[127] | 1 | package agents.anac.y2010.AgentFSEGA;
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| 2 |
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| 3 | import java.util.ArrayList;
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| 4 | import java.util.Collections;
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| 5 | import java.util.List;
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| 6 |
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| 7 | import genius.core.Bid;
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| 8 | import genius.core.issue.Issue;
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| 9 | import genius.core.issue.IssueDiscrete;
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| 10 | import genius.core.issue.IssueReal;
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| 11 | import genius.core.utility.AdditiveUtilitySpace;
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| 12 | import genius.core.utility.EVALFUNCTYPE;
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| 13 | import genius.core.utility.EvaluatorDiscrete;
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| 14 | import genius.core.utility.EvaluatorReal;
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| 15 |
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| 16 | public class MyBayesianOpponentModel extends OpponentModel {
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| 17 | private AdditiveUtilitySpace uUS;
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| 18 | private ArrayList<UtilitySpaceHypothesis> uUSHypothesis;
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| 19 | private double previousBidUtility;
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| 20 | private double SIGMA = 0.25;
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| 21 | private double CONCESSION_STRATEGY = 0.035; // estimated opponent concession
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| 22 | // strategy
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| 23 |
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| 24 | // scalable
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| 25 | private boolean bUseMostProb = true;
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| 26 | private ArrayList<UtilitySpaceHypothesis> mostProbHyps;
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| 27 |
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| 28 | public MyBayesianOpponentModel(AdditiveUtilitySpace pUS) {
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| 29 | if (pUS == null)
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| 30 | throw new NullPointerException(
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| 31 | "MyBayesianOpponentModel: utility space = null");
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| 32 | uUS = pUS;
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| 33 |
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| 34 | previousBidUtility = 1;
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| 35 | dDomain = pUS.getDomain();
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| 36 | // aBiddingHistory = new ArrayList<Bid>();
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| 37 |
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| 38 | List<Issue> issues = dDomain.getIssues();
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| 39 | ArrayList<ArrayList<EvaluatorHypothesis>> aaEvaluatorHypothesis = new ArrayList<ArrayList<EvaluatorHypothesis>>();
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| 40 |
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| 41 | int numberOfIssues = issues.size();
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| 42 |
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| 43 | // generate weight hypothesis ==> <count of issues>! hypothesis
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| 44 | WeightHypothesis[] weightHypothesis = new WeightHypothesis[factorial(numberOfIssues)];
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| 45 |
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| 46 | // createFrom all permutations
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| 47 | double[] P = new double[numberOfIssues];
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| 48 |
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| 49 | // normalize weights
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| 50 | for (int i = 0; i < numberOfIssues; i++)
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| 51 | P[i] = 2.0 * (i + 1)
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| 52 | / (double) (numberOfIssues * (numberOfIssues + 1));
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| 53 | weightPermutations(0, weightHypothesis, P, numberOfIssues - 1);
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| 54 |
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| 55 | // add initial probabilities
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| 56 | for (int i = 0; i < weightHypothesis.length; i++)
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| 57 | weightHypothesis[i].setProbability(1.0 / weightHypothesis.length);
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| 58 |
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| 59 | // generate evaluator hypotheses
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| 60 | for (int i = 0; i < numberOfIssues; i++) {
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| 61 | ArrayList<EvaluatorHypothesis> lEvalHyps;
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| 62 | switch (uUS.getEvaluator(issues.get(i).getNumber()).getType()) {
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| 63 | case DISCRETE:
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| 64 | lEvalHyps = new ArrayList<EvaluatorHypothesis>();
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| 65 | aaEvaluatorHypothesis.add(lEvalHyps);
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| 66 | IssueDiscrete lDiscIssue = (IssueDiscrete) (dDomain.getIssues()
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| 67 | .get(i));
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| 68 |
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| 69 | // uphill
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| 70 | EvaluatorDiscrete lDiscreteEvaluator = new EvaluatorDiscrete();
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| 71 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 72 | lDiscreteEvaluator.addEvaluation(lDiscIssue.getValue(j),
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| 73 | 1000 * j + 1);
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| 74 | EvaluatorHypothesis lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 75 | lDiscreteEvaluator, "uphill");
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| 76 |
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| 77 | lEvalHyps.add(lEvaluatorHypothesis);
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| 78 |
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| 79 | // downhill
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| 80 | lDiscreteEvaluator = new EvaluatorDiscrete();
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| 81 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 82 | lDiscreteEvaluator
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| 83 | .addEvaluation(
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| 84 | lDiscIssue.getValue(j),
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| 85 | 1000 * (lDiscIssue.getNumberOfValues() - j - 1) + 1);
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| 86 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 87 | lDiscreteEvaluator, "downhill");
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| 88 |
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| 89 | lEvalHyps.add(lEvaluatorHypothesis);
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| 90 |
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| 91 | // triangular
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| 92 | lDiscreteEvaluator = new EvaluatorDiscrete();
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| 93 | int halfway = lDiscIssue.getNumberOfValues() / 2;
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| 94 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 95 | if (j < halfway)
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| 96 | lDiscreteEvaluator.addEvaluation(
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| 97 | lDiscIssue.getValue(j), 1000 * j + 1);
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| 98 | else
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| 99 | lDiscreteEvaluator
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| 100 | .addEvaluation(lDiscIssue.getValue(j),
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| 101 | 1000 * (lDiscIssue.getNumberOfValues()
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| 102 | - j - 1) + 1);
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| 103 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 104 | lDiscreteEvaluator, "triangular");
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| 105 |
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| 106 | lEvalHyps.add(lEvaluatorHypothesis);
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| 107 | break;
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| 108 |
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| 109 | // Eval hypothesis for real attributes
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| 110 | case REAL:
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| 111 | lEvalHyps = new ArrayList<EvaluatorHypothesis>();
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| 112 | aaEvaluatorHypothesis.add(lEvalHyps);
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| 113 | IssueReal lRealIssue = (IssueReal) (dDomain.getIssues().get(i)); // Laptop
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| 114 | // |
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| 115 | // Harddisk
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| 116 | // |
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| 117 | // Monitor
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| 118 |
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| 119 | // uphill
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| 120 | EvaluatorReal lRealEvaluator = new EvaluatorReal();
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| 121 | lRealEvaluator.setLowerBound(lRealIssue.getLowerBound());
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| 122 | lRealEvaluator.setUpperBound(lRealIssue.getUpperBound());
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| 123 | lRealEvaluator.setType(EVALFUNCTYPE.LINEAR);
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| 124 | lRealEvaluator.addParam(1, 1.0 / (lRealEvaluator
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| 125 | .getUpperBound() - lRealEvaluator.getLowerBound()));
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| 126 | lRealEvaluator
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| 127 | .addParam(
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| 128 | 0,
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| 129 | -lRealEvaluator.getLowerBound()
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| 130 | / (lRealEvaluator.getUpperBound() - lRealEvaluator
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| 131 | .getLowerBound()));
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| 132 | lEvaluatorHypothesis = new EvaluatorHypothesis(lRealEvaluator,
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| 133 | "uphill");
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| 134 | lEvalHyps.add(lEvaluatorHypothesis);
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| 135 |
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| 136 | // downhill
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| 137 | lRealEvaluator = new EvaluatorReal();
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| 138 | lRealEvaluator.setLowerBound(lRealIssue.getLowerBound());
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| 139 | lRealEvaluator.setUpperBound(lRealIssue.getUpperBound());
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| 140 | lRealEvaluator.setType(EVALFUNCTYPE.LINEAR);
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| 141 | lRealEvaluator
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| 142 | .addParam(
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| 143 | 1,
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| 144 | -1.0
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| 145 | / (lRealEvaluator.getUpperBound() - lRealEvaluator
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| 146 | .getLowerBound()));
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| 147 | lRealEvaluator
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| 148 | .addParam(
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| 149 | 0,
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| 150 | 1.0
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| 151 | + lRealEvaluator.getLowerBound()
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| 152 | / (lRealEvaluator.getUpperBound() - lRealEvaluator
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| 153 | .getLowerBound()));
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| 154 | lEvaluatorHypothesis = new EvaluatorHypothesis(lRealEvaluator,
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| 155 | "downhill");
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| 156 | lEvalHyps.add(lEvaluatorHypothesis);
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| 157 |
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| 158 | // triangular
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| 159 | int lTotalTriangularFns = 1;
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| 160 | for (int k = 1; k <= lTotalTriangularFns; k++) {
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| 161 | lRealEvaluator = new EvaluatorReal();
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| 162 | lRealEvaluator.setLowerBound(lRealIssue.getLowerBound());
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| 163 | lRealEvaluator.setUpperBound(lRealIssue.getUpperBound());
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| 164 | lRealEvaluator.setType(EVALFUNCTYPE.TRIANGULAR);
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| 165 | lRealEvaluator.addParam(0, lRealEvaluator.getLowerBound());
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| 166 | lRealEvaluator.addParam(1, lRealEvaluator.getUpperBound());
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| 167 | lRealEvaluator
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| 168 | .addParam(
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| 169 | 2,
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| 170 | lRealEvaluator.getLowerBound()
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| 171 | + (double) k
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| 172 | * (lRealEvaluator.getUpperBound() - lRealEvaluator
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| 173 | .getLowerBound())
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| 174 | / (lTotalTriangularFns + 1));
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| 175 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 176 | lRealEvaluator, "triangular");
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| 177 | lEvaluatorHypothesis.setProbability((double) 1 / 3);
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| 178 | lEvalHyps.add(lEvaluatorHypothesis);
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| 179 | }
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| 180 | for (int k = 0; k < lEvalHyps.size(); k++) {
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| 181 | lEvalHyps.get(k).setProbability(
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| 182 | (double) 1 / lEvalHyps.size());
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| 183 | }
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| 184 |
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| 185 | break;
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| 186 |
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| 187 | default:
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| 188 | throw new NullPointerException(
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| 189 | "Evaluator type not implemented: eval type - "
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| 190 | + uUS.getEvaluator(issues.get(i).getNumber())
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| 191 | .getType());
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| 192 | }
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| 193 | }
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| 194 |
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| 195 | // build evaluation hypothesis
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| 196 | ArrayList<EvaluatorHypothesis[]> evalHypothesis = new ArrayList<EvaluatorHypothesis[]>();
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| 197 | EvaluatorHypothesis[] ehTmp = new EvaluatorHypothesis[uUS
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| 198 | .getNrOfEvaluators()];
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| 199 |
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| 200 | buildEvaluationHypothesis(evalHypothesis, ehTmp,
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| 201 | uUS.getNrOfEvaluators() - 1, aaEvaluatorHypothesis);
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| 202 |
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| 203 | // build user space hypothesis
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| 204 | buildUtilitySpaceHypothesis(weightHypothesis, evalHypothesis);
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| 205 | }
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| 206 |
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| 207 | private void buildEvaluationHypothesis(
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| 208 | ArrayList<EvaluatorHypothesis[]> pHyps,
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| 209 | EvaluatorHypothesis[] pEval, int m,
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| 210 | ArrayList<ArrayList<EvaluatorHypothesis>> paaEval) {
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| 211 | if (m == 0) {
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| 212 | ArrayList<EvaluatorHypothesis> lEvalHyps = paaEval.get(uUS
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| 213 | .getNrOfEvaluators() - 1);
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| 214 | for (int i = 0; i < lEvalHyps.size(); i++) {
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| 215 | pEval[uUS.getNrOfEvaluators() - 1] = lEvalHyps.get(i);
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| 216 | EvaluatorHypothesis[] lTmp = new EvaluatorHypothesis[uUS
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| 217 | .getNrOfEvaluators()];
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| 218 | // copy to temporary array
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| 219 | for (int j = 0; j < lTmp.length; j++)
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| 220 | lTmp[j] = pEval[j];
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| 221 | pHyps.add(lTmp);
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| 222 | }
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| 223 | } else {
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| 224 | ArrayList<EvaluatorHypothesis> lEvalHyps = paaEval.get(uUS
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| 225 | .getNrOfEvaluators() - m - 1);
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| 226 | for (int i = 0; i < lEvalHyps.size(); i++) {
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| 227 | pEval[uUS.getNrOfEvaluators() - m - 1] = lEvalHyps.get(i);
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| 228 | buildEvaluationHypothesis(pHyps, pEval, m - 1, paaEval);
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| 229 | }
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| 230 | }
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| 231 | }
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| 232 |
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| 233 | private void buildUtilitySpaceHypothesis(
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| 234 | WeightHypothesis[] pWeightHypothesis,
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| 235 | ArrayList<EvaluatorHypothesis[]> pEvalHypothesis) {
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| 236 | uUSHypothesis = new ArrayList<UtilitySpaceHypothesis>();
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| 237 | for (int i = 0; i < pWeightHypothesis.length; i++) {
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| 238 | for (int j = 0; j < pEvalHypothesis.size(); j++) {
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| 239 | UtilitySpaceHypothesis lUSHyp = new UtilitySpaceHypothesis(
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| 240 | dDomain, uUS, pWeightHypothesis[i],
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| 241 | pEvalHypothesis.get(j));
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| 242 | uUSHypothesis.add(lUSHyp);
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| 243 | }
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| 244 | }
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| 245 |
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| 246 | // set initial probability for all hyps
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| 247 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 248 | uUSHypothesis.get(i).setProbability(
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| 249 | 1.0 / (double) (uUSHypothesis.size()));
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| 250 | }
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| 251 | }
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| 252 |
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| 253 | private Integer weightPermutations(Integer index, WeightHypothesis[] hyps,
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| 254 | double[] P, int m) {
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| 255 | if (m == 0) {
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| 256 | WeightHypothesis lWH = new WeightHypothesis(dDomain);
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| 257 | for (int i = 0; i < P.length; i++)
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| 258 | lWH.setWeight(i, P[i]);
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| 259 | hyps[index] = lWH;
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| 260 | index++;
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| 261 | } else {
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| 262 | for (int i = 0; i <= m; i++) {
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| 263 | index = weightPermutations(index, hyps, P, m - 1);
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| 264 | if (i < m) {
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| 265 | // swap elements i and m
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| 266 | double tmp = P[i];
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| 267 | P[i] = P[m];
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| 268 | P[m] = tmp;
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| 269 | reverse(P, m - 1);
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| 270 | } // if
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| 271 | }
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| 272 | }
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| 273 | return index;
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| 274 | }
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| 275 |
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| 276 | private void reverse(double[] array, int size) {
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| 277 | int i = 0, j = size;
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| 278 | while (i < j) {
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| 279 | // swap i <-> j
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| 280 | double tmp = array[i];
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| 281 | array[i] = array[j];
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| 282 | array[j] = tmp;
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| 283 | i++;
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| 284 | j--;
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| 285 | }
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| 286 | }
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| 287 |
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| 288 | private int factorial(int n) {
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| 289 | int result = 1;
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| 290 | for (; n > 1; n--) {
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| 291 | result *= n;
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| 292 | }
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| 293 | return result;
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| 294 | }
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| 295 |
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| 296 | public void updateBeliefs(Bid pBid) throws Exception {
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| 297 | // calculate probability for the given bid
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| 298 | double lProbSum = 0;
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| 299 | double lMaxProb = 0;
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| 300 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 301 | UtilitySpaceHypothesis hyp = uUSHypothesis.get(i);
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| 302 | double condDistrib = hyp.getProbability()
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| 303 | * conditionalDistribution(
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| 304 | uUSHypothesis.get(i).getUtility(pBid),
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| 305 | previousBidUtility);
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| 306 | lProbSum += condDistrib;
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| 307 | if (condDistrib > lMaxProb)
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| 308 | lMaxProb = condDistrib;
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| 309 | hyp.setProbability(condDistrib);
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| 310 | }
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| 311 |
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| 312 | if (bUseMostProb)
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| 313 | mostProbHyps = new ArrayList<UtilitySpaceHypothesis>();
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| 314 |
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| 315 | double mostProbHypSum = 0;
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| 316 |
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| 317 | // receiveMessage the weights hyps and evaluators hyps
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| 318 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 319 | UtilitySpaceHypothesis hyp = uUSHypothesis.get(i);
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| 320 | double normalizedProbability = hyp.getProbability() / lProbSum;
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| 321 |
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| 322 | if (bUseMostProb)
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| 323 | if (normalizedProbability > lMaxProb * 0.95 / lProbSum) {
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| 324 | mostProbHyps.add(hyp);
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| 325 | mostProbHypSum += normalizedProbability;
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| 326 | }
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| 327 |
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| 328 | // exclude if probability is 0
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| 329 | if (normalizedProbability > 0)
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| 330 | hyp.setProbability(normalizedProbability);
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| 331 | else {
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| 332 | uUSHypothesis.remove(i);
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| 333 | }
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| 334 | // --- end exclude hyps with prob. around 0
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| 335 | }
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| 336 |
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| 337 | // normalize most probable hypothesis
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| 338 | if (bUseMostProb) {
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| 339 | for (int i = 0; i < mostProbHyps.size(); i++) {
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| 340 | UtilitySpaceHypothesis tmpHyp = mostProbHyps.get(i);
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| 341 | double normalizedProbability = tmpHyp.getProbability()
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| 342 | / mostProbHypSum;
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| 343 | tmpHyp.setProbability(normalizedProbability);
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| 344 | }
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| 345 | }
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| 346 |
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| 347 | // calculate utility of the next partner's bid according to the
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| 348 | // concession functions
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| 349 | previousBidUtility = previousBidUtility - CONCESSION_STRATEGY;
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| 350 |
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| 351 | // sort hypotesis by probability
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| 352 | Collections.sort(uUSHypothesis);
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| 353 |
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| 354 | // exclude bids with sum under 0.95
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| 355 | int cutPoint = Integer.MAX_VALUE;
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| 356 |
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| 357 | double cummulativeSum = 0;
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| 358 |
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| 359 | // get cutPoint
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| 360 | // and cumulative sum for normalization
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| 361 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 362 | cummulativeSum += uUSHypothesis.get(i).getProbability();
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| 363 | if (cummulativeSum > 0.95) {
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| 364 | cutPoint = i;
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| 365 | break;
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| 366 | }
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| 367 | }
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| 368 | // eliminate from cutPoint to last item
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| 369 | if (cutPoint != Integer.MAX_VALUE) {
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| 370 | for (int i = uUSHypothesis.size() - 1; i >= cutPoint; i--) {
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| 371 | uUSHypothesis.remove(i);
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| 372 | }
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| 373 | }
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| 374 |
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| 375 | // normalize remained hypothesis probability
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| 376 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 377 | UtilitySpaceHypothesis currentHyp = uUSHypothesis.get(i);
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| 378 | double newProbability = currentHyp.getProbability()
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| 379 | / cummulativeSum;
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| 380 | currentHyp.setProbability(newProbability);
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| 381 | }
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| 382 | }
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| 383 |
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| 384 | private double conditionalDistribution(double pUtility,
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| 385 | double pPreviousBidUtility) {
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| 386 | if (pPreviousBidUtility < pUtility)
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| 387 | return 0;
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| 388 | else {
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| 389 | double x = (pPreviousBidUtility - pUtility) / pPreviousBidUtility;
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| 390 | double distribution = (1 / (SIGMA * Math.sqrt(2 * Math.PI)) * Math
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| 391 | .exp(-(x * x) / (2 * SIGMA * SIGMA)));
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| 392 | return distribution;
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| 393 | }
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| 394 | }
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| 395 |
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| 396 | public double getExpectedUtility(Bid pBid) throws Exception {
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| 397 | double lExpectedUtility = 0;
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| 398 |
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| 399 | if (bUseMostProb && (mostProbHyps != null)) {
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| 400 |
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| 401 | for (int i = 0; i < mostProbHyps.size(); i++) {
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| 402 | UtilitySpaceHypothesis tmpUSHypothesis = mostProbHyps.get(i);
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| 403 | double p = tmpUSHypothesis.getProbability();
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| 404 | double u = tmpUSHypothesis.getUtility(pBid);
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| 405 | lExpectedUtility += p * u;
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| 406 | }
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| 407 | } else {
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| 408 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 409 | UtilitySpaceHypothesis tmpUSHypothesis = uUSHypothesis.get(i);
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| 410 | double p = tmpUSHypothesis.getProbability();
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| 411 | double u = tmpUSHypothesis.getUtility(pBid);
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| 412 | lExpectedUtility += p * u;
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| 413 | }
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| 414 | }
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| 415 |
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| 416 | return lExpectedUtility;
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| 417 | }
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| 418 |
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| 419 | public double getExpectedWeight(int pIssueNumber) {
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| 420 | double lExpectedWeight = 0;
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| 421 | for (int i = 0; i < uUSHypothesis.size(); i++) {
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| 422 | UtilitySpaceHypothesis lUSHyp = uUSHypothesis.get(i);
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| 423 | double p = lUSHyp.getProbability();
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| 424 | double u = lUSHyp.getHeightHyp().getWeight(pIssueNumber);
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| 425 | lExpectedWeight += p * u;
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| 426 | }
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| 427 | return lExpectedWeight;
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| 428 | }
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| 429 | }
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