1 | package agents.anac.y2019.agentlarry;
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2 |
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3 | import genius.core.AgentID;
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4 | import genius.core.Bid;
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5 | import genius.core.BidIterator;
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6 | import genius.core.actions.Accept;
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7 | import genius.core.actions.Action;
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8 | import genius.core.actions.EndNegotiation;
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9 | import genius.core.actions.Offer;
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10 | import genius.core.list.Tuple;
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11 | import genius.core.parties.AbstractNegotiationParty;
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12 | import genius.core.parties.NegotiationInfo;
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13 | import genius.core.persistent.PersistentDataType;
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14 | import genius.core.persistent.StandardInfo;
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15 | import genius.core.persistent.StandardInfoList;
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16 | import genius.core.uncertainty.AdditiveUtilitySpaceFactory;
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17 | import genius.core.utility.AbstractUtilitySpace;
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18 |
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19 | import java.util.ArrayList;
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20 | import java.util.HashMap;
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21 | import java.util.List;
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22 | import java.util.Map;
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23 |
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24 | public class AgentLarry extends AbstractNegotiationParty {
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25 | private final Map<AgentID, BidHistory> agentsBidHistories = new HashMap<>();
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26 | private Bid lastOfferedBid = null;
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27 | private BidHistory initialHistory = new BidHistory();
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28 | private final VectorConverter vectorConverter = new VectorConverter();
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29 |
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30 | private class BidHistory extends ArrayList<Tuple<Bid, Boolean>> {}
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31 |
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32 | @Override
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33 | public void init(NegotiationInfo info) {
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34 | super.init(info);
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35 | if (this.getData().getPersistentDataType() == PersistentDataType.STANDARD) {
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36 | StandardInfoList infoList = (StandardInfoList) info.getPersistentData().get();
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37 | for (StandardInfo sessionInfo : infoList) {
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38 | Bid initialBid = sessionInfo.getAgreement().get1();
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39 | if (initialBid != null) {
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40 | System.out.println(String.format("initial bid: %s", initialBid.toString()));
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41 | initialHistory.add(new Tuple<>(initialBid, true));
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42 | }
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43 | }
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44 | }
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45 | }
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46 |
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47 | /**
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48 | * Receive message and save it in the bid history
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49 | * If it is accept save the bid the agent accept with true
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50 | * if it is offer save the last bid with false since the agent did not accept and
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51 | * save the bid he made in his offer with true assuming because he offered it he would accept it
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52 | *
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53 | * @param sender The id of the agent who sent the message
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54 | * @param act the action that was sent
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55 | */
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56 | @Override
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57 | public void receiveMessage(AgentID sender, Action act) {
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58 | super.receiveMessage(sender, act);
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59 | if (act instanceof Offer || act instanceof Accept) {
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60 | if (!agentsBidHistories.containsKey(sender)) {
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61 | agentsBidHistories.put(sender, (BidHistory) initialHistory.clone());
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62 | }
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63 | }
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64 |
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65 | if (act instanceof Offer) {
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66 | Bid bid = ((Offer) act).getBid();
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67 | this.agentsBidHistories.get(sender).add(new Tuple<>(bid, true));
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68 | if (this.lastOfferedBid != null) {
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69 | this.agentsBidHistories.get(sender).add(new Tuple<>(this.lastOfferedBid, false));
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70 | }
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71 | this.lastOfferedBid = bid;
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72 | } else if (act instanceof Accept) {
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73 | Bid bid = ((Accept) act).getBid();
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74 | this.agentsBidHistories.get(sender).add(new Tuple<>(bid, true));
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75 | }
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76 | }
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77 |
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78 | /**
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79 | * Choose if to accept the last offer or make a new offer
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80 | *
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81 | * First we initialize a logistic regression model for each agent (except us) with his bid history
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82 | * and whether he accepted each bid or rejected it
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83 | * We train a new logistic regression each time and not use the same one for the whole session
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84 | * because retrain it each time gives a better results
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85 | * (probably because of the random weight before you start train it)
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86 | *
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87 | * Then for each bid we value the chances each agent will accept it using the logistic regression models
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88 | * and evaluate the bid by multiple them all together with the utility of the bid and choose the bid
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89 | * with the highest evaluation
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90 | *
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91 | * Then if the last offered bid has an higher utility we accept it, otherwise we offer the bid we chose
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92 | *
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93 | * @param list The available actions to do
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94 | * @return The chosen action
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95 | */
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96 | @Override
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97 | public Action chooseAction(List<Class<? extends Action>> list) {
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98 | try {
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99 | System.out.println(getPartyId());
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100 | System.out.println("choosing action");
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101 |
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102 | System.out.println("initializing model");
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103 | List<LogisticRegression> logisticRegressionsModels = this.initializeModels();
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104 | LogisticRegression larryModel = this.initializeLarryModel();
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105 |
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106 | System.out.println("searching for best bid");
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107 | Bid nextBid = this.findNextBid(logisticRegressionsModels, larryModel);
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108 |
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109 | System.out.println("choosing of accepting or offering");
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110 |
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111 | if (list.contains(Accept.class) && shouldAccept(nextBid)) {
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112 | System.out.println("Accepting");
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113 | return new Accept(getPartyId(), lastOfferedBid);
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114 | } else {
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115 | System.out.println("offering");
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116 | this.lastOfferedBid = nextBid;
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117 | return new Offer(this.getPartyId(), nextBid);
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118 | }
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119 |
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120 | } catch (Exception e) {
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121 | e.printStackTrace();
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122 | return new EndNegotiation(getPartyId());
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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 | * @param nextBid The next bid to offer
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128 | * @return Whether to accept the last bid or offer the nextBid
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129 | */
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130 | private boolean shouldAccept(Bid nextBid) {
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131 | if (lastOfferedBid != null) {
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132 | if (userModel.getBidRanking().getBidOrder().indexOf(lastOfferedBid) >=
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133 | userModel.getBidRanking().getBidOrder().indexOf(nextBid) * this.utilitySpace.getDiscountFactor()) {
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134 | return true;
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135 | }
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136 | }
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137 | return false;
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138 | }
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139 |
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140 | /**
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141 | * @param logisticRegressionsModels The models of the agents
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142 | * @return The next bid to offer
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143 | */
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144 | private Bid findNextBid(List<LogisticRegression> logisticRegressionsModels, LogisticRegression larryModel) {
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145 | double bestBidEvaluation = 0;
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146 | Bid nextBid = null;
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147 |
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148 | BidIterator bidIterator = new BidIterator(this.utilitySpace.getDomain());
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149 |
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150 | while (bidIterator.hasNext()) {
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151 | Bid bid = bidIterator.next();
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152 | Vector vector = this.vectorConverter.convert(bid);
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153 | double chancesForAcceptance = 1;
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154 | for (LogisticRegression model : logisticRegressionsModels) {
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155 | chancesForAcceptance *= model.classify(vector);
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156 | }
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157 | double bidUtility = larryModel.classify(vector);
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158 | double bidEvaluation = bidUtility + chancesForAcceptance;
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159 |
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160 | if (bidEvaluation >= bestBidEvaluation) {
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161 | nextBid = bid;
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162 | bestBidEvaluation = bidEvaluation;
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163 | }
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164 | }
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165 | System.out.println(String.format("next bid evaluation %f", bestBidEvaluation));
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166 | return nextBid;
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167 | }
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168 |
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169 | /**
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170 | * Initialize the models of the agents
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171 | * each model gets a bid and returns the chances the agent will accept it
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172 | *
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173 | * @return The logistic models of the agents
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174 | */
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175 | private List<LogisticRegression> initializeModels() {
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176 | List<LogisticRegression> logisticRegressionsModels = new ArrayList<>();
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177 | for (BidHistory bidHistory : this.agentsBidHistories.values()) {
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178 | LogisticRegression logisticRegression = new LogisticRegression(
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179 | this.vectorConverter.getVectorSize(this.utilitySpace.getDomain()));
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180 | for (Tuple<Bid, Boolean> bidToDidAccept : bidHistory) {
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181 | Vector vector = this.vectorConverter.convert(bidToDidAccept.get1());
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182 | double label = bidToDidAccept.get2() ? 1 : 0;
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183 | logisticRegression.train(vector, label);
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184 | }
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185 | logisticRegressionsModels.add(logisticRegression);
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186 | }
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187 | return logisticRegressionsModels;
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188 | }
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189 |
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190 | private LogisticRegression initializeLarryModel() {
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191 | LogisticRegression model = new LogisticRegression(this.vectorConverter.getVectorSize(this.utilitySpace.getDomain()));
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192 | List<Bid> bids = userModel.getBidRanking().getBidOrder();
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193 | for (int j = 0; j < 5; j++) {
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194 | for (int i = 0; i < bids.size(); i++) {
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195 | Vector vector = this.vectorConverter.convert(bids.get(i));
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196 | double label = (userModel.getBidRanking().getHighUtility() - userModel.getBidRanking().getLowUtility()) * i / bids.size();
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197 | model.train(vector, label);
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198 | }
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199 | }
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200 | return model;
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201 | }
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202 |
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203 | @Override
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204 | public AbstractUtilitySpace estimateUtilitySpace() {
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205 | return new AdditiveUtilitySpaceFactory(getDomain()).getUtilitySpace();
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206 | }
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207 |
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208 | @Override
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209 | public String getDescription() {
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210 | return "ANAC2019 AgentLarry";
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211 | }
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212 | }
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