1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2012 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 HeuristicLab.Common;
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25 | using HeuristicLab.Core;
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26 | using HeuristicLab.Data;
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27 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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28 | using HeuristicLab.Parameters;
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29 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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30 |
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31 | namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
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32 | [StorableClass]
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33 | [Item("SymbolicDataAnalysisExpressionTreeInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.")]
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34 | public sealed class SymbolicDataAnalysisExpressionTreeInterpreter : ParameterizedNamedItem,
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35 | ISymbolicDataAnalysisExpressionTreeInterpreter, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter {
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36 | private const string CheckExpressionsWithIntervalArithmeticParameterName = "CheckExpressionsWithIntervalArithmetic";
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37 | private const string EvaluatedSolutionsParameterName = "EvaluatedSolutions";
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38 |
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39 | public override bool CanChangeName {
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40 | get { return false; }
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41 | }
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42 | public override bool CanChangeDescription {
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43 | get { return false; }
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44 | }
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45 |
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46 | #region parameter properties
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47 | public IValueParameter<BoolValue> CheckExpressionsWithIntervalArithmeticParameter {
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48 | get { return (IValueParameter<BoolValue>)Parameters[CheckExpressionsWithIntervalArithmeticParameterName]; }
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49 | }
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50 |
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51 | public IValueParameter<IntValue> EvaluatedSolutionsParameter {
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52 | get { return (IValueParameter<IntValue>)Parameters[EvaluatedSolutionsParameterName]; }
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53 | }
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54 | #endregion
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55 |
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56 | #region properties
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57 | public BoolValue CheckExpressionsWithIntervalArithmetic {
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58 | get { return CheckExpressionsWithIntervalArithmeticParameter.Value; }
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59 | set { CheckExpressionsWithIntervalArithmeticParameter.Value = value; }
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60 | }
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61 |
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62 | public IntValue EvaluatedSolutions {
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63 | get { return EvaluatedSolutionsParameter.Value; }
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64 | set { EvaluatedSolutionsParameter.Value = value; }
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65 | }
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66 | #endregion
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67 |
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68 | [StorableConstructor]
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69 | private SymbolicDataAnalysisExpressionTreeInterpreter(bool deserializing) : base(deserializing) { }
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70 | private SymbolicDataAnalysisExpressionTreeInterpreter(SymbolicDataAnalysisExpressionTreeInterpreter original, Cloner cloner) : base(original, cloner) { }
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71 | public override IDeepCloneable Clone(Cloner cloner) {
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72 | return new SymbolicDataAnalysisExpressionTreeInterpreter(this, cloner);
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73 | }
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74 |
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75 | public SymbolicDataAnalysisExpressionTreeInterpreter()
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76 | : base("SymbolicDataAnalysisExpressionTreeInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.") {
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77 | Parameters.Add(new ValueParameter<BoolValue>(CheckExpressionsWithIntervalArithmeticParameterName, "Switch that determines if the interpreter checks the validity of expressions with interval arithmetic before evaluating the expression.", new BoolValue(false)));
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78 | Parameters.Add(new ValueParameter<IntValue>(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
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79 | }
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80 |
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81 | [StorableHook(HookType.AfterDeserialization)]
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82 | private void AfterDeserialization() {
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83 | if (!Parameters.ContainsKey(EvaluatedSolutionsParameterName))
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84 | Parameters.Add(new ValueParameter<IntValue>(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
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85 | }
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86 |
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87 | #region IStatefulItem
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88 | public void InitializeState() {
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89 | EvaluatedSolutions.Value = 0;
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90 | }
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91 |
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92 | public void ClearState() {
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93 | }
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94 | #endregion
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95 |
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96 | public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows) {
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97 | return GetSymbolicExpressionTreeValues(tree, dataset, new string[] { "#NOTHING#" }, rows);
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98 | }
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99 |
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100 | public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, string[] targetVariables, IEnumerable<int> rows) {
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101 | return GetSymbolicExpressionTreeValues(tree, dataset, targetVariables, rows, 1);
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102 | }
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103 |
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104 | // for each row for each horizon for each target variable one value
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105 | public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, string[] targetVariables, IEnumerable<int> rows, int horizon) {
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106 | if (CheckExpressionsWithIntervalArithmetic.Value)
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107 | throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
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108 |
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109 | EvaluatedSolutions.Value++; // increment the evaluated solutions counter
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110 | var state = PrepareInterpreterState(tree, dataset, targetVariables[0]);
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111 |
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112 | // produce a n-step forecast for each target variable for all rows
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113 | var cachedPrognosedValues = new Dictionary<string, double[]>();
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114 | //foreach (var targetVariable in targetVariables)
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115 | // cachedPrognosedValues[targetVariable] = new double[horizon];
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116 | foreach (var rowEnum in rows) {
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117 | int row = rowEnum;
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118 | for (int localRow = row; localRow < row + horizon; localRow++) {
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119 | //int localRow = horizonRow; // create a local variable for the ref parameter
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120 | yield return Evaluate(dataset, ref localRow, row - 1, state, cachedPrognosedValues);
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121 | //cachedPrognosedValues[targetVariables[c]][horizonRow - row] = prog;
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122 | state.Reset();
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123 | }
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124 | }
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125 | }
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126 |
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127 | private InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, Dataset dataset, string targetVariable) {
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128 | Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
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129 | int necessaryArgStackSize = 0;
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130 | for (int i = 0; i < code.Length; i++) {
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131 | Instruction instr = code[i];
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132 | if (instr.opCode == OpCodes.Variable) {
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133 | var variableTreeNode = (VariableTreeNode)instr.dynamicNode;
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134 | instr.iArg0 = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
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135 | code[i] = instr;
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136 | } else if (instr.opCode == OpCodes.LagVariable) {
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137 | var laggedVariableTreeNode = (LaggedVariableTreeNode)instr.dynamicNode;
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138 | instr.iArg0 = dataset.GetReadOnlyDoubleValues(laggedVariableTreeNode.VariableName);
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139 | code[i] = instr;
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140 | } else if (instr.opCode == OpCodes.VariableCondition) {
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141 | var variableConditionTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
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142 | instr.iArg0 = dataset.GetReadOnlyDoubleValues(variableConditionTreeNode.VariableName);
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143 | } else if (instr.opCode == OpCodes.Call) {
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144 | necessaryArgStackSize += instr.nArguments + 1;
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145 | }
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146 | }
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147 | return new InterpreterState(code, necessaryArgStackSize);
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148 | }
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149 |
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150 | private double Evaluate(Dataset dataset, ref int row, int lastObservedRow, InterpreterState state, Dictionary<string, double[]> cachedPrognosedValues) {
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151 | Instruction currentInstr = state.NextInstruction();
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152 | switch (currentInstr.opCode) {
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153 | case OpCodes.Add: {
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154 | double s = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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155 | for (int i = 1; i < currentInstr.nArguments; i++) {
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156 | s += Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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157 | }
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158 | return s;
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159 | }
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160 | case OpCodes.Sub: {
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161 | double s = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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162 | for (int i = 1; i < currentInstr.nArguments; i++) {
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163 | s -= Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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164 | }
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165 | if (currentInstr.nArguments == 1) s = -s;
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166 | return s;
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167 | }
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168 | case OpCodes.Mul: {
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169 | double p = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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170 | for (int i = 1; i < currentInstr.nArguments; i++) {
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171 | p *= Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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172 | }
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173 | return p;
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174 | }
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175 | case OpCodes.Div: {
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176 | double p = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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177 | for (int i = 1; i < currentInstr.nArguments; i++) {
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178 | p /= Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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179 | }
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180 | if (currentInstr.nArguments == 1) p = 1.0 / p;
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181 | return p;
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182 | }
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183 | case OpCodes.Average: {
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184 | double sum = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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185 | for (int i = 1; i < currentInstr.nArguments; i++) {
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186 | sum += Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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187 | }
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188 | return sum / currentInstr.nArguments;
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189 | }
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190 | case OpCodes.Cos: {
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191 | return Math.Cos(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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192 | }
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193 | case OpCodes.Sin: {
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194 | return Math.Sin(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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195 | }
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196 | case OpCodes.Tan: {
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197 | return Math.Tan(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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198 | }
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199 | case OpCodes.Square: {
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200 | return Math.Pow(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues), 2);
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201 | }
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202 | case OpCodes.Power: {
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203 | double x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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204 | double y = Math.Round(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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205 | return Math.Pow(x, y);
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206 | }
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207 | case OpCodes.SquareRoot: {
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208 | return Math.Sqrt(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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209 | }
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210 | case OpCodes.Root: {
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211 | double x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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212 | double y = Math.Round(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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213 | return Math.Pow(x, 1 / y);
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214 | }
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215 | case OpCodes.Exp: {
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216 | return Math.Exp(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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217 | }
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218 | case OpCodes.Log: {
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219 | return Math.Log(Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues));
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220 | }
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221 | case OpCodes.Gamma: {
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222 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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223 | if (double.IsNaN(x)) return double.NaN;
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224 | else return alglib.gammafunction(x);
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225 | }
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226 | case OpCodes.Psi: {
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227 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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228 | if (double.IsNaN(x)) return double.NaN;
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229 | else if (x.IsAlmost(0.0)) return double.NaN;
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230 | else if ((Math.Floor(x) - x).IsAlmost(0)) return double.NaN;
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231 | return alglib.psi(x);
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232 | }
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233 | case OpCodes.Dawson: {
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234 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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235 | if (double.IsNaN(x)) return double.NaN;
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236 | return alglib.dawsonintegral(x);
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237 | }
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238 | case OpCodes.ExponentialIntegralEi: {
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239 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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240 | if (double.IsNaN(x)) return double.NaN;
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241 | return alglib.exponentialintegralei(x);
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242 | }
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243 | case OpCodes.SineIntegral: {
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244 | double si, ci;
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245 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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246 | if (double.IsNaN(x)) return double.NaN;
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247 | else {
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248 | alglib.sinecosineintegrals(x, out si, out ci);
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249 | return si;
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250 | }
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251 | }
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252 | case OpCodes.CosineIntegral: {
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253 | double si, ci;
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254 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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255 | if (double.IsNaN(x)) return double.NaN;
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256 | else {
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257 | alglib.sinecosineintegrals(x, out si, out ci);
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258 | return ci;
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259 | }
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260 | }
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261 | case OpCodes.HyperbolicSineIntegral: {
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262 | double shi, chi;
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263 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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264 | if (double.IsNaN(x)) return double.NaN;
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265 | else {
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266 | alglib.hyperbolicsinecosineintegrals(x, out shi, out chi);
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267 | return shi;
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268 | }
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269 | }
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270 | case OpCodes.HyperbolicCosineIntegral: {
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271 | double shi, chi;
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272 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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273 | if (double.IsNaN(x)) return double.NaN;
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274 | else {
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275 | alglib.hyperbolicsinecosineintegrals(x, out shi, out chi);
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276 | return chi;
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277 | }
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278 | }
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279 | case OpCodes.FresnelCosineIntegral: {
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280 | double c = 0, s = 0;
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281 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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282 | if (double.IsNaN(x)) return double.NaN;
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283 | else {
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284 | alglib.fresnelintegral(x, ref c, ref s);
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285 | return c;
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286 | }
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287 | }
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288 | case OpCodes.FresnelSineIntegral: {
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289 | double c = 0, s = 0;
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290 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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291 | if (double.IsNaN(x)) return double.NaN;
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292 | else {
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293 | alglib.fresnelintegral(x, ref c, ref s);
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294 | return s;
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295 | }
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296 | }
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297 | case OpCodes.AiryA: {
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298 | double ai, aip, bi, bip;
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299 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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300 | if (double.IsNaN(x)) return double.NaN;
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301 | else {
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302 | alglib.airy(x, out ai, out aip, out bi, out bip);
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303 | return ai;
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304 | }
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305 | }
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306 | case OpCodes.AiryB: {
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307 | double ai, aip, bi, bip;
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308 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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309 | if (double.IsNaN(x)) return double.NaN;
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310 | else {
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311 | alglib.airy(x, out ai, out aip, out bi, out bip);
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312 | return bi;
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313 | }
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314 | }
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315 | case OpCodes.Norm: {
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316 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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317 | if (double.IsNaN(x)) return double.NaN;
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318 | else return alglib.normaldistribution(x);
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319 | }
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320 | case OpCodes.Erf: {
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321 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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322 | if (double.IsNaN(x)) return double.NaN;
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323 | else return alglib.errorfunction(x);
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324 | }
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325 | case OpCodes.Bessel: {
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326 | var x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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327 | if (double.IsNaN(x)) return double.NaN;
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328 | else return alglib.besseli0(x);
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329 | }
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330 | case OpCodes.IfThenElse: {
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331 | double condition = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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332 | double result;
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333 | if (condition > 0.0) {
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334 | result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues); state.SkipInstructions();
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335 | } else {
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336 | state.SkipInstructions(); result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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337 | }
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338 | return result;
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339 | }
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340 | case OpCodes.AND: {
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341 | double result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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342 | for (int i = 1; i < currentInstr.nArguments; i++) {
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343 | if (result > 0.0) result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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344 | else {
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345 | state.SkipInstructions();
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346 | }
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347 | }
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348 | return result > 0.0 ? 1.0 : -1.0;
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349 | }
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350 | case OpCodes.OR: {
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351 | double result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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352 | for (int i = 1; i < currentInstr.nArguments; i++) {
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353 | if (result <= 0.0) result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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354 | else {
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355 | state.SkipInstructions();
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356 | }
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357 | }
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358 | return result > 0.0 ? 1.0 : -1.0;
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359 | }
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360 | case OpCodes.NOT: {
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361 | return Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues) > 0.0 ? -1.0 : 1.0;
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362 | }
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363 | case OpCodes.GT: {
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364 | double x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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365 | double y = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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366 | if (x > y) return 1.0;
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367 | else return -1.0;
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368 | }
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369 | case OpCodes.LT: {
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370 | double x = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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371 | double y = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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372 | if (x < y) return 1.0;
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373 | else return -1.0;
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374 | }
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375 | case OpCodes.TimeLag: {
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376 | var timeLagTreeNode = (LaggedTreeNode)currentInstr.dynamicNode;
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377 | row += timeLagTreeNode.Lag;
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378 | double result = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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379 | row -= timeLagTreeNode.Lag;
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380 | return result;
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381 | }
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382 | case OpCodes.Integral: {
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383 | int savedPc = state.ProgramCounter;
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384 | var timeLagTreeNode = (LaggedTreeNode)currentInstr.dynamicNode;
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385 | double sum = 0.0;
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386 | for (int i = 0; i < Math.Abs(timeLagTreeNode.Lag); i++) {
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387 | row += Math.Sign(timeLagTreeNode.Lag);
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388 | sum += Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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389 | state.ProgramCounter = savedPc;
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390 | }
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391 | row -= timeLagTreeNode.Lag;
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392 | sum += Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
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393 | return sum;
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394 | }
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395 |
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396 | //mkommend: derivate calculation taken from:
|
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397 | //http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/smooth-low-noise-differentiators/
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398 | //one sided smooth differentiatior, N = 4
|
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399 | // y' = 1/8h (f_i + 2f_i-1, -2 f_i-3 - f_i-4)
|
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400 | case OpCodes.Derivative: {
|
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401 | int savedPc = state.ProgramCounter;
|
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402 | double f_0 = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues); row--;
|
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403 | state.ProgramCounter = savedPc;
|
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404 | double f_1 = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues); row -= 2;
|
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405 | state.ProgramCounter = savedPc;
|
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406 | double f_3 = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues); row--;
|
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407 | state.ProgramCounter = savedPc;
|
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408 | double f_4 = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
|
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409 | row += 4;
|
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410 |
|
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411 | return (f_0 + 2 * f_1 - 2 * f_3 - f_4) / 8; // h = 1
|
---|
412 | }
|
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413 | case OpCodes.Call: {
|
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414 | // evaluate sub-trees
|
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415 | double[] argValues = new double[currentInstr.nArguments];
|
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416 | for (int i = 0; i < currentInstr.nArguments; i++) {
|
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417 | argValues[i] = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
|
---|
418 | }
|
---|
419 | // push on argument values on stack
|
---|
420 | state.CreateStackFrame(argValues);
|
---|
421 |
|
---|
422 | // save the pc
|
---|
423 | int savedPc = state.ProgramCounter;
|
---|
424 | // set pc to start of function
|
---|
425 | state.ProgramCounter = (ushort)currentInstr.iArg0;
|
---|
426 | // evaluate the function
|
---|
427 | double v = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
|
---|
428 |
|
---|
429 | // delete the stack frame
|
---|
430 | state.RemoveStackFrame();
|
---|
431 |
|
---|
432 | // restore the pc => evaluation will continue at point after my subtrees
|
---|
433 | state.ProgramCounter = savedPc;
|
---|
434 | return v;
|
---|
435 | }
|
---|
436 | case OpCodes.Arg: {
|
---|
437 | return state.GetStackFrameValue((ushort)currentInstr.iArg0);
|
---|
438 | }
|
---|
439 | case OpCodes.Variable: {
|
---|
440 | if (row < 0 || row >= dataset.Rows) return double.NaN;
|
---|
441 | var variableTreeNode = (VariableTreeNode)currentInstr.dynamicNode;
|
---|
442 | if (row <= lastObservedRow || !cachedPrognosedValues.ContainsKey(variableTreeNode.VariableName)) return ((IList<double>)currentInstr.iArg0)[row] * variableTreeNode.Weight;
|
---|
443 | else return cachedPrognosedValues[variableTreeNode.VariableName][row - lastObservedRow - 1] * variableTreeNode.Weight;
|
---|
444 | }
|
---|
445 | case OpCodes.LagVariable: {
|
---|
446 | var laggedVariableTreeNode = (LaggedVariableTreeNode)currentInstr.dynamicNode;
|
---|
447 | int actualRow = row + laggedVariableTreeNode.Lag;
|
---|
448 | if (actualRow < 0 || actualRow >= dataset.Rows)
|
---|
449 | return double.NaN;
|
---|
450 | if (actualRow <= lastObservedRow || !cachedPrognosedValues.ContainsKey(laggedVariableTreeNode.VariableName)) return ((IList<double>)currentInstr.iArg0)[actualRow] * laggedVariableTreeNode.Weight;
|
---|
451 | else return cachedPrognosedValues[laggedVariableTreeNode.VariableName][actualRow - lastObservedRow - 1] * laggedVariableTreeNode.Weight;
|
---|
452 | }
|
---|
453 | case OpCodes.Constant: {
|
---|
454 | var constTreeNode = (ConstantTreeNode)currentInstr.dynamicNode;
|
---|
455 | return constTreeNode.Value;
|
---|
456 | }
|
---|
457 |
|
---|
458 | //mkommend: this symbol uses the logistic function f(x) = 1 / (1 + e^(-alpha * x) )
|
---|
459 | //to determine the relative amounts of the true and false branch see http://en.wikipedia.org/wiki/Logistic_function
|
---|
460 | case OpCodes.VariableCondition: {
|
---|
461 | if (row < 0 || row >= dataset.Rows)
|
---|
462 | return double.NaN;
|
---|
463 | var variableConditionTreeNode = (VariableConditionTreeNode)currentInstr.dynamicNode;
|
---|
464 | double variableValue;
|
---|
465 | if (row <= lastObservedRow || !cachedPrognosedValues.ContainsKey(variableConditionTreeNode.VariableName))
|
---|
466 | variableValue = ((IList<double>)currentInstr.iArg0)[row];
|
---|
467 | else
|
---|
468 | variableValue = cachedPrognosedValues[variableConditionTreeNode.VariableName][row - lastObservedRow - 1];
|
---|
469 |
|
---|
470 | double x = variableValue - variableConditionTreeNode.Threshold;
|
---|
471 | double p = 1 / (1 + Math.Exp(-variableConditionTreeNode.Slope * x));
|
---|
472 |
|
---|
473 | double trueBranch = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
|
---|
474 | double falseBranch = Evaluate(dataset, ref row, lastObservedRow, state, cachedPrognosedValues);
|
---|
475 |
|
---|
476 | return trueBranch * p + falseBranch * (1 - p);
|
---|
477 | }
|
---|
478 | default: throw new NotSupportedException();
|
---|
479 | }
|
---|
480 | }
|
---|
481 | }
|
---|
482 | }
|
---|