, Vladimir, and Yunqian Ma.
          // "Practical selection of SVM parameters and noise estimation for SVM regression."
          // Neural networks 17.1 (2004): 113-126.
          // p5 equation 11
          double C = Math.max(Math.abs(avr-sigmaY*3), Math.abs(avr+sigmaY*3));
  
          System.out.println("C " + Formatter.format3(C));
  
          KNNRegression knnRegression = new KNNRegression();
  
          final int k = 3;
  
          knnRegression.setNeighbours(k);
  
          Matrix yHat = knnRegression.createModel(modelXYTrain);
  
          ModelError modelErrorKNN = ModelError.calculateError(modelXYTrain.Y, yHat);
  
          // Cherkassky, Vladimir, and Yunqian Ma.
          // "Practical selection of SVM parameters and noise estimation for SVM regression."
          // Neural networks 17.1 (2004): 113-126.
          // p18 equation 23
          double sigmaSquaredYHat = k/(k-1) * 1.0/rows * modelErrorKNN.errSumSquared;
  
          System.out.println("Sigma squared y hat " + Formatter.format3(sigmaSquaredYHat));
  
          // Cherkassky, Vladimir, and Yunqian Ma.
          // "Practical selection of SVM parameters and noise estimation for SVM regression."
          // Neural networks 17.1 (2004): 113-126.
          // p6 equation 14
          final double tau = 3;
  
          double epsilon = tau * Math.sqrt(sigmaSquaredYHat) * Math.sqrt(Math.log(rows)/rows);
  
          System.out.println("Epsilon " + Formatter.format3(epsilon));
  
          double gamma = 1.0 / modelXYTrain.X.cols();
  
          System.out.println("Gamma " + Formatter.format4(gamma));
  
          int kernelType = svm_parameter.RBF;
  
          svm_parameter svmParameter = SVMParameterHelper.regressionEpsilonSVR();
  
          svmParameter.kernel_type = kernelType;
  
          svmParameter.eps = epsilon;
  
          svmParameter.C = C;
  
          svmParameter.gamma = gamma;
  
          svmParameter.degree = 0;
  
          ParameterSVM parameterSVM = new ParameterSVM(svmParameter);
  
          return parameterSVM;
  
      }
  
  }
  