1 | using System;
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2 | using System.Collections;
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3 | using System.Collections.Generic;
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4 | using System.Collections.ObjectModel;
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5 | using System.Collections.Specialized;
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6 | using System.Drawing.Design;
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7 | using System.Linq;
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8 | using HeuristicLab.Common;
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9 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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10 | using HeuristicLab.Problems.DataAnalysis;
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11 | using HeuristicLab.Problems.DataAnalysis.Symbolic;
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12 |
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13 | namespace HeuristicLab.Problems.GeneticProgramming.GlucosePrediction {
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14 | public static class Interpreter {
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15 | private class Data {
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16 | public double[] realGluc;
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17 | public double[] realIns;
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18 | public double[] realCh;
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19 | public Dictionary<ISymbolicExpressionTreeNode, double[]> precalculatedValues;
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20 | }
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21 |
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22 | public static IEnumerable<double> Apply(ISymbolicExpressionTreeNode model, IDataset dataset, IEnumerable<int> rows) {
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23 | double[] targetGluc = dataset.GetDoubleValues("Glucose_target", rows).ToArray(); // only for skipping rows for which we should not produce an output
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24 |
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25 | var data = new Data {
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26 | realGluc = dataset.GetDoubleValues("Glucose_Interpol", rows).ToArray(),
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27 | realIns = dataset.GetDoubleValues("Insuline", rows).ToArray(),
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28 | realCh = dataset.GetDoubleValues("CH", rows).ToArray(),
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29 | precalculatedValues = CreatePrecalculatedValues(model, dataset)
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30 | };
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31 | var predictions = new double[targetGluc.Length];
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32 | var rowsEnumerator = rows.GetEnumerator();
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33 | for (int k = 0; k < predictions.Length; k++, rowsEnumerator.MoveNext()) {
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34 | if (double.IsNaN(targetGluc[k])) {
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35 | predictions[k] = double.NaN;
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36 | } else {
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37 | var rawPred = InterpretRec(model, data, rowsEnumerator.Current);
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38 | predictions[k] = rawPred;
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39 | }
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40 | }
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41 | return predictions;
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42 | }
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43 |
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44 | private static Dictionary<ISymbolicExpressionTreeNode, double[]> CreatePrecalculatedValues(ISymbolicExpressionTreeNode root, IDataset dataset) {
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45 | var dict = new Dictionary<ISymbolicExpressionTreeNode, double[]>();
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46 | // here we integrate ins or ch inputs over the whole day to generate smoothed ins/ch values with the same number of rows
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47 | // the integrated values are reset to zero whenever a new evluation period starts
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48 | foreach (var node in root.IterateNodesPrefix()) {
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49 | var curvedInsNode = node as CurvedInsVariableTreeNode;
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50 | var curvedChNode = node as CurvedChVariableTreeNode;
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51 | if (curvedInsNode != null) {
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52 | dict.Add(curvedInsNode, Integrate(curvedInsNode, dataset));
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53 | } else if (curvedChNode != null) {
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54 | dict.Add(curvedChNode, Integrate(curvedChNode, dataset));
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55 | }
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56 | }
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57 | return dict;
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58 | }
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59 |
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60 | private static double[] Integrate(CurvedInsVariableTreeNode node, IDataset dataset) {
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61 | // d Q1 / dt = ins(t) - alpha * Q1(t)
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62 | // d Q2 / dt = alpha * (Q1(t) - Q2(t))
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63 | // S = Q1(t) + Q2(t)
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64 | var alpha = node.Alpha;
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65 |
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66 | var ins = dataset.GetReadOnlyDoubleValues("Insuline");
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67 | var time = dataset.GetReadOnlyDoubleValues("HourMin").ToArray();
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68 |
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69 | // TODO reset for new time intervals
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70 |
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71 | double q1, q2, q1_prev, q2_prev;
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72 | // starting values: zeros
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73 | q1 = q2 = q1_prev = q2_prev = 0;
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74 | double[] s = new double[dataset.Rows];
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75 |
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76 | for (int t = 1; t < dataset.Rows; t++) {
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77 | if (IsStartOfNewPeriod(time, t)) {
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78 | q1 = q2 = q1_prev = q2_prev = 0;
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79 | }
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80 | q1 = q1_prev + ins[t] - alpha * q1_prev;
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81 | q2 = q2_prev + alpha * (q1_prev - q2_prev);
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82 | s[t] = q1 + q2;
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83 | q1_prev = q1;
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84 | q2_prev = q2;
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85 |
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86 | }
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87 | return s;
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88 | }
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89 |
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90 | private static bool IsStartOfNewPeriod(double[] time, int t) {
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91 | return t == 0 ||
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92 | (time[t].IsAlmost(2005) && !time[t - 1].IsAlmost(2000));
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93 | }
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94 |
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95 |
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96 | private static double[] Integrate(CurvedChVariableTreeNode node, IDataset dataset) {
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97 | // d Q1 / dt = ins(t) - alpha * Q1(t)
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98 | // d Q2 / dt = alpha * (Q1(t) - Q2(t))
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99 | // S = Q1(t) + Q2(t)
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100 | var alpha = node.Alpha;
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101 |
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102 | var ch = dataset.GetReadOnlyDoubleValues("CH");
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103 | var time = dataset.GetReadOnlyDoubleValues("HourMin").ToArray();
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104 |
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105 | // TODO reset for new time intervals
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106 |
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107 | double q1, q2, q1_prev, q2_prev;
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108 | // starting values: zeros
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109 | q1 = q2 = q1_prev = q2_prev = 0;
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110 | double[] s = new double[dataset.Rows];
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111 |
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112 | for (int t = 1; t < dataset.Rows; t++) {
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113 | if (IsStartOfNewPeriod(time, t)) {
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114 | q1 = q2 = q1_prev = q2_prev = 0;
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115 | }
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116 | q1 = q1_prev + ch[t] - alpha * q1_prev;
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117 | q2 = q2_prev + alpha * (q1_prev - q2_prev);
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118 | s[t] = q1 + q2;
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119 | q1_prev = q1;
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120 | q2_prev = q2;
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121 |
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122 | }
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123 | return s;
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124 | }
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125 |
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126 | private static double InterpretRec(ISymbolicExpressionTreeNode node, Data data, int k) {
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127 | if (node.Symbol is SimpleSymbol) {
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128 | switch (node.Symbol.Name) {
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129 | case "+":
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130 | case "+Ins":
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131 | case "+Ch": {
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132 | return InterpretRec(node.GetSubtree(0), data, k) + InterpretRec(node.GetSubtree(1), data, k);
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133 | }
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134 | case "-":
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135 | case "-Ins":
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136 | case "-Ch": {
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137 | return InterpretRec(node.GetSubtree(0), data, k) - InterpretRec(node.GetSubtree(1), data, k);
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138 | }
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139 | case "*":
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140 | case "*Ins":
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141 | case "*Ch": {
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142 | return InterpretRec(node.GetSubtree(0), data, k) * InterpretRec(node.GetSubtree(1), data, k);
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143 | }
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144 | case "/Ch":
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145 | case "/Ins":
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146 | case "/": {
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147 | return InterpretRec(node.GetSubtree(0), data, k) / InterpretRec(node.GetSubtree(1), data, k);
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148 | }
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149 | case "Exp":
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150 | case "ExpIns":
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151 | case "ExpCh": {
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152 | return Math.Exp(InterpretRec(node.GetSubtree(0), data, k));
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153 | }
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154 | case "Sin":
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155 | case "SinIns":
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156 | case "SinCh": {
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157 | return Math.Sin(InterpretRec(node.GetSubtree(0), data, k));
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158 | }
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159 | case "CosCh":
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160 | case "CosIns":
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161 | case "Cos": {
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162 | return Math.Cos(InterpretRec(node.GetSubtree(0), data, k));
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163 | }
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164 | case "LogCh":
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165 | case "LogIns":
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166 | case "Log": {
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167 | return Math.Log(InterpretRec(node.GetSubtree(0), data, k));
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168 | }
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169 | case "Func": {
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170 | // <exprgluc> + <exprch> - <exprins>
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171 | return InterpretRec(node.GetSubtree(0), data, k)
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172 | + InterpretRec(node.GetSubtree(1), data, k)
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173 | - InterpretRec(node.GetSubtree(2), data, k);
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174 | }
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175 | case "ExprGluc": {
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176 | return InterpretRec(node.GetSubtree(0), data, k);
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177 | }
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178 | case "ExprCh": {
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179 | return InterpretRec(node.GetSubtree(0), data, k);
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180 | }
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181 | case "ExprIns": {
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182 | return InterpretRec(node.GetSubtree(0), data, k);
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183 | }
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184 | default: {
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185 | throw new InvalidProgramException("Found an unknown symbol " + node.Symbol);
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186 | }
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187 | }
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188 | } else if (node.Symbol is PredictedGlucoseVariableSymbol) {
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189 | throw new NotSupportedException();
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190 | } else if (node.Symbol is RealGlucoseVariableSymbol) {
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191 | var n = (RealGlucoseVariableTreeNode)node;
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192 | if (k + n.RowOffset < 0 || k + n.RowOffset >= data.realGluc.Length) return double.NaN;
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193 | return data.realGluc[k + n.RowOffset];
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194 | } else if (node.Symbol is CurvedChVariableSymbol) {
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195 | return data.precalculatedValues[node][k];
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196 | } else if (node.Symbol is RealInsulineVariableSymbol) {
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197 | throw new NotSupportedException();
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198 | } else if (node.Symbol is CurvedInsVariableSymbol) {
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199 | return data.precalculatedValues[node][k];
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200 | } else if (node.Symbol is Constant) {
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201 | var n = (ConstantTreeNode)node;
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202 | return n.Value;
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203 | } else {
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204 | throw new InvalidProgramException("found unknown symbol " + node.Symbol);
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205 | }
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206 | }
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207 |
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208 | private static double Beta(double x, double alpha, double beta) {
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209 | return 1.0 / alglib.beta(alpha, beta) * Math.Pow(x, alpha - 1) * Math.Pow(1 - x, beta - 1);
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210 | }
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211 | }
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212 | }
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