content: /*
   * Copyright (c) 1997 - 2016
   * Actelion Pharmaceuticals Ltd.
   * Gewerbestrasse 16
   * CH-4123 Allschwil, Switzerland
   *
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   * modification, are permitted provided that the following conditions are met:
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   * 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
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  package com.actelion.research.calc.regression.linear.pls;
  
  import com.actelion.research.calc.Matrix;
  import com.actelion.research.calc.regression.ModelError;
  import com.actelion.research.calc.statistics.median.MedianStatisticFunctions;
  import com.actelion.research.calc.statistics.median.ModelMedianDouble;
  import com.actelion.research.util.datamodel.ModelXY;
  import com.actelion.research.util.datamodel.ModelXYCrossValidation;
  import com.actelion.research.util.datamodel.ModelXYIndex;
  
  import java.text.DecimalFormat;
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * 
   * 
   * SimPLSLMOValidation
   * @author Modest von Korff
   * @version 1.0
   * Jul 26, 2011 MvK: Start implementation
   */
  @Deprecated // Replaced by LMOCV for multiple regression techniques
  public class SimPLSLMOValidation {
  	
      public static final DecimalFormat NF = new DecimalFormat("0.000");
  		
  	private int nRepetitions;
  	
  	private int nFactors;
  	
  	private List<ModelError> liModelErrorTest;
  	
  	private List<ModelError> liModelErrorTrain;
  	
  	private boolean centerData;
  
  	private ModelXYCrossValidation modelXYCrossValidation;
  	
  	public SimPLSLMOValidation(Matrix X, Matrix Y) {
  		
  		modelXYCrossValidation = new ModelXYCrossValidation(new ModelXY(X, Y));
  		
  	}
  
  	public void setFractionLeaveOut(double fracOut) {
  		modelXYCrossValidation.setFractionLeaveOut(fracOut);
  	}
  	
  	public void setNumRepetitions(int nRepetitions) {
  		this.nRepetitions = nRepetitions;
  	}
  	
  	public void setNumFactors(int nFactors) {
  		this.nFactors = nFactors;
  	}
  	
  	public void setCenterData(boolean centerData) {
  		this.centerData = centerData;
  	}
  
  	/**
  	 * 
  	 * @return median error of all repetitions.
  	 */
  	public double calculateMedianTestError() {
  		
  		liModelErrorTest = new ArrayList<ModelError>();
  		
  		liModelErrorTrain = new ArrayList<ModelError>();
  		
  		PLSRegressionModelCalculator rmc = new PLSRegressionModelCalculator();
  		
  		rmc.setCenterData(centerData);
  		
  		for (int i = 0; i < nRepetitions; i++) {
  			
  			modelXYCrossValidation.next();
  
  			ModelXYIndex modelXYIndex = new ModelXYIndex();
  
  			modelXYIndex.X = modelXYCrossValidation.getXtrain();
  			modelXYIndex.Y = modelXYCrossValidation.getYtrain();
  
  			Matrix yHat = rmc.createModel(modelXYIndex);
  
  			ModelError modelErrorTrain = ModelError.calculateError(modelXYIndex.Y, yHat);
  
  			ModelError modelErrorTest = rmc.calculateModelErrorTest(modelXYCrossValidation.getXtest(), modelXYCrossValidation.getYtest());
  			
  			liModelErrorTrain.add(modelErrorTrain);
  			
  			liModelErrorTest.add(modelErrorTest);
  			
  		}
  		
  		List<Double> liErrorTest = ModelError.getError(liModelErrorTest);
  		
  		ModelMedianDouble modelMedian = MedianStatisticFunctions.getMedianForDouble(liErrorTest);
  		
  		return modelMedian.median;
  		
  	}
  
  	
  	
  }
  