[4475] | 1 | using System;
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| 2 | using System.Collections.Generic;
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| 3 | using System.Linq;
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| 4 | using System.Text;
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| 5 | using HeuristicLab.Core;
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| 6 | using HeuristicLab.Data;
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| 7 | using HeuristicLab.Problems.DataAnalysis.Evaluators;
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| 8 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 9 | using HeuristicLab.Parameters;
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| 10 |
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| 11 | namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Evaluators {
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| 12 | public class OnlineMeanMahalanobisDistanceEvaluator : IMultiVariateOnlineEvaluator {
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| 13 | private int n;
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| 14 | private double distance;
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| 15 | private double[,] covMatrix;
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[4555] | 16 | private double[] diff;
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| 17 | private double[] target;
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[4475] | 18 | public double MeanMahalanobisDistance {
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| 19 | get {
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| 20 | if (n == 0) throw new InvalidOperationException("no elements");
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| 21 | else
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| 22 | return Math.Sqrt(distance) / n;
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| 23 | }
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| 24 | }
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| 25 |
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| 26 | public double MeanGeneralizedSquaredInterpointDistance {
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| 27 | get {
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| 28 | if (n == 0) throw new InvalidOperationException("no elements");
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| 29 | else
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| 30 | return distance / n;
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| 31 | }
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| 32 | }
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| 33 |
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| 34 | public OnlineMeanMahalanobisDistanceEvaluator() {
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| 35 | Reset();
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| 36 | }
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| 37 |
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| 38 | #region IMultiVariateOnlineEvaluator Members
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| 39 | public double Value {
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| 40 | get { return MeanMahalanobisDistance; }
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| 41 | }
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| 42 |
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[4555] | 43 | public void Add(IEnumerable<double> original, IEnumerable<double> estimated) {
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| 44 | if (covMatrix == null) throw new InvalidOperationException("Covariance matrix must be initialized before values can be added.");
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[4475] | 45 |
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[4555] | 46 | {
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| 47 | // calculate difference vector
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| 48 | var originalEnumerator = original.GetEnumerator();
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| 49 | var estimatedEnumerator = estimated.GetEnumerator();
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| 50 | int i = 0;
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| 51 | while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext() && i < diff.Length) {
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| 52 | diff[i++] = originalEnumerator.Current - estimatedEnumerator.Current;
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| 53 | }
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| 54 | if (originalEnumerator.MoveNext() | estimatedEnumerator.MoveNext() || i < diff.Length) {
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| 55 | throw new ArgumentException("Number of elements of original and estimated doesn't match or is not compatible with covariance matrix.");
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| 56 | }
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[4475] | 57 | }
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| 58 |
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[4555] | 59 | {
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| 60 | // calculate mahalanobis distance using covariance matrix
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| 61 | // covMatrix^(-1) * diff => target
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[5275] | 62 | alglib.ablas.rmatrixmv(covMatrix.GetLength(0), covMatrix.GetLength(1), covMatrix, 0, 0, 0, diff, 0, ref target, 0);
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[4475] | 63 |
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[4555] | 64 | // diff^T * (covMatrix^(-1) * diff) => sum
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| 65 | double sum = 0.0;
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| 66 | for (int i = 0; i < diff.Length; i++) {
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| 67 | sum += diff[i] * target[i];
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| 68 | }
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| 69 | distance += sum;
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| 70 | n++;
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[4475] | 71 | }
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| 72 | }
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| 73 |
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| 74 | public void Reset() {
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| 75 | n = 0;
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| 76 | distance = 0.0;
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[4555] | 77 | covMatrix = null;
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| 78 | diff = null;
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| 79 | target = null;
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[4475] | 80 | }
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| 81 |
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| 82 | #endregion
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| 83 |
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| 84 | public void InitializeCovarianceMatrixFromSamples(params IEnumerable<double>[] samples) {
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| 85 | covMatrix = new double[samples.Length, samples.Length];
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| 86 | OnlineCovarianceEvaluator covEvaluator = new OnlineCovarianceEvaluator();
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| 87 | for (int i = 0; i < samples.Length; i++) {
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| 88 | for (int j = i; j < samples.Length; j++) {
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| 89 | var xEnumerator = samples[i].GetEnumerator();
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| 90 | var yEnumerator = samples[j].GetEnumerator();
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| 91 | covEvaluator.Reset();
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| 92 | while (xEnumerator.MoveNext() & yEnumerator.MoveNext()) {
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| 93 | covEvaluator.Add(xEnumerator.Current, yEnumerator.Current);
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| 94 | }
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| 95 | if (xEnumerator.MoveNext() | yEnumerator.MoveNext()) {
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| 96 | throw new ArgumentException("Number of elements must be the same in all enumerations.");
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| 97 | }
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| 98 | covMatrix[i, j] = covEvaluator.Covariance;
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| 99 | covMatrix[j, i] = covEvaluator.Covariance;
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| 100 | }
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| 101 | }
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| 102 | int info = 0;
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| 103 | alglib.matinv.matinvreport report = new alglib.matinv.matinvreport();
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[5275] | 104 | alglib.matinv.rmatrixinverse(ref covMatrix, covMatrix.GetLength(0), ref info, report);
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[4475] | 105 | if (info != 1) throw new InvalidOperationException("Can't invert covariance matrix.");
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[4555] | 106 | diff = new double[samples.Length];
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| 107 | target = new double[samples.Length];
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[4475] | 108 | }
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| 109 | }
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| 110 | }
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