1 | #region License Information
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2 | /* HeuristicLab
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3 | * Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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4 | *
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5 | * This file is part of HeuristicLab.
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6 | *
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7 | * HeuristicLab is free software: you can redistribute it and/or modify
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8 | * it under the terms of the GNU General Public License as published by
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9 | * the Free Software Foundation, either version 3 of the License, or
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10 | * (at your option) any later version.
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11 | *
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12 | * HeuristicLab is distributed in the hope that it will be useful,
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13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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15 | * GNU General Public License for more details.
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16 | *
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17 | * You should have received a copy of the GNU General Public License
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18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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19 | */
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20 | #endregion
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21 |
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22 | using System;
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23 | using System.Collections.Generic;
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24 | using System.Linq;
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25 | using System.Text;
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26 | using HeuristicLab.DataAnalysis;
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27 | using HeuristicLab.Core;
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28 | using System.Xml;
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29 | using System.Diagnostics;
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30 |
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31 | namespace HeuristicLab.Functions {
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32 | internal class BakedTreeEvaluator : IEvaluator {
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33 | private const int MAX_TREE_SIZE = 4096;
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34 | private const double EPSILON = 1.0e-7;
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35 |
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36 | private class Instr {
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37 | public double d_arg0;
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38 | public int i_arg0;
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39 | public int i_arg1;
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40 | public int arity;
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41 | public int symbol;
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42 | }
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43 |
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44 | private Instr[] codeArr;
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45 | private int PC;
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46 | private Dataset dataset;
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47 | private int sampleIndex;
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48 |
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49 |
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50 | public BakedTreeEvaluator(Dataset dataset) {
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51 | this.dataset = dataset;
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52 | codeArr = new Instr[MAX_TREE_SIZE];
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53 | for(int i = 0; i < MAX_TREE_SIZE; i++) {
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54 | codeArr[i] = new Instr();
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55 | }
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56 | }
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57 |
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58 | public void ResetEvaluator(IFunctionTree functionTree) {
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59 | List<LightWeightFunction> linearRepresentation = ((BakedFunctionTree)functionTree).LinearRepresentation;
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60 | int i = 0;
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61 | foreach(LightWeightFunction f in linearRepresentation) {
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62 | TranslateToInstr(f, codeArr[i++]);
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63 | }
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64 | }
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65 |
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66 | private Instr TranslateToInstr(LightWeightFunction f, Instr instr) {
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67 | instr.arity = f.arity;
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68 | instr.symbol = EvaluatorSymbolTable.MapFunction(f.functionType);
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69 | switch(instr.symbol) {
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70 | case EvaluatorSymbolTable.VARIABLE: {
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71 | instr.i_arg0 = (int)f.data[0]; // var
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72 | instr.d_arg0 = f.data[1]; // weight
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73 | instr.i_arg1 = (int)f.data[2]; // sample-offset
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74 | break;
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75 | }
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76 | case EvaluatorSymbolTable.CONSTANT: {
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77 | instr.d_arg0 = f.data[0]; // value
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78 | break;
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79 | }
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80 | }
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81 | return instr;
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82 | }
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83 |
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84 | public double Evaluate(int sampleIndex) {
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85 | PC = 0;
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86 | this.sampleIndex = sampleIndex;
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87 | return EvaluateBakedCode();
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88 | }
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89 |
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90 | private double EvaluateBakedCode() {
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91 | Instr currInstr = codeArr[PC++];
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92 | switch(currInstr.symbol) {
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93 | case EvaluatorSymbolTable.VARIABLE: {
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94 | int row = sampleIndex + currInstr.i_arg1;
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95 | if(row < 0 || row >= dataset.Rows) return double.NaN;
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96 | else return currInstr.d_arg0 * dataset.GetValue(row, currInstr.i_arg0);
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97 | }
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98 | case EvaluatorSymbolTable.CONSTANT: {
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99 | return currInstr.d_arg0;
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100 | }
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101 | case EvaluatorSymbolTable.DIFFERENTIAL: {
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102 | int row = sampleIndex + currInstr.i_arg1;
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103 | if(row < 1 || row >= dataset.Rows) return double.NaN;
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104 | else return currInstr.d_arg0 * (dataset.GetValue(row, currInstr.i_arg0) - dataset.GetValue(row - 1, currInstr.i_arg0));
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105 | }
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106 | case EvaluatorSymbolTable.MULTIPLICATION: {
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107 | double result = EvaluateBakedCode();
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108 | for(int i = 1; i < currInstr.arity; i++) {
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109 | result *= EvaluateBakedCode();
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110 | }
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111 | return result;
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112 | }
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113 | case EvaluatorSymbolTable.ADDITION: {
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114 | double sum = EvaluateBakedCode();
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115 | for(int i = 1; i < currInstr.arity; i++) {
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116 | sum += EvaluateBakedCode();
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117 | }
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118 | return sum;
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119 | }
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120 | case EvaluatorSymbolTable.SUBTRACTION: {
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121 | if(currInstr.arity == 1) {
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122 | return -EvaluateBakedCode();
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123 | } else {
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124 | double result = EvaluateBakedCode();
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125 | for(int i = 1; i < currInstr.arity; i++) {
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126 | result -= EvaluateBakedCode();
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127 | }
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128 | return result;
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129 | }
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130 | }
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131 | case EvaluatorSymbolTable.DIVISION: {
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132 | double result;
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133 | if(currInstr.arity == 1) {
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134 | result = 1.0 / EvaluateBakedCode();
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135 | } else {
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136 | result = EvaluateBakedCode();
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137 | for(int i = 1; i < currInstr.arity; i++) {
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138 | result /= EvaluateBakedCode();
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139 | }
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140 | }
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141 | if(double.IsInfinity(result)) return 0.0;
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142 | else return result;
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143 | }
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144 | case EvaluatorSymbolTable.AVERAGE: {
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145 | double sum = EvaluateBakedCode();
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146 | for(int i = 1; i < currInstr.arity; i++) {
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147 | sum += EvaluateBakedCode();
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148 | }
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149 | return sum / currInstr.arity;
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150 | }
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151 | case EvaluatorSymbolTable.COSINUS: {
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152 | return Math.Cos(EvaluateBakedCode());
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153 | }
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154 | case EvaluatorSymbolTable.SINUS: {
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155 | return Math.Sin(EvaluateBakedCode());
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156 | }
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157 | case EvaluatorSymbolTable.EXP: {
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158 | return Math.Exp(EvaluateBakedCode());
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159 | }
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160 | case EvaluatorSymbolTable.LOG: {
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161 | return Math.Log(EvaluateBakedCode());
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162 | }
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163 | case EvaluatorSymbolTable.POWER: {
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164 | double x = EvaluateBakedCode();
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165 | double p = EvaluateBakedCode();
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166 | return Math.Pow(x, p);
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167 | }
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168 | case EvaluatorSymbolTable.SIGNUM: {
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169 | double value = EvaluateBakedCode();
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170 | if(double.IsNaN(value)) return double.NaN;
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171 | else return Math.Sign(value);
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172 | }
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173 | case EvaluatorSymbolTable.SQRT: {
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174 | return Math.Sqrt(EvaluateBakedCode());
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175 | }
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176 | case EvaluatorSymbolTable.TANGENS: {
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177 | return Math.Tan(EvaluateBakedCode());
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178 | }
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179 | case EvaluatorSymbolTable.AND: { // only defined for inputs 1 and 0
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180 | double result = 1.0;
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181 | // have to evaluate all sub-trees, skipping would probably not lead to a big gain because
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182 | // we have to iterate over the linear structure anyway
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183 | for(int i = 0; i < currInstr.arity; i++) {
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184 | double x = EvaluateBakedCode();
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185 | Debug.Assert(x == 0.0 || x == 1.0);
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186 | result *= x;
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187 | }
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188 | return result;
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189 | }
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190 | case EvaluatorSymbolTable.EQU: {
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191 | double x = EvaluateBakedCode();
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192 | double y = EvaluateBakedCode();
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193 | if(Math.Abs(x - y) < EPSILON) return 1.0; else return 0.0;
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194 | }
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195 | case EvaluatorSymbolTable.GT: {
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196 | double x = EvaluateBakedCode();
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197 | double y = EvaluateBakedCode();
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198 | if(x > y) return 1.0;
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199 | else return 0.0;
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200 | }
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201 | case EvaluatorSymbolTable.IFTE: { // only defined for condition 0 or 1
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202 | double condition = EvaluateBakedCode();
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203 | Debug.Assert(condition == 0.0 || condition == 1.0);
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204 | double x = EvaluateBakedCode();
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205 | double y = EvaluateBakedCode();
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206 | if(condition == 0.0) return x;
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207 | else return y;
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208 | }
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209 | case EvaluatorSymbolTable.LT: {
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210 | double x = EvaluateBakedCode();
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211 | double y = EvaluateBakedCode();
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212 | if(x < y) return 1.0;
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213 | else return 0.0;
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214 | }
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215 | case EvaluatorSymbolTable.NOT: { // only defined for inputs 0 or 1
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216 | double result = EvaluateBakedCode();
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217 | Debug.Assert(result == 0.0 || result == 1.0);
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218 | if(result == 0.0) return 1.0;
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219 | else return 0.0;
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220 | }
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221 | case EvaluatorSymbolTable.OR: { // only defined for inputs 0 or 1
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222 | double result = 0.0; // default is false
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223 | for(int i = 0; i < currInstr.arity; i++) {
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224 | double x = EvaluateBakedCode();
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225 | Debug.Assert(x == 0.0 || x == 1.0);
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226 | if(x == 1.0) result = 1.0;
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227 | }
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228 | return result;
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229 | }
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230 | case EvaluatorSymbolTable.XOR: { // only defined for inputs 0 or 1
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231 | double x = EvaluateBakedCode();
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232 | double y = EvaluateBakedCode();
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233 | return Math.Abs(x - y);
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234 | }
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235 | default: {
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236 | throw new NotImplementedException();
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237 | }
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238 | }
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239 | }
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240 | }
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241 | }
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