[5500] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[7259] | 3 | * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[5500] | 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 | using HeuristicLab.Core;
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| 27 | using HeuristicLab.Data;
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| 28 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 29 | using HeuristicLab.Operators;
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| 30 | using HeuristicLab.Parameters;
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| 31 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 32 | using HeuristicLab.Random;
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| 33 |
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| 34 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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[5689] | 35 | [StorableClass]
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[5509] | 36 | public abstract class SymbolicDataAnalysisEvaluator<T> : SingleSuccessorOperator,
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[5720] | 37 | ISymbolicDataAnalysisEvaluator<T>, ISymbolicDataAnalysisInterpreterOperator, ISymbolicDataAnalysisBoundedOperator
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[5509] | 38 | where T : class, IDataAnalysisProblemData {
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[5500] | 39 | private const string RandomParameterName = "Random";
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| 40 | private const string SymbolicExpressionTreeParameterName = "SymbolicExpressionTree";
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[5514] | 41 | private const string SymbolicDataAnalysisTreeInterpreterParameterName = "SymbolicExpressionTreeInterpreter";
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[5500] | 42 | private const string ProblemDataParameterName = "ProblemData";
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[5770] | 43 | private const string EstimationLimitsParameterName = "EstimationLimits";
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[5759] | 44 | private const string EvaluationPartitionParameterName = "EvaluationPartition";
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[5500] | 45 | private const string RelativeNumberOfEvaluatedSamplesParameterName = "RelativeNumberOfEvaluatedSamples";
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[8664] | 46 | private const string ApplyLinearScalingParameterName = "ApplyLinearScaling";
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[5500] | 47 |
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[5618] | 48 | public override bool CanChangeName { get { return false; } }
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| 49 |
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[5500] | 50 | #region parameter properties
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[5514] | 51 | public IValueLookupParameter<IRandom> RandomParameter {
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| 52 | get { return (IValueLookupParameter<IRandom>)Parameters[RandomParameterName]; }
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[5500] | 53 | }
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| 54 | public ILookupParameter<ISymbolicExpressionTree> SymbolicExpressionTreeParameter {
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| 55 | get { return (ILookupParameter<ISymbolicExpressionTree>)Parameters[SymbolicExpressionTreeParameterName]; }
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| 56 | }
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[5649] | 57 | public ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter> SymbolicDataAnalysisTreeInterpreterParameter {
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| 58 | get { return (ILookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>)Parameters[SymbolicDataAnalysisTreeInterpreterParameterName]; }
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[5500] | 59 | }
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[5514] | 60 | public IValueLookupParameter<T> ProblemDataParameter {
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| 61 | get { return (IValueLookupParameter<T>)Parameters[ProblemDataParameterName]; }
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[5500] | 62 | }
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| 63 |
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[5759] | 64 | public IValueLookupParameter<IntRange> EvaluationPartitionParameter {
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| 65 | get { return (IValueLookupParameter<IntRange>)Parameters[EvaluationPartitionParameterName]; }
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[5500] | 66 | }
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[5770] | 67 | public IValueLookupParameter<DoubleLimit> EstimationLimitsParameter {
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| 68 | get { return (IValueLookupParameter<DoubleLimit>)Parameters[EstimationLimitsParameterName]; }
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[5500] | 69 | }
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[5759] | 70 | public IValueLookupParameter<PercentValue> RelativeNumberOfEvaluatedSamplesParameter {
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| 71 | get { return (IValueLookupParameter<PercentValue>)Parameters[RelativeNumberOfEvaluatedSamplesParameterName]; }
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[5500] | 72 | }
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[8664] | 73 | public ILookupParameter<BoolValue> ApplyLinearScalingParameter {
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| 74 | get { return (ILookupParameter<BoolValue>)Parameters[ApplyLinearScalingParameterName]; }
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| 75 | }
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[5500] | 76 | #endregion
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| 77 |
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| 78 |
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| 79 | [StorableConstructor]
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| 80 | protected SymbolicDataAnalysisEvaluator(bool deserializing) : base(deserializing) { }
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[5509] | 81 | protected SymbolicDataAnalysisEvaluator(SymbolicDataAnalysisEvaluator<T> original, Cloner cloner)
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[5500] | 82 | : base(original, cloner) {
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| 83 | }
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| 84 | public SymbolicDataAnalysisEvaluator()
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| 85 | : base() {
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[5514] | 86 | Parameters.Add(new ValueLookupParameter<IRandom>(RandomParameterName, "The random generator to use."));
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[5649] | 87 | Parameters.Add(new LookupParameter<ISymbolicDataAnalysisExpressionTreeInterpreter>(SymbolicDataAnalysisTreeInterpreterParameterName, "The interpreter that should be used to calculate the output values of the symbolic data analysis tree."));
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[5618] | 88 | Parameters.Add(new LookupParameter<ISymbolicExpressionTree>(SymbolicExpressionTreeParameterName, "The symbolic data analysis solution encoded as a symbolic expression tree."));
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[5514] | 89 | Parameters.Add(new ValueLookupParameter<T>(ProblemDataParameterName, "The problem data on which the symbolic data analysis solution should be evaluated."));
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[5759] | 90 | Parameters.Add(new ValueLookupParameter<IntRange>(EvaluationPartitionParameterName, "The start index of the dataset partition on which the symbolic data analysis solution should be evaluated."));
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[5770] | 91 | Parameters.Add(new ValueLookupParameter<DoubleLimit>(EstimationLimitsParameterName, "The upper and lower limit that should be used as cut off value for the output values of symbolic data analysis trees."));
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[5759] | 92 | Parameters.Add(new ValueLookupParameter<PercentValue>(RelativeNumberOfEvaluatedSamplesParameterName, "The relative number of samples of the dataset partition, which should be randomly chosen for evaluation between the start and end index."));
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[8664] | 93 | Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
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[5500] | 94 | }
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| 95 |
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[8664] | 96 | [StorableHook(HookType.AfterDeserialization)]
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| 97 | private void AfterDeserialization() {
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| 98 | if (Parameters.ContainsKey(ApplyLinearScalingParameterName) && !(Parameters[ApplyLinearScalingParameterName] is LookupParameter<BoolValue>))
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| 99 | Parameters.Remove(ApplyLinearScalingParameterName);
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| 100 | if (!Parameters.ContainsKey(ApplyLinearScalingParameterName))
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| 101 | Parameters.Add(new LookupParameter<BoolValue>(ApplyLinearScalingParameterName, "Flag that indicates if the individual should be linearly scaled before evaluating."));
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| 102 | }
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| 103 |
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[5500] | 104 | protected IEnumerable<int> GenerateRowsToEvaluate() {
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[5914] | 105 | return GenerateRowsToEvaluate(RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value);
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| 106 | }
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| 107 |
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| 108 | protected IEnumerable<int> GenerateRowsToEvaluate(double percentageOfRows) {
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| 109 | IEnumerable<int> rows;
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[5759] | 110 | int samplesStart = EvaluationPartitionParameter.ActualValue.Start;
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| 111 | int samplesEnd = EvaluationPartitionParameter.ActualValue.End;
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[5823] | 112 | int testPartitionStart = ProblemDataParameter.ActualValue.TestPartition.Start;
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| 113 | int testPartitionEnd = ProblemDataParameter.ActualValue.TestPartition.End;
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[5618] | 114 |
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[5759] | 115 | if (samplesEnd < samplesStart) throw new ArgumentException("Start value is larger than end value.");
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[5914] | 116 |
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| 117 | if (percentageOfRows.IsAlmost(1.0))
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| 118 | rows = Enumerable.Range(samplesStart, samplesEnd - samplesStart);
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| 119 | else {
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| 120 | int seed = RandomParameter.ActualValue.Next();
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| 121 | int count = (int)((samplesEnd - samplesStart) * percentageOfRows);
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| 122 | if (count == 0) count = 1;
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| 123 | rows = RandomEnumerable.SampleRandomNumbers(seed, samplesStart, samplesEnd, count);
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| 124 | }
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| 125 |
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| 126 | return rows.Where(i => i < testPartitionStart || testPartitionEnd <= i);
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[5500] | 127 | }
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[8664] | 128 |
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| 129 | [ThreadStatic]
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| 130 | private static double[] cache;
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| 131 | protected static void CalculateWithScaling(IEnumerable<double> targetValues, IEnumerable<double> estimatedValues,
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| 132 | double lowerEstimationLimit, double upperEstimationLimit,
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| 133 | IOnlineCalculator calculator, int maxRows) {
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| 134 | if (cache == null || cache.GetLength(0) < maxRows) {
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| 135 | cache = new double[maxRows];
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| 136 | }
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| 137 |
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| 138 | //calculate linear scaling
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| 139 | //the static methods of the calculator could not be used as it performs a check if the enumerators have an equal amount of elements
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| 140 | //this is not true if the cache is used
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| 141 | int i = 0;
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| 142 | var linearScalingCalculator = new OnlineLinearScalingParameterCalculator();
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| 143 | var targetValuesEnumerator = targetValues.GetEnumerator();
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| 144 | var estimatedValuesEnumerator = estimatedValues.GetEnumerator();
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| 145 | while (targetValuesEnumerator.MoveNext() & estimatedValuesEnumerator.MoveNext()) {
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| 146 | double target = targetValuesEnumerator.Current;
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| 147 | double estimated = estimatedValuesEnumerator.Current;
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| 148 | cache[i] = estimated;
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| 149 | if (!double.IsNaN(estimated) && !double.IsInfinity(estimated))
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| 150 | linearScalingCalculator.Add(estimated, target);
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| 151 | i++;
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| 152 | }
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| 153 | if (linearScalingCalculator.ErrorState == OnlineCalculatorError.None && (targetValuesEnumerator.MoveNext() || estimatedValuesEnumerator.MoveNext()))
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| 154 | throw new ArgumentException("Number of elements in target and estimated values enumeration do not match.");
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| 155 |
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| 156 | double alpha = linearScalingCalculator.Alpha;
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| 157 | double beta = linearScalingCalculator.Beta;
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| 158 | if (linearScalingCalculator.ErrorState != OnlineCalculatorError.None) {
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| 159 | alpha = 0.0;
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| 160 | beta = 1.0;
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| 161 | }
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| 162 |
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| 163 | //calculate the quality by using the passed online calculator
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| 164 | targetValuesEnumerator = targetValues.GetEnumerator();
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| 165 | var scaledBoundedEstimatedValuesEnumerator = Enumerable.Range(0, i).Select(x => cache[x] * beta + alpha)
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| 166 | .LimitToRange(lowerEstimationLimit, upperEstimationLimit).GetEnumerator();
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| 167 |
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| 168 | while (targetValuesEnumerator.MoveNext() & scaledBoundedEstimatedValuesEnumerator.MoveNext()) {
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| 169 | calculator.Add(targetValuesEnumerator.Current, scaledBoundedEstimatedValuesEnumerator.Current);
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| 170 | }
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| 171 | }
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[5500] | 172 | }
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| 173 | }
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