1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2017 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using HeuristicLab.Common;
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26 |
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27 | namespace HeuristicLab.Algorithms.DataAnalysis {
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28 | /// <summary>
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29 | /// Helper class for incremental split calculation.
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30 | /// Used while moving a potential splitter along the ordered training instances
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31 | /// </summary>
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32 | internal class UnivariateOnlineLR {
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33 | #region state
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34 | private readonly NeumaierSum targetMean;
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35 | private readonly NeumaierSum attributeMean;
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36 | private readonly NeumaierSum targetVarSum;
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37 | private readonly NeumaierSum attributeVarSum;
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38 | private readonly NeumaierSum comoment;
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39 | private readonly NeumaierSum ssr;
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40 | private int size;
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41 | #endregion
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42 |
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43 | public double Ssr {
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44 | get { return ssr.Get(); }
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45 | }
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46 | public int Size {
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47 | get { return size; }
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48 | }
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49 |
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50 | private double Beta {
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51 | get { return comoment.Get() / attributeVarSum.Get(); }
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52 | }
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53 | private double Alpha {
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54 | get { return targetMean.Get() - Beta * attributeMean.Get(); }
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55 | }
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56 |
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57 | public UnivariateOnlineLR(ICollection<double> attributeValues, ICollection<double> targetValues) {
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58 | if (attributeValues.Count != targetValues.Count) throw new ArgumentException("Targets and Attributes need to have the same length");
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59 | size = attributeValues.Count;
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60 |
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61 | var yMean = targetValues.Average();
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62 | var xMean = attributeValues.Average();
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63 | targetMean = new NeumaierSum(yMean);
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64 | attributeMean = new NeumaierSum(xMean);
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65 | targetVarSum = new NeumaierSum(targetValues.VariancePop() * size);
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66 | attributeVarSum = new NeumaierSum(attributeValues.VariancePop() * size);
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67 | comoment = new NeumaierSum(attributeValues.Zip(targetValues, (x, y) => (x - xMean) * (y - yMean)).Sum());
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68 |
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69 | var beta = comoment.Get() / attributeVarSum.Get();
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70 | var alpha = yMean - beta * xMean;
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71 | ssr = new NeumaierSum(attributeValues.Zip(targetValues, (x, y) => y - alpha - beta * x).Sum(x => x * x));
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72 | }
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73 |
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74 | public void Add(double attributeValue, double targetValue) {
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75 | var predictOld = Predict(attributeValue, targetValue);
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76 |
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77 | size++;
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78 | var dx = attributeValue - attributeMean.Get();
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79 | var dy = targetValue - targetMean.Get();
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80 | attributeMean.Add(dx / size);
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81 | targetMean.Add(dy / size);
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82 | var dx2 = attributeValue - attributeMean.Get();
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83 | var dy2 = targetValue - targetMean.Get();
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84 | attributeVarSum.Add(dx * dx2);
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85 | targetVarSum.Add(dy * dy2);
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86 | comoment.Add(dx * dy2);
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87 |
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88 | ssr.Add(predictOld * Predict(attributeValue, targetValue));
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89 | }
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90 |
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91 | public void Remove(double attributeValue, double targetValue) {
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92 | var predictOld = Predict(attributeValue, targetValue);
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93 |
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94 | var dx2 = attributeValue - attributeMean.Get();
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95 | var dy2 = targetValue - targetMean.Get();
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96 | attributeMean.Mul(size / (size - 1.0));
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97 | targetMean.Mul(size / (size - 1.0));
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98 | attributeMean.Add(-attributeValue / (size - 1.0));
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99 | targetMean.Add(-targetValue / (size - 1.0));
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100 | var dx = attributeValue - attributeMean.Get();
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101 | var dy = targetValue - targetMean.Get();
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102 | attributeVarSum.Add(-dx * dx2);
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103 | targetVarSum.Add(-dy * dy2);
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104 | comoment.Add(-dx * dy2);
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105 | size--;
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106 |
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107 | ssr.Add(-predictOld * Predict(attributeValue, targetValue));
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108 | }
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109 |
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110 | private double Predict(double attributeValue, double targetValue) {
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111 | return targetValue - Alpha - Beta * attributeValue;
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112 | }
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113 | }
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114 | } |
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