1 | /* ======================================================================== *\
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2 | !
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3 | ! *
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4 | ! * This file is part of MARS, the MAGIC Analysis and Reconstruction
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5 | ! * Software. It is distributed to you in the hope that it can be a useful
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6 | ! * and timesaving tool in analysing Data of imaging Cerenkov telescopes.
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7 | ! * It is distributed WITHOUT ANY WARRANTY.
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8 | ! *
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9 | ! * Permission to use, copy, modify and distribute this software and its
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10 | ! * documentation for any purpose is hereby granted without fee,
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11 | ! * provided that the above copyright notice appear in all copies and
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12 | ! * that both that copyright notice and this permission notice appear
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13 | ! * in supporting documentation. It is provided "as is" without express
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14 | ! * or implied warranty.
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15 | ! *
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16 | !
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17 | !
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18 | ! Author(s): Thomas Hengstebeck 3/2003 <mailto:hengsteb@physik.hu-berlin.de>
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19 | !
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20 | ! Copyright: MAGIC Software Development, 2000-2005
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21 | !
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22 | !
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23 | \* ======================================================================== */
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24 |
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25 | /////////////////////////////////////////////////////////////////////////////
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26 | //
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27 | // MRanForest
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28 | //
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29 | // ParameterContainer for Forest structure
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30 | //
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31 | // A random forest can be grown by calling GrowForest.
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32 | // In advance SetupGrow must be called in order to initialize arrays and
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33 | // do some preprocessing.
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34 | // GrowForest() provides the training data for a single tree (bootstrap
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35 | // aggregate procedure)
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36 | //
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37 | // Essentially two random elements serve to provide a "random" forest,
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38 | // namely bootstrap aggregating (which is done in GrowForest()) and random
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39 | // split selection (which is subject to MRanTree::GrowTree())
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40 | //
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41 | /////////////////////////////////////////////////////////////////////////////
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42 | #include "MRanForest.h"
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43 |
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44 | #include <TVector.h>
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45 | #include <TRandom.h>
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46 |
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47 | #include "MHMatrix.h"
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48 | #include "MRanTree.h"
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49 | #include "MData.h"
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50 | #include "MDataArray.h"
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51 | #include "MParList.h"
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52 |
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53 | #include "MLog.h"
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54 | #include "MLogManip.h"
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55 |
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56 | ClassImp(MRanForest);
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57 |
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58 | using namespace std;
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59 |
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60 | // --------------------------------------------------------------------------
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61 | //
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62 | // Default constructor.
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63 | //
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64 | MRanForest::MRanForest(const char *name, const char *title)
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65 | : fClassify(kTRUE), fNumTrees(100), fNumTry(0), fNdSize(1),
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66 | fRanTree(NULL), fRules(NULL), fMatrix(NULL), fUserVal(-1)
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67 | {
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68 | fName = name ? name : "MRanForest";
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69 | fTitle = title ? title : "Storage container for Random Forest";
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70 |
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71 | fForest=new TObjArray();
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72 | fForest->SetOwner(kTRUE);
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73 | }
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74 |
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75 | MRanForest::MRanForest(const MRanForest &rf)
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76 | {
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77 | // Copy constructor
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78 | fName = rf.fName;
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79 | fTitle = rf.fTitle;
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80 |
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81 | fClassify = rf.fClassify;
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82 | fNumTrees = rf.fNumTrees;
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83 | fNumTry = rf.fNumTry;
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84 | fNdSize = rf.fNdSize;
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85 | fTreeNo = rf.fTreeNo;
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86 | fRanTree = NULL;
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87 |
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88 | fRules=new MDataArray();
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89 | fRules->Reset();
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90 |
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91 | MDataArray *newrules=rf.fRules;
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92 |
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93 | for(Int_t i=0;i<newrules->GetNumEntries();i++)
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94 | {
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95 | MData &data=(*newrules)[i];
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96 | fRules->AddEntry(data.GetRule());
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97 | }
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98 |
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99 | // trees
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100 | fForest=new TObjArray();
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101 | fForest->SetOwner(kTRUE);
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102 |
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103 | TObjArray *newforest=rf.fForest;
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104 | for(Int_t i=0;i<newforest->GetEntries();i++)
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105 | {
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106 | MRanTree *rantree=(MRanTree*)newforest->At(i);
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107 |
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108 | MRanTree *newtree=new MRanTree(*rantree);
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109 | fForest->Add(newtree);
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110 | }
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111 |
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112 | fHadTrue = rf.fHadTrue;
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113 | fHadEst = rf.fHadEst;
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114 | fDataSort = rf.fDataSort;
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115 | fDataRang = rf.fDataRang;
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116 | fClassPop = rf.fClassPop;
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117 | fWeight = rf.fWeight;
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118 | fTreeHad = rf.fTreeHad;
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119 |
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120 | fNTimesOutBag = rf.fNTimesOutBag;
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121 |
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122 | }
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123 |
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124 | // --------------------------------------------------------------------------
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125 | // Destructor.
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126 | MRanForest::~MRanForest()
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127 | {
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128 | delete fForest;
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129 | if (fMatrix)
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130 | delete fMatrix;
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131 | if (fRules)
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132 | delete fRules;
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133 | }
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134 |
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135 | MRanTree *MRanForest::GetTree(Int_t i)
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136 | {
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137 | return (MRanTree*)(fForest->At(i));
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138 | }
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139 |
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140 | void MRanForest::SetNumTrees(Int_t n)
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141 | {
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142 | //at least 1 tree
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143 | fNumTrees=TMath::Max(n,1);
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144 | fTreeHad.Set(fNumTrees);
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145 | fTreeHad.Reset();
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146 | }
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147 |
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148 | void MRanForest::SetNumTry(Int_t n)
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149 | {
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150 | fNumTry=TMath::Max(n,0);
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151 | }
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152 |
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153 | void MRanForest::SetNdSize(Int_t n)
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154 | {
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155 | fNdSize=TMath::Max(n,1);
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156 | }
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157 |
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158 | void MRanForest::SetWeights(const TArrayF &weights)
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159 | {
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160 | fWeight=weights;
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161 | }
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162 |
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163 | void MRanForest::SetGrid(const TArrayD &grid)
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164 | {
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165 | const int n=grid.GetSize();
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166 |
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167 | for(int i=0;i<n-1;i++)
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168 | if(grid[i]>=grid[i+1])
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169 | {
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170 | *fLog<<warn<<"Grid points must be in increasing order! Ignoring grid."<<endl;
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171 | return;
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172 | }
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173 |
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174 | fGrid=grid;
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175 |
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176 | //*fLog<<inf<<"Following "<<n<<" grid points are used:"<<endl;
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177 | //for(int i=0;i<n;i++)
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178 | // *fLog<<inf<<" "<<i<<") "<<fGrid[i]<<endl;
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179 | }
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180 |
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181 | Int_t MRanForest::GetNumDim() const
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182 | {
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183 | return fMatrix ? fMatrix->GetNcols() : 0;
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184 | }
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185 |
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186 | Int_t MRanForest::GetNumData() const
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187 | {
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188 | return fMatrix ? fMatrix->GetNrows() : 0;
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189 | }
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190 |
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191 | Int_t MRanForest::GetNclass() const
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192 | {
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193 | int maxidx = TMath::LocMax(fClass.GetSize(),fClass.GetArray());
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194 |
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195 | return int(fClass[maxidx])+1;
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196 | }
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197 |
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198 | void MRanForest::PrepareClasses()
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199 | {
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200 | const int numdata=fHadTrue.GetSize();
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201 |
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202 | if(fGrid.GetSize()>0)
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203 | {
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204 | // classes given by grid
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205 | const int ngrid=fGrid.GetSize();
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206 |
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207 | for(int j=0;j<numdata;j++)
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208 | {
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209 | // Array is supposed to be sorted prior to this call.
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210 | // If match is found, function returns position of element.
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211 | // If no match found, function gives nearest element smaller
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212 | // than value.
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213 | int k=TMath::BinarySearch(ngrid, fGrid.GetArray(), fHadTrue[j]);
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214 |
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215 | fClass[j] = k;
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216 | }
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217 |
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218 | int minidx = TMath::LocMin(fClass.GetSize(),fClass.GetArray());
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219 | int min = fClass[minidx];
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220 | if(min!=0) for(int j=0;j<numdata;j++)fClass[j]-=min;
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221 |
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222 | }else{
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223 | // classes directly given
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224 | for (Int_t j=0;j<numdata;j++)
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225 | fClass[j] = int(fHadTrue[j]+0.5);
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226 | }
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227 | }
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228 |
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229 | Double_t MRanForest::CalcHadroness()
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230 | {
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231 | TVector event;
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232 | *fRules >> event;
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233 |
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234 | return CalcHadroness(event);
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235 | }
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236 |
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237 | Double_t MRanForest::CalcHadroness(const TVector &event)
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238 | {
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239 | Double_t hadroness=0;
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240 | Int_t ntree=0;
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241 |
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242 | TIter Next(fForest);
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243 |
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244 | MRanTree *tree;
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245 | while ((tree=(MRanTree*)Next()))
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246 | hadroness += (fTreeHad[ntree++]=tree->TreeHad(event));
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247 |
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248 | return hadroness/ntree;
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249 | }
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250 |
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251 | Bool_t MRanForest::AddTree(MRanTree *rantree=NULL)
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252 | {
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253 | fRanTree = rantree ? rantree : fRanTree;
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254 |
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255 | if (!fRanTree) return kFALSE;
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256 |
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257 | MRanTree *newtree=new MRanTree(*fRanTree);
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258 | fForest->Add(newtree);
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259 |
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260 | return kTRUE;
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261 | }
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262 |
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263 | Bool_t MRanForest::SetupGrow(MHMatrix *mat,MParList *plist)
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264 | {
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265 | //-------------------------------------------------------------------
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266 | // access matrix, copy last column (target) preliminarily
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267 | // into fHadTrue
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268 | if (fMatrix)
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269 | delete fMatrix;
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270 | fMatrix = new TMatrix(mat->GetM());
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271 |
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272 | int dim = fMatrix->GetNcols()-1;
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273 | int numdata = fMatrix->GetNrows();
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274 |
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275 | fHadTrue.Set(numdata);
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276 | fHadTrue.Reset(0);
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277 |
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278 | for (Int_t j=0;j<numdata;j++)
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279 | fHadTrue[j] = (*fMatrix)(j,dim);
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280 |
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281 | // remove last col
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282 | fMatrix->ResizeTo(numdata,dim);
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283 |
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284 | //-------------------------------------------------------------------
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285 | // setup labels for classification/regression
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286 | fClass.Set(numdata);
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287 | fClass.Reset(0);
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288 |
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289 | if (fClassify)
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290 | PrepareClasses();
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291 |
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292 | //-------------------------------------------------------------------
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293 | // allocating and initializing arrays
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294 | fHadEst.Set(numdata);
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295 | fHadEst.Reset(0);
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296 |
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297 | fNTimesOutBag.Set(numdata);
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298 | fNTimesOutBag.Reset(0);
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299 |
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300 | fDataSort.Set(dim*numdata);
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301 | fDataSort.Reset(0);
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302 |
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303 | fDataRang.Set(dim*numdata);
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304 | fDataRang.Reset(0);
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305 |
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306 | if(fWeight.GetSize()!=numdata)
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307 | {
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308 | fWeight.Set(numdata);
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309 | fWeight.Reset(1.);
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310 | *fLog << inf <<"Setting weights to 1 (no weighting)"<< endl;
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311 | }
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312 |
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313 | //-------------------------------------------------------------------
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314 | // setup rules to be used for classification/regression
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315 | const MDataArray *allrules=(MDataArray*)mat->GetColumns();
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316 | if(allrules==NULL)
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317 | {
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318 | *fLog << err <<"Rules of matrix == null, exiting"<< endl;
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319 | return kFALSE;
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320 | }
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321 |
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322 | if (fRules)
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323 | delete fRules;
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324 | fRules = new MDataArray();
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325 | fRules->Reset();
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326 |
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327 | const TString target_rule = (*allrules)[dim].GetRule();
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328 | for (Int_t i=0;i<dim;i++)
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329 | fRules->AddEntry((*allrules)[i].GetRule());
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330 |
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331 | *fLog << inf << endl;
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332 | *fLog << "Setting up RF for training on target:" << endl;
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333 | *fLog << " " << target_rule.Data() << endl;
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334 | *fLog << "Following rules are used as input to RF:" << endl;
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335 | for (Int_t i=0;i<dim;i++)
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336 | *fLog << " " << i << ") " << (*fRules)[i].GetRule() << endl;
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337 |
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338 | *fLog << endl;
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339 |
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340 | //-------------------------------------------------------------------
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341 | // prepare (sort) data for fast optimization algorithm
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342 | if (!CreateDataSort())
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343 | return kFALSE;
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344 |
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345 | //-------------------------------------------------------------------
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346 | // access and init tree container
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347 | fRanTree = (MRanTree*)plist->FindCreateObj("MRanTree");
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348 | if(!fRanTree)
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349 | {
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350 | *fLog << err << dbginf << "MRanForest, fRanTree not initialized... aborting." << endl;
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351 | return kFALSE;
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352 | }
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353 |
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354 | const Int_t tryest = TMath::Nint(TMath::Sqrt(dim));
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355 |
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356 | *fLog << inf << endl;
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357 | *fLog << "Following input for the tree growing are used:"<<endl;
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358 | *fLog << " Number of Trees : "<<fNumTrees<<endl;
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359 | *fLog << " Number of Trials: "<<(fNumTry==0?tryest:fNumTry)<<(fNumTry==0?" (auto)":"")<<endl;
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360 | *fLog << " Final Node size : "<<fNdSize<<endl;
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361 | *fLog << " Using Grid : "<<(fGrid.GetSize()>0?"Yes":"No")<<endl;
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362 | *fLog << " Number of Events: "<<numdata<<endl;
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363 | *fLog << " Number of Params: "<<dim<<endl;
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364 |
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365 | if(fNumTry==0)
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366 | {
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367 | fNumTry=tryest;
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368 | *fLog << inf << endl;
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369 | *fLog << "Set no. of trials to the recommended value of round(";
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370 | *fLog << TMath::Sqrt(dim) << ") = " << fNumTry << endl;
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371 | }
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372 |
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373 | fRanTree->SetNumTry(fNumTry);
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374 | fRanTree->SetClassify(fClassify);
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375 | fRanTree->SetNdSize(fNdSize);
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376 |
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377 | fTreeNo=0;
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378 |
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379 | return kTRUE;
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380 | }
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381 |
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382 | Bool_t MRanForest::GrowForest()
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383 | {
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384 | if(!gRandom)
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385 | {
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386 | *fLog << err << dbginf << "gRandom not initialized... aborting." << endl;
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387 | return kFALSE;
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388 | }
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389 |
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390 | fTreeNo++;
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391 |
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392 | //-------------------------------------------------------------------
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393 | // initialize running output
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394 |
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395 | float minfloat=fHadTrue[TMath::LocMin(fHadTrue.GetSize(),fHadTrue.GetArray())];
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396 | Bool_t calcResolution=(minfloat>0.001);
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397 |
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398 | if (fTreeNo==1)
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399 | {
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400 | *fLog << inf << endl << underline;
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401 |
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402 | if(calcResolution)
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403 | *fLog << "no. of tree no. of nodes resolution in % (from oob-data -> overest. of error)" << endl;
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404 | else
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405 | *fLog << "no. of tree no. of nodes rms in % (from oob-data -> overest. of error)" << endl;
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406 | // 12345678901234567890123456789012345678901234567890
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407 | }
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408 |
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409 | const Int_t numdata = GetNumData();
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410 | const Int_t nclass = GetNclass();
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411 |
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412 | //-------------------------------------------------------------------
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413 | // bootstrap aggregating (bagging) -> sampling with replacement:
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414 |
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415 | TArrayF classpopw(nclass);
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416 | TArrayI jinbag(numdata); // Initialization includes filling with 0
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417 | TArrayF winbag(numdata); // Initialization includes filling with 0
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418 |
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419 | float square=0;
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420 | float mean=0;
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421 |
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422 | for (Int_t n=0; n<numdata; n++)
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423 | {
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424 | // The integer k is randomly (uniformly) chosen from the set
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425 | // {0,1,...,numdata-1}, which is the set of the index numbers of
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426 | // all events in the training sample
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427 |
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428 | const Int_t k = Int_t(gRandom->Rndm()*numdata);
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429 |
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430 | if(fClassify)
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431 | classpopw[fClass[k]]+=fWeight[k];
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432 | else
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433 | classpopw[0]+=fWeight[k];
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434 |
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435 | mean +=fHadTrue[k]*fWeight[k];
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436 | square+=fHadTrue[k]*fHadTrue[k]*fWeight[k];
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437 |
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438 | winbag[k]+=fWeight[k];
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439 | jinbag[k]=1;
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440 |
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441 | }
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442 |
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443 | //-------------------------------------------------------------------
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444 | // modifying sorted-data array for in-bag data:
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445 |
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446 | // In bagging procedure ca. 2/3 of all elements in the original
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447 | // training sample are used to build the in-bag data
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448 | TArrayI datsortinbag=fDataSort;
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449 | Int_t ninbag=0;
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450 |
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451 | ModifyDataSort(datsortinbag, ninbag, jinbag);
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452 |
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453 | fRanTree->GrowTree(fMatrix,fHadTrue,fClass,datsortinbag,fDataRang,classpopw,mean,square,
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454 | jinbag,winbag,nclass);
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455 |
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456 | //-------------------------------------------------------------------
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457 | // error-estimates from out-of-bag data (oob data):
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458 | //
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459 | // For a single tree the events not(!) contained in the bootstrap sample of
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460 | // this tree can be used to obtain estimates for the classification error of
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461 | // this tree.
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462 | // If you take a certain event, it is contained in the oob-data of 1/3 of
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463 | // the trees (see comment to ModifyData). This means that the classification error
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464 | // determined from oob-data is underestimated, but can still be taken as upper limit.
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465 |
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466 | for (Int_t ievt=0;ievt<numdata;ievt++)
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467 | {
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468 | if (jinbag[ievt]>0)
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469 | continue;
|
---|
470 |
|
---|
471 | fHadEst[ievt] +=fRanTree->TreeHad((*fMatrix), ievt);
|
---|
472 | fNTimesOutBag[ievt]++;
|
---|
473 |
|
---|
474 | }
|
---|
475 |
|
---|
476 | Int_t n=0;
|
---|
477 | Float_t ferr=0;
|
---|
478 |
|
---|
479 | for (Int_t ievt=0;ievt<numdata;ievt++)
|
---|
480 | {
|
---|
481 | if(fNTimesOutBag[ievt]!=0)
|
---|
482 | {
|
---|
483 | float val = fHadEst[ievt]/float(fNTimesOutBag[ievt])-fHadTrue[ievt];
|
---|
484 | if(calcResolution) val/=fHadTrue[ievt];
|
---|
485 |
|
---|
486 | ferr += val*val;
|
---|
487 | n++;
|
---|
488 | }
|
---|
489 | }
|
---|
490 | ferr = TMath::Sqrt(ferr/n);
|
---|
491 |
|
---|
492 | //-------------------------------------------------------------------
|
---|
493 | // give running output
|
---|
494 | *fLog << setw(5) << fTreeNo;
|
---|
495 | *fLog << setw(18) << fRanTree->GetNumEndNodes();
|
---|
496 | *fLog << Form("%18.2f", ferr*100.);
|
---|
497 | *fLog << endl;
|
---|
498 |
|
---|
499 | fRanTree->SetError(ferr);
|
---|
500 |
|
---|
501 | // adding tree to forest
|
---|
502 | AddTree();
|
---|
503 |
|
---|
504 | return fTreeNo<fNumTrees;
|
---|
505 | }
|
---|
506 |
|
---|
507 | Bool_t MRanForest::CreateDataSort()
|
---|
508 | {
|
---|
509 | // fDataSort(m,n) is the event number in which fMatrix(m,n) occurs.
|
---|
510 | // fDataRang(m,n) is the rang of fMatrix(m,n), i.e. if rang = r:
|
---|
511 | // fMatrix(m,n) is the r-th highest value of all fMatrix(m,.).
|
---|
512 | //
|
---|
513 | // There may be more then 1 event with rang r (due to bagging).
|
---|
514 |
|
---|
515 | const Int_t numdata = GetNumData();
|
---|
516 | const Int_t dim = GetNumDim();
|
---|
517 |
|
---|
518 | TArrayF v(numdata);
|
---|
519 | TArrayI isort(numdata);
|
---|
520 |
|
---|
521 |
|
---|
522 | for (Int_t mvar=0;mvar<dim;mvar++)
|
---|
523 | {
|
---|
524 |
|
---|
525 | for(Int_t n=0;n<numdata;n++)
|
---|
526 | {
|
---|
527 | v[n]=(*fMatrix)(n,mvar);
|
---|
528 | isort[n]=n;
|
---|
529 |
|
---|
530 | if(TMath::IsNaN(v[n]))
|
---|
531 | {
|
---|
532 | *fLog << err <<"Event no. "<<n<<", matrix column no. "<<mvar;
|
---|
533 | *fLog << err <<" has the value NaN."<<endl;
|
---|
534 | return kFALSE;
|
---|
535 | }
|
---|
536 | }
|
---|
537 |
|
---|
538 | TMath::Sort(numdata,v.GetArray(),isort.GetArray(),kFALSE);
|
---|
539 |
|
---|
540 | // this sorts the v[n] in ascending order. isort[n] is the event number
|
---|
541 | // of that v[n], which is the n-th from the lowest (assume the original
|
---|
542 | // event numbers are 0,1,...).
|
---|
543 |
|
---|
544 | // control sorting
|
---|
545 | for(int n=1;n<numdata;n++)
|
---|
546 | if(v[isort[n-1]]>v[isort[n]])
|
---|
547 | {
|
---|
548 | *fLog << err <<"Event no. "<<n<<", matrix column no. "<<mvar;
|
---|
549 | *fLog << err <<" not at correct sorting position."<<endl;
|
---|
550 | return kFALSE;
|
---|
551 | }
|
---|
552 |
|
---|
553 | for(Int_t n=0;n<numdata-1;n++)
|
---|
554 | {
|
---|
555 | const Int_t n1=isort[n];
|
---|
556 | const Int_t n2=isort[n+1];
|
---|
557 |
|
---|
558 | fDataSort[mvar*numdata+n]=n1;
|
---|
559 | if(n==0) fDataRang[mvar*numdata+n1]=0;
|
---|
560 | if(v[n1]<v[n2])
|
---|
561 | {
|
---|
562 | fDataRang[mvar*numdata+n2]=fDataRang[mvar*numdata+n1]+1;
|
---|
563 | }else{
|
---|
564 | fDataRang[mvar*numdata+n2]=fDataRang[mvar*numdata+n1];
|
---|
565 | }
|
---|
566 | }
|
---|
567 | fDataSort[(mvar+1)*numdata-1]=isort[numdata-1];
|
---|
568 | }
|
---|
569 | return kTRUE;
|
---|
570 | }
|
---|
571 |
|
---|
572 | void MRanForest::ModifyDataSort(TArrayI &datsortinbag, Int_t ninbag, const TArrayI &jinbag)
|
---|
573 | {
|
---|
574 | const Int_t numdim=GetNumDim();
|
---|
575 | const Int_t numdata=GetNumData();
|
---|
576 |
|
---|
577 | ninbag=0;
|
---|
578 | for (Int_t n=0;n<numdata;n++)
|
---|
579 | if(jinbag[n]==1) ninbag++;
|
---|
580 |
|
---|
581 | for(Int_t m=0;m<numdim;m++)
|
---|
582 | {
|
---|
583 | Int_t k=0;
|
---|
584 | Int_t nt=0;
|
---|
585 | for(Int_t n=0;n<numdata;n++)
|
---|
586 | {
|
---|
587 | if(jinbag[datsortinbag[m*numdata+k]]==1)
|
---|
588 | {
|
---|
589 | datsortinbag[m*numdata+nt]=datsortinbag[m*numdata+k];
|
---|
590 | k++;
|
---|
591 | }else{
|
---|
592 | for(Int_t j=1;j<numdata-k;j++)
|
---|
593 | {
|
---|
594 | if(jinbag[datsortinbag[m*numdata+k+j]]==1)
|
---|
595 | {
|
---|
596 | datsortinbag[m*numdata+nt]=datsortinbag[m*numdata+k+j];
|
---|
597 | k+=j+1;
|
---|
598 | break;
|
---|
599 | }
|
---|
600 | }
|
---|
601 | }
|
---|
602 | nt++;
|
---|
603 | if(nt>=ninbag) break;
|
---|
604 | }
|
---|
605 | }
|
---|
606 | }
|
---|
607 |
|
---|
608 | Bool_t MRanForest::AsciiWrite(ostream &out) const
|
---|
609 | {
|
---|
610 | Int_t n=0;
|
---|
611 | MRanTree *tree;
|
---|
612 | TIter forest(fForest);
|
---|
613 |
|
---|
614 | while ((tree=(MRanTree*)forest.Next()))
|
---|
615 | {
|
---|
616 | tree->AsciiWrite(out);
|
---|
617 | n++;
|
---|
618 | }
|
---|
619 |
|
---|
620 | return n==fNumTrees;
|
---|
621 | }
|
---|