content: /*
   * Copyright (c) 1997 - 2016
   * Actelion Pharmaceuticals Ltd.
   * Gewerbestrasse 16
   * CH-4123 Allschwil, Switzerland
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   *    names of its contributors may be used to endorse or promote products
   *    derived from this software without specific prior written permission.
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   * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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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.util.datamodel.ModelXYIndex;
  
  /**
   * RegressionModelCalculatorOptimumFactors
   * @author Modest von Korff
   * @version 1.0
   * Aug 14, 2015 MvK Start implementation
   */
  public class RegressionModelCalculatorOptimumFactors {
  
  	private static final double FRACTION_LEAVE_OUT = 0.25;
  	
  	private static final int LMO_REPETITIONS = 7;
  	
  	
  	private boolean centerData;
  	
  	private Matrix B;
  		
  	private Matrix YHat;
  	
  	private int factorsMin;
  	
  	/**
  	 * 
  	 */
  	public RegressionModelCalculatorOptimumFactors() {
  		centerData = false;
  	}
  	
  	
  	
  	/**
  	 * @param centerData the centerData to set
  	 */
  	public void setCenterData(boolean centerData) {
  		this.centerData = centerData;
  	}
  
  
  	/**
  	 * Calculates the PLS regression model for the given data set. 
  	 * A Leave Multiple Out estimator is used to assess the optimum number of factors.
  	 * The calculation starts with factorsStart factor up to factorsEnd.
  	 * @param dataXYTrain
  	 * @param factorsStart
  	 * @param factorsEnd
  	 * @return
  	 */
  	public ModelError calculateModel(ModelXYIndex dataXYTrain, int factorsStart, int factorsEnd){
  		
  		
  		SimPLSLMOValidation simPLSLMOValidation = new SimPLSLMOValidation(dataXYTrain.X, dataXYTrain.Y);
  				
  		simPLSLMOValidation.setFractionLeaveOut(FRACTION_LEAVE_OUT);
  		
  		simPLSLMOValidation.setNumRepetitions(LMO_REPETITIONS);
  		
  		double errTestMin = Integer.MAX_VALUE;
  		
  		factorsMin = 1;
  		
  		for (int i = factorsStart; i < factorsEnd+1; i++) {
  			
  			simPLSLMOValidation.setNumFactors(i);
  			
  			double errTest = simPLSLMOValidation.calculateMedianTestError();
  			
  			if(errTest < errTestMin){
  				errTestMin = errTest;
  				factorsMin = i;
  			}
  			
  		}
  		
  		System.out.println("Optimum number of factors " + factorsMin);
  		
  		PLSRegressionModelCalculator rmc = new PLSRegressionModelCalculator();
  		
  		rmc.setCenterData(centerData);
  
  		rmc.setFactors(factorsMin);
  
  		Matrix yHat = rmc.createModel(dataXYTrain);
  
  		ModelError modelErrorTrain = ModelError.calculateError(dataXYTrain.Y, yHat);
  
  
  		B = rmc.getB();
  		
  		YHat = rmc.getYHat();
  		       
          return modelErrorTrain;
  	}
  
  	/**
  	 * @return the b
  	 */
  	public Matrix getB() {
  		return B;
  	}
  
  
  	/**
  	 * @return the yHat
  	 */
  	public Matrix getYHat() {
  		return YHat;
  	}
  
  
  
  	/**
  	 * @return the factorsMin
  	 */
  	public int getFactorsMin() {
  		return factorsMin;
  	}
  
  	
  	
  }
  