1 | using System;
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2 | using HeuristicLab.Data;
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3 |
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4 | namespace HeuristicLab.Algorithms.DataAnalysis.ContinuedFractionRegression {
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5 | public class Transformation {
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6 |
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7 | DoubleMatrix dataMatrix;
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8 |
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9 | private double[] tempMinMax;
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10 | private double[] phiMinMax;
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11 | private double[] phipMinMax;
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12 |
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13 | public Transformation(DoubleMatrix dataMatrix) {
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14 | this.dataMatrix = dataMatrix;
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15 | transform0();
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16 | }
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17 |
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18 | private void transform0() { // care! change the other two transformations accordingly!
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19 | tempMinMax = findMinMax(0);
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20 | minMaxTransformation(this.dataMatrix, tempMinMax, 0);
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21 | log10Transformation(this.dataMatrix, 2);
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22 | }
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23 |
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24 | public double[] transform0(double[] orig) {
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25 | double[] trans = new double[3];
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26 | trans[0] = minMaxTransformation(tempMinMax, orig[0]);
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27 | trans[1] = orig[1];
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28 | trans[2] = log10Transformation(orig[2]);
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29 |
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30 | return trans;
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31 | }
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32 |
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33 | public void useSameTransformation(DoubleMatrix dataMatrix) {
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34 | minMaxTransformation(dataMatrix, tempMinMax, 0);
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35 | log10Transformation(dataMatrix, 2);
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36 | }
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37 |
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38 | //another transformation - not as good as i thougt
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39 | private void transform1() { // care! change retransformation accordingly!
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40 | tempMinMax = findMinMax(0);
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41 | minMaxTransformation(this.dataMatrix, this.tempMinMax, 0);
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42 | phiMinMax = findMinMax(1);
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43 | minMaxTransformation(this.dataMatrix, this.phiMinMax, 1);
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44 | log10Transformation(2); // no negativ values!
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45 | phipMinMax = findMinMax(2);
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46 | minMaxTransformation(this.dataMatrix, this.phipMinMax, 2); // Min and Max have to be different!
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47 | }
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48 |
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49 | public double[] transform1(double[] orig) {
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50 | double[] trans = new double[3];
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51 | trans[0] = minMaxTransformation(tempMinMax, orig[0]);
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52 | trans[1] = minMaxTransformation(phiMinMax, orig[1]);
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53 | trans[2] = log10Transformation(orig[2]);
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54 | trans[2] = minMaxTransformation(phipMinMax, trans[2]);
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55 |
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56 | return trans;
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57 | }
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58 | //
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59 |
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60 | private double[] findMinMax(int column) {
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61 | // the first row has index 0
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62 | double[] MinMax = new double[2] { double.MaxValue, double.MinValue };
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63 | // find min and max in column
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64 | for (int i = 0; i < dataMatrix.Rows; i++) {
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65 | if (dataMatrix[i, column] < MinMax[0])
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66 | MinMax[0] = dataMatrix[i, column];
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67 | if (dataMatrix[i, column] > MinMax[1])
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68 | MinMax[1] = dataMatrix[i, column];
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69 | }
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70 | return MinMax;
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71 | }
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72 |
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73 | private void minMaxTransformation(DoubleMatrix matrix, double[] minMax, int column) {
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74 | // transform all values in column
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75 | for (int i = 0; i < matrix.Rows; i++) {
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76 | matrix[i, column] = (matrix[i, column] - minMax[0]) / (minMax[1] - minMax[0]);
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77 | }
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78 | }
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79 |
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80 | private double minMaxTransformation(double[] minMax, double x) {
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81 | return (x - minMax[0]) / (minMax[1] - minMax[0]);
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82 | }
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83 |
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84 | private void log10Transformation(DoubleMatrix matrix, int column) {
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85 | // the first row has index 0
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86 | for (int i = 0; i < matrix.Rows; i++) {
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87 | matrix[i, column] = Math.Log10(matrix[i, column]);
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88 | }
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89 | }
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90 |
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91 | private double log10Transformation(double x) {
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92 | return Math.Log10(x);
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93 | }
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94 | }
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95 |
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96 | }
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