[5574] | 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 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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[6974] | 22 | using System;
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[5574] | 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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[6880] | 25 | using HeuristicLab.Common;
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[5944] | 26 | using HeuristicLab.Problems.DataAnalysis;
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[5574] | 27 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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| 28 | namespace HeuristicLab.Problems.DataAnalysis_3_4.Tests {
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| 29 |
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| 30 | [TestClass()]
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| 31 | public class StatisticCalculatorsTest {
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| 32 | private double[,] testData = new double[,] {
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| 33 | {5,1,1,1,2,1,3,1,1,2},
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| 34 | {5,4,4,5,7,10,3,2,1,2},
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| 35 | {3,1,1,1,2,2,3,1,1,2},
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| 36 | {6,8,8,1,3,4,3,7,1,2},
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| 37 | {4,1,1,3,2,1,3,1,1,2},
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| 38 | {8,10,10,8,7,10,9,7,1,4},
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| 39 | {1,1,1,1,2,10,3,1,1,2},
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| 40 | {2,1,2,1,2,1,3,1,1,2},
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| 41 | {2,1,1,1,2,1,1,1,5,2},
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| 42 | {4,2,1,1,2,1,2,1,1,2},
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| 43 | {1,1,1,1,1,1,3,1,1,2},
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| 44 | {2,1,1,1,2,1,2,1,1,2},
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| 45 | {5,3,3,3,2,3,4,4,1,4},
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| 46 | {8,7,5,10,7,9,5,5,4,4},
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| 47 | {7,4,6,4,6,1,4,3,1,4},
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| 48 | {4,1,1,1,2,1,2,1,1,2},
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| 49 | {4,1,1,1,2,1,3,1,1,2},
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| 50 | {10,7,7,6,4,10,4,1,2,4},
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| 51 | {6,1,1,1,2,1,3,1,1,2},
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| 52 | {7,3,2,10,5,10,5,4,4,4},
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| 53 | {10,5,5,3,6,7,7,10,1,4}
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| 54 | };
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| 55 |
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| 56 | [TestMethod]
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| 57 | public void CalculateMeanAndVarianceTest() {
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| 58 | System.Random random = new System.Random(31415);
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| 59 |
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| 60 | int n = testData.GetLength(0);
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| 61 | int cols = testData.GetLength(1);
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| 62 | {
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| 63 | for (int col = 0; col < cols; col++) {
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[6880] | 64 | double scale = random.NextDouble();
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[5574] | 65 | IEnumerable<double> x = from rows in Enumerable.Range(0, n)
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| 66 | select testData[rows, col] * scale;
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| 67 | double[] xs = x.ToArray();
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| 68 | double mean_alglib, variance_alglib;
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| 69 | mean_alglib = variance_alglib = 0.0;
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| 70 | double tmp = 0;
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| 71 |
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| 72 | alglib.samplemoments(xs, n, out mean_alglib, out variance_alglib, out tmp, out tmp);
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| 73 |
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| 74 | var calculator = new OnlineMeanAndVarianceCalculator();
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| 75 | for (int i = 0; i < n; i++) {
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| 76 | calculator.Add(xs[i]);
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| 77 | }
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| 78 | double mean = calculator.Mean;
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| 79 | double variance = calculator.Variance;
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| 80 |
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[6880] | 81 | Assert.IsTrue(mean_alglib.IsAlmost(mean));
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| 82 | Assert.IsTrue(variance_alglib.IsAlmost(variance));
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[5574] | 83 | }
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| 84 | }
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| 85 | }
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| 86 |
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| 87 | [TestMethod]
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| 88 | public void CalculatePearsonsRSquaredTest() {
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| 89 | System.Random random = new System.Random(31415);
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| 90 | int n = testData.GetLength(0);
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| 91 | int cols = testData.GetLength(1);
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| 92 | for (int c1 = 0; c1 < cols; c1++) {
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| 93 | for (int c2 = c1 + 1; c2 < cols; c2++) {
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| 94 | {
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| 95 | double c1Scale = random.NextDouble() * 1E7;
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| 96 | double c2Scale = random.NextDouble() * 1E7;
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| 97 | IEnumerable<double> x = from rows in Enumerable.Range(0, n)
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| 98 | select testData[rows, c1] * c1Scale;
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| 99 | IEnumerable<double> y = from rows in Enumerable.Range(0, n)
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| 100 | select testData[rows, c2] * c2Scale;
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| 101 | double[] xs = x.ToArray();
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| 102 | double[] ys = y.ToArray();
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| 103 | double r2_alglib = alglib.pearsoncorrelation(xs, ys, n);
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| 104 | r2_alglib *= r2_alglib;
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| 105 |
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[5944] | 106 | var r2Calculator = new OnlinePearsonsRSquaredCalculator();
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[5574] | 107 | for (int i = 0; i < n; i++) {
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| 108 | r2Calculator.Add(xs[i], ys[i]);
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| 109 | }
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| 110 | double r2 = r2Calculator.RSquared;
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| 111 |
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[6880] | 112 | Assert.IsTrue(r2_alglib.IsAlmost(r2));
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[5574] | 113 | }
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| 114 | }
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| 115 | }
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| 116 | }
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[6184] | 117 | [TestMethod]
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| 118 | public void CalculatePearsonsRSquaredOfConstantTest() {
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| 119 | System.Random random = new System.Random(31415);
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| 120 | int n = 12;
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| 121 | int cols = testData.GetLength(1);
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| 122 | for (int c1 = 0; c1 < cols; c1++) {
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| 123 | double c1Scale = random.NextDouble() * 1E7;
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| 124 | IEnumerable<double> x = from rows in Enumerable.Range(0, n)
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| 125 | select testData[rows, c1] * c1Scale;
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[6738] | 126 | IEnumerable<double> y = (new List<double>() { 150494407424305.47 })
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[6184] | 127 | .Concat(Enumerable.Repeat(150494407424305.47, n - 1));
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| 128 | double[] xs = x.ToArray();
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| 129 | double[] ys = y.ToArray();
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| 130 | double r2_alglib = alglib.pearsoncorrelation(xs, ys, n);
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| 131 | r2_alglib *= r2_alglib;
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| 132 |
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| 133 | var r2Calculator = new OnlinePearsonsRSquaredCalculator();
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| 134 | for (int i = 0; i < n; i++) {
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| 135 | r2Calculator.Add(xs[i], ys[i]);
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| 136 | }
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| 137 | double r2 = r2Calculator.RSquared;
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| 138 |
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| 139 | Assert.AreEqual(r2_alglib.ToString(), r2.ToString());
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| 140 | }
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| 141 | }
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[6974] | 142 |
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| 143 | [TestMethod]
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| 144 | public void CalculateDirectionalSymmetryTest() {
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| 145 | // delta: +0.01, +1, -0.01, -2, -0.01, -1, +0.01, +2
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| 146 | var original = new double[]
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| 147 | {
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| 148 | 0,
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| 149 | 0.01,
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| 150 | 1.01,
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| 151 | 1,
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| 152 | -1,
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| 153 | -1.01,
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| 154 | -2.01,
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| 155 | -2,
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| 156 | 0
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| 157 | };
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| 158 | // delta to original(t-1): +1, +0, -1, -0, -1, +0.01, +0.01, +2
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| 159 | var estimated = new double[]
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| 160 | {
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| 161 | -1,
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| 162 | 1,
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| 163 | 0.01,
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| 164 | 0.01,
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| 165 | 1,
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| 166 | -1,
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| 167 | -1.02,
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| 168 | -2.02,
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| 169 | 0
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| 170 | };
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| 171 |
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| 172 | // one-step forecast
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| 173 | var startValues = original;
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| 174 | var actualContinuations = from x in original.Skip(1)
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| 175 | select Enumerable.Repeat(x, 1);
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| 176 | var predictedContinuations = from x in estimated.Skip(1)
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| 177 | select Enumerable.Repeat(x, 1);
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| 178 | double expected = 0.5; // half of the predicted deltas are correct
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| 179 | OnlineCalculatorError errorState;
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| 180 | double actual = OnlineDirectionalSymmetryCalculator.Calculate(startValues, actualContinuations, predictedContinuations, out errorState);
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| 181 | Assert.AreEqual(expected, actual, 1E-9);
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| 182 | }
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| 183 | [TestMethod]
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| 184 | public void CalculateWeightedDirectionalSymmetryTest() {
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| 185 | var original = new double[] { 0, 0.01, 1.01, 1, -1, -1.01, -2.01, -2, 0 }; // +0.01, +1, -0.01, -2, -0.01, -1, +0.01, +2
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| 186 | var estimated = new double[] { 1, 2, 2, 1, 1, 0, 0.01, 0.02, 2.02 }; // delta to original: +2, +1.99, -0.01, 0, +1, -1.02, +2.01, +4.02
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| 187 | // one-step forecast
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| 188 | var startValues = original;
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| 189 | var actualContinuations = from x in original.Skip(1)
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| 190 | select Enumerable.Repeat(x, 1);
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| 191 | var predictedContinuations = from x in estimated.Skip(1)
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| 192 | select Enumerable.Repeat(x, 1);
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| 193 | // absolute errors = 1.99, 0.99, 0, 2, 1.01, 2.02, 2.02, 2.02
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| 194 | // sum of absolute errors for correctly predicted deltas = 2.97
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| 195 | // sum of absolute errors for incorrectly predicted deltas = 3.03
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| 196 | double expected = 5.03 / 7.02;
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| 197 | OnlineCalculatorError errorState;
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| 198 | double actual = OnlineWeightedDirectionalSymmetryCalculator.Calculate(startValues, actualContinuations, predictedContinuations, out errorState);
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| 199 | Assert.AreEqual(expected, actual, 1E-9);
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| 200 | }
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| 201 | [TestMethod]
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| 202 | public void CalculateTheilsUTest() {
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| 203 | var original = new double[] { 0, 0.01, 1.01, 1, -1, -1.01, -2.01, -2, 0 };
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| 204 | var estimated = new double[] { 1, 1.01, 0.01, 2, 0, -0.01, -1.01, -3, 1 };
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| 205 | // one-step forecast
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| 206 | var startValues = original;
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| 207 | var actualContinuations = from x in original.Skip(1)
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| 208 | select Enumerable.Repeat(x, 1);
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| 209 | var predictedContinuations = from x in estimated.Skip(1)
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| 210 | select Enumerable.Repeat(x, 1);
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| 211 | // Sum of squared errors of model y(t+1) = y(t) = 10.0004
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| 212 | // Sum of squared errors of predicted values = 8
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| 213 | double expected = Math.Sqrt(8 / 10.0004);
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| 214 | OnlineCalculatorError errorState;
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| 215 | double actual = OnlineTheilsUStatisticCalculator.Calculate(startValues, actualContinuations, predictedContinuations, out errorState);
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| 216 | Assert.AreEqual(expected, actual, 1E-9);
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| 217 | }
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| 218 | [TestMethod]
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| 219 | public void CalculateAccuracyTest() {
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| 220 | var original = new double[] { 1, 1, 0, 0 };
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| 221 | var estimated = new double[] { 1, 0, 1, 0 };
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| 222 | double expected = 0.5;
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| 223 | OnlineCalculatorError errorState;
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| 224 | double actual = OnlineAccuracyCalculator.Calculate(original, estimated, out errorState);
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| 225 | Assert.AreEqual(expected, actual, 1E-9);
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| 226 | }
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| 227 |
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| 228 | [TestMethod]
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| 229 | public void CalculateMeanAbsolutePercentageErrorTest() {
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| 230 | var original = new double[] { 1, 2, 3, 1, 5 };
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| 231 | var estimated = new double[] { 2, 1, 3, 1, 0 };
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| 232 | double expected = 0.5;
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| 233 | OnlineCalculatorError errorState;
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| 234 | double actual = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(original, estimated, out errorState);
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| 235 | Assert.AreEqual(expected, actual, 1E-9);
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| 236 | Assert.AreEqual(OnlineCalculatorError.None, errorState);
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| 237 |
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| 238 | // if the original contains zero values the result is not defined
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| 239 | var original2 = new double[] { 1, 2, 0, 0, 0 };
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| 240 | OnlineMeanAbsolutePercentageErrorCalculator.Calculate(original2, estimated, out errorState);
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| 241 | Assert.AreEqual(OnlineCalculatorError.InvalidValueAdded, errorState);
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| 242 | }
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[5574] | 243 | }
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| 244 | }
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