| 1 | package agents.bayesianopponentmodel;
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| 2 |
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| 3 | import java.util.ArrayList;
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| 4 | import java.util.List;
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| 5 |
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| 6 | import genius.core.Bid;
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| 7 | import genius.core.issue.Issue;
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| 8 | import genius.core.issue.IssueDiscrete;
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| 9 | import genius.core.issue.IssueReal;
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| 10 | import genius.core.utility.AdditiveUtilitySpace;
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| 11 | import genius.core.utility.EVALFUNCTYPE;
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| 12 | import genius.core.utility.EvaluatorDiscrete;
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| 13 | import genius.core.utility.EvaluatorReal;
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| 14 |
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| 15 | /**
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| 16 | * Implementation of the unscalable Bayesian Model. Only working with
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| 17 | * {@link AdditiveUtilitySpace}
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| 18 | *
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| 19 | * Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian
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| 20 | * Learning by K. Hindriks, D. Tykhonov
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| 21 | */
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| 22 | public class BayesianOpponentModel extends OpponentModel {
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| 23 |
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| 24 | private AdditiveUtilitySpace fUS;
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| 25 | private WeightHypothesis[] fWeightHyps;
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| 26 | private ArrayList<ArrayList<EvaluatorHypothesis>> fEvaluatorHyps;
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| 27 | private ArrayList<EvaluatorHypothesis[]> fEvalHyps;
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| 28 | private ArrayList<UtilitySpaceHypothesis> fUSHyps;
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| 29 | private boolean fUseMostProbableHypsOnly = false;
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| 30 | private ArrayList<UtilitySpaceHypothesis> fMostProbableUSHyps;
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| 31 | private double fPreviousBidUtility;
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| 32 | private double EXPECTED_CONCESSION_STEP = 0.035;
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| 33 | private double SIGMA = 0.25;
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| 34 | private boolean USE_DOMAIN_KNOWLEDGE = false;
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| 35 | List<Issue> issues;
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| 36 |
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| 37 | public BayesianOpponentModel(AdditiveUtilitySpace pUtilitySpace) {
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| 38 | if (pUtilitySpace == null)
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| 39 | throw new NullPointerException("pUtilitySpace=null");
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| 40 | fDomain = pUtilitySpace.getDomain();
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| 41 | fPreviousBidUtility = 1;
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| 42 | fUS = pUtilitySpace;
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| 43 | fBiddingHistory = new ArrayList<Bid>();
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| 44 | issues = fDomain.getIssues();
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| 45 | int lNumberOfHyps = factorial(issues.size());
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| 46 | fWeightHyps = new WeightHypothesis[lNumberOfHyps/* +1 */];
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| 47 | // generate all possible ordering combinations of the weights
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| 48 | int index = 0;
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| 49 | double[] P = new double[issues.size()];
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| 50 | // take care of weights normalization
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| 51 | for (int i = 0; i < issues.size(); i++)
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| 52 | P[i] = (i + 1)
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| 53 | / ((double) ((issues.size() * (fDomain.getIssues().size() + 1)) / 2.0));
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| 54 | // build all possible orderings of the weights from P
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| 55 | antilex(new Integer(index), fWeightHyps, P,
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| 56 | fDomain.getIssues().size() - 1);
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| 57 | // add the all equal hyp
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| 58 | /*
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| 59 | * WeightHypothesis allEqual = new WeightHypothesis(fDomain); for(int
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| 60 | * i=0;i< issues.size();i++) allEqual.setWeight(i,
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| 61 | * 1./((double)(issues.size()))); //set uniform probability distribution
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| 62 | * to the weights hyps fWeightHyps[fWeightHyps.length-1] = allEqual;
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| 63 | */
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| 64 | for (int i = 0; i < fWeightHyps.length; i++)
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| 65 | fWeightHyps[i].setProbability(1. / fWeightHyps.length);
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| 66 | // generate all possible hyps of evaluation functions (arraylist with
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| 67 | // length issues with an arraylist of length values for each issue)
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| 68 | fEvaluatorHyps = new ArrayList<ArrayList<EvaluatorHypothesis>>();
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| 69 | int lTotalTriangularFns = 1;
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| 70 | for (int i = 0; i < fUS.getNrOfEvaluators(); i++) {
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| 71 | ArrayList<EvaluatorHypothesis> lEvalHyps = new ArrayList<EvaluatorHypothesis>();
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| 72 | lEvalHyps = new ArrayList<EvaluatorHypothesis>();
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| 73 | fEvaluatorHyps.add(lEvalHyps);
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| 74 | switch (fUS.getEvaluator(issues.get(i).getNumber()).getType()) {
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| 75 |
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| 76 | case REAL:
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| 77 | // EvaluatorReal lEval = (EvaluatorReal)(fUS.getEvaluator(i));
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| 78 | IssueReal lIssue = (IssueReal) (fDomain.getIssues().get(i));
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| 79 | EvaluatorReal lHypEval = new EvaluatorReal();
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| 80 | EvaluatorHypothesis lEvaluatorHypothesis;
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| 81 | if (USE_DOMAIN_KNOWLEDGE) {
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| 82 | // uphill
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| 83 | lHypEval = new EvaluatorReal();
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| 84 | lHypEval.setUpperBound(lIssue.getUpperBound());
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| 85 | lHypEval.setLowerBound(lIssue.getLowerBound());
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| 86 | lHypEval.setType(EVALFUNCTYPE.LINEAR);
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| 87 | lHypEval.addParam(1,
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| 88 | 1. / (lHypEval.getUpperBound() - lHypEval
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| 89 | .getLowerBound()));
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| 90 | lHypEval.addParam(
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| 91 | 0,
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| 92 | -lHypEval.getLowerBound()
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| 93 | / (lHypEval.getUpperBound() - lHypEval
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| 94 | .getLowerBound()));
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| 95 | lEvaluatorHypothesis = new EvaluatorHypothesis(lHypEval);
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| 96 | lEvaluatorHypothesis.setDesc("uphill");
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| 97 | lEvalHyps.add(lEvaluatorHypothesis);
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| 98 |
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| 99 | } else {
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| 100 | // uphill
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| 101 | lHypEval = new EvaluatorReal();
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| 102 | lHypEval.setUpperBound(lIssue.getUpperBound());
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| 103 | lHypEval.setLowerBound(lIssue.getLowerBound());
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| 104 | lHypEval.setType(EVALFUNCTYPE.LINEAR);
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| 105 | lHypEval.addParam(1,
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| 106 | 1. / (lHypEval.getUpperBound() - lHypEval
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| 107 | .getLowerBound()));
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| 108 | lHypEval.addParam(
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| 109 | 0,
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| 110 | -lHypEval.getLowerBound()
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| 111 | / (lHypEval.getUpperBound() - lHypEval
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| 112 | .getLowerBound()));
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| 113 | lEvaluatorHypothesis = new EvaluatorHypothesis(lHypEval);
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| 114 | lEvaluatorHypothesis.setDesc("uphill");
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| 115 | lEvalHyps.add(lEvaluatorHypothesis);
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| 116 | // downhill
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| 117 | lHypEval = new EvaluatorReal();
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| 118 | lHypEval.setUpperBound(lIssue.getUpperBound());
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| 119 | lHypEval.setLowerBound(lIssue.getLowerBound());
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| 120 | lHypEval.setType(EVALFUNCTYPE.LINEAR);
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| 121 | lHypEval.addParam(
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| 122 | 1,
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| 123 | -1.0
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| 124 | / (lHypEval.getUpperBound() - lHypEval
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| 125 | .getLowerBound()));
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| 126 | lHypEval.addParam(
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| 127 | 0,
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| 128 | 1.0
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| 129 | + lHypEval.getLowerBound()
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| 130 | / (lHypEval.getUpperBound() - lHypEval
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| 131 | .getLowerBound()));
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| 132 | lEvaluatorHypothesis = new EvaluatorHypothesis(lHypEval);
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| 133 | lEvalHyps.add(lEvaluatorHypothesis);
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| 134 | lEvaluatorHypothesis.setDesc("downhill");
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| 135 | // triangular
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| 136 | for (int k = 1; k <= lTotalTriangularFns; k++) {
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| 137 | // triangular
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| 138 | lHypEval = new EvaluatorReal();
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| 139 | lHypEval.setUpperBound(lIssue.getUpperBound());
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| 140 | lHypEval.setLowerBound(lIssue.getLowerBound());
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| 141 | lHypEval.setType(EVALFUNCTYPE.TRIANGULAR);
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| 142 | lHypEval.addParam(0, lHypEval.getLowerBound());
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| 143 | lHypEval.addParam(1, lHypEval.getUpperBound());
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| 144 | lHypEval.addParam(
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| 145 | 2,
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| 146 | lHypEval.getLowerBound()
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| 147 | + (double) k
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| 148 | * (lHypEval.getUpperBound() - lHypEval
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| 149 | .getLowerBound())
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| 150 | / (lTotalTriangularFns + 1));
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| 151 | lEvaluatorHypothesis = new EvaluatorHypothesis(lHypEval);
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| 152 | lEvaluatorHypothesis.setProbability((double) 1 / 3);
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| 153 | lEvalHyps.add(lEvaluatorHypothesis);
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| 154 | lEvaluatorHypothesis.setDesc("triangular");
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| 155 | }
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| 156 | }
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| 157 | for (int k = 0; k < lEvalHyps.size(); k++) {
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| 158 | lEvalHyps.get(k).setProbability(
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| 159 | (double) 1 / lEvalHyps.size());
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| 160 | }
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| 161 |
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| 162 | break;
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| 163 | // for each issue three possible hypothesis are generated
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| 164 | case DISCRETE:
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| 165 | lEvalHyps = new ArrayList<EvaluatorHypothesis>();
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| 166 | fEvaluatorHyps.add(lEvalHyps);
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| 167 | // EvaluatorReal lEval = (EvaluatorReal)(fUS.getEvaluator(i));
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| 168 | IssueDiscrete lDiscIssue = (IssueDiscrete) (fDomain.getIssues()
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| 169 | .get(i));
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| 170 | if (USE_DOMAIN_KNOWLEDGE) {
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| 171 | // uphill
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| 172 | EvaluatorDiscrete lDiscreteEval = new EvaluatorDiscrete();
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| 173 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 174 | lDiscreteEval.addEvaluation(lDiscIssue.getValue(j),
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| 175 | 1000 * j);
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| 176 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 177 | lDiscreteEval);
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| 178 | lEvaluatorHypothesis.setDesc("uphill");
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| 179 | lEvalHyps.add(lEvaluatorHypothesis);
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| 180 |
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| 181 | } else {
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| 182 | // uphill (from 1 to 1000 * valueCount + 1)
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| 183 | EvaluatorDiscrete lDiscreteEval = new EvaluatorDiscrete();
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| 184 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 185 | lDiscreteEval.addEvaluation(lDiscIssue.getValue(j),
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| 186 | 1000 * j + 1);
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| 187 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 188 | lDiscreteEval);
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| 189 | lEvaluatorHypothesis.setDesc("uphill");
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| 190 | lEvalHyps.add(lEvaluatorHypothesis);
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| 191 | // downhill
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| 192 | lDiscreteEval = new EvaluatorDiscrete();
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| 193 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 194 | lDiscreteEval
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| 195 | .addEvaluation(lDiscIssue.getValue(j),
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| 196 | 1000 * (lDiscIssue.getNumberOfValues()
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| 197 | - j - 1) + 1);
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| 198 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 199 | lDiscreteEval);
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| 200 | lEvalHyps.add(lEvaluatorHypothesis);
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| 201 | lEvaluatorHypothesis.setDesc("downhill");
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| 202 | // triangular
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| 203 | lDiscreteEval = new EvaluatorDiscrete();
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| 204 | int halfway = lDiscIssue.getNumberOfValues() / 2;
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| 205 | for (int j = 0; j < lDiscIssue.getNumberOfValues(); j++)
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| 206 | if (j < halfway)
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| 207 | lDiscreteEval.addEvaluation(lDiscIssue.getValue(j),
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| 208 | 1000 * j + 1);
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| 209 | // (double)j/(((double)(lDiscIssue.getNumberOfValues()-2))/2));
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| 210 | else
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| 211 | lDiscreteEval
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| 212 | .addEvaluation(
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| 213 | lDiscIssue.getValue(j),
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| 214 | 1000 * (lDiscIssue
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| 215 | .getNumberOfValues() - j - 1) + 1);
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| 216 | // 1.0-(j-((double)(lDiscIssue.getNumberOfValues())-1)/2)/(((double)(lDiscIssue.getNumberOfValues())-1)-((double)(lDiscIssue.getNumberOfValues())-1)/2));
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| 217 | lEvaluatorHypothesis = new EvaluatorHypothesis(
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| 218 | lDiscreteEval);
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| 219 | lEvalHyps.add(lEvaluatorHypothesis);
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| 220 | lEvaluatorHypothesis.setDesc("triangular");
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| 221 | }
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| 222 | break;
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| 223 |
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| 224 | }
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| 225 | }
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| 226 | // each issue is estimated by a uphill, downhill, or triangular function
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| 227 | // an hypothesis about the space, is therefore a choice for uphill,
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| 228 | // downhill, or triangular for each issue.
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| 229 | // For example; if there are 6 issues, then there are 3^6 possible
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| 230 | // combinations for the issues alone!
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| 231 | buildEvaluationHyps();
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| 232 | // createFrom all hypothesis, all combinations of weights hypothesis and
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| 233 | // evaluations.
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| 234 | // For example, if there are 6 issues, then there are 6! possible weight
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| 235 | // orderings, which
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| 236 | // with all 3^6 evaluation hypothesis leads to 6! * 3^6 combinations.
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| 237 | buildUniformHyps();
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| 238 | }
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| 239 |
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| 240 | private void buildUniformHyps() {
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| 241 | fUSHyps = new ArrayList<UtilitySpaceHypothesis>();
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| 242 | for (int i = 0; i < fWeightHyps.length; i++) {
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| 243 | // EvaluatorHypothesis[] lEvalHyps = new
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| 244 | // EvaluatorHypothesis[fUS.getNrOfEvaluators()];
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| 245 | for (int j = 0; j < fEvalHyps.size(); j++) {
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| 246 | UtilitySpaceHypothesis lUSHyp = new UtilitySpaceHypothesis(
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| 247 | fDomain, fUS, fWeightHyps[i], fEvalHyps.get(j));
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| 248 | fUSHyps.add(lUSHyp);
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| 249 | }
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| 250 | }
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| 251 | // normalize intial utilities
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| 252 | for (int i = 0; i < fUSHyps.size(); i++) {
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| 253 | fUSHyps.get(i).setProbability(1 / (double) (fUSHyps.size()));
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| 254 | }
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| 255 | }
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| 256 |
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| 257 | private void reverse(double[] P, int m) {
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| 258 | int i = 0, j = m;
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| 259 | while (i < j) {
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| 260 | // swap elements i and j
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| 261 | double lTmp = P[i];
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| 262 | P[i] = P[j];
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| 263 | P[j] = lTmp;
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| 264 | ++i;
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| 265 | --j;
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| 266 | }
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| 267 | }
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| 268 |
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| 269 | private Integer antilex(Integer index, WeightHypothesis[] hyps, double[] P,
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| 270 | int m) {
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| 271 | if (m == 0) {
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| 272 | WeightHypothesis lWH = new WeightHypothesis(fDomain);
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| 273 | for (int i = 0; i < P.length; i++)
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| 274 | lWH.setWeight(i, P[i]);
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| 275 | hyps[index] = lWH;
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| 276 | index++;
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| 277 | } else {
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| 278 | for (int i = 0; i <= m; i++) {
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| 279 | index = antilex(index, hyps, P, m - 1);
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| 280 | if (i < m) {
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| 281 | // swap elements i and m
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| 282 | double lTmp = P[i];
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| 283 | P[i] = P[m];
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| 284 | P[m] = lTmp;
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| 285 | reverse(P, m - 1);
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| 286 | } // if
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| 287 | }
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| 288 | }
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| 289 | return index;
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| 290 | }
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| 291 |
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| 292 | private double conditionalDistribution(double pUtility,
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| 293 | double pPreviousBidUtility) {
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| 294 | // TODO: check this conditionb
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| 295 | if (pPreviousBidUtility < pUtility)
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| 296 | return 0;
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| 297 | else {
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| 298 |
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| 299 | double x = (pPreviousBidUtility - pUtility) / pPreviousBidUtility;
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| 300 | double lResult = 1 / (SIGMA * Math.sqrt(2 * Math.PI))
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| 301 | * Math.exp(-(x * x) / (2 * SIGMA * SIGMA));
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| 302 | return lResult;
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| 303 | }
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| 304 | }
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| 305 |
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| 306 | public void updateBeliefs(Bid pBid) throws Exception {
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| 307 | fBiddingHistory.add(pBid);
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| 308 | if (haveSeenBefore(pBid))
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| 309 | return;
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| 310 | // calculate full probability for the given bid
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| 311 | double lFullProb = 0;
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| 312 | double lMaxProb = 0;
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| 313 | for (int i = 0; i < fUSHyps.size(); i++) {
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| 314 | UtilitySpaceHypothesis hyp = fUSHyps.get(i);
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| 315 | double condDistrib = hyp.getProbability()
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| 316 | * conditionalDistribution(fUSHyps.get(i).getUtility(pBid),
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| 317 | fPreviousBidUtility);
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| 318 | lFullProb += condDistrib;
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| 319 | if (condDistrib > lMaxProb)
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| 320 | lMaxProb = condDistrib;
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| 321 | hyp.setProbability(condDistrib);
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| 322 | }
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| 323 | if (fUseMostProbableHypsOnly)
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| 324 | fMostProbableUSHyps = new ArrayList<UtilitySpaceHypothesis>();
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| 325 | // receiveMessage the weights hyps and evaluators hyps
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| 326 | double lMostProbableHypFullProb = 0;
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| 327 | for (int i = 0; i < fUSHyps.size(); i++) {
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| 328 | UtilitySpaceHypothesis hyp = fUSHyps.get(i);
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| 329 | double normalizedProbability = hyp.getProbability() / lFullProb;
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| 330 | hyp.setProbability(normalizedProbability);
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| 331 | if (fUseMostProbableHypsOnly)
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| 332 | if (normalizedProbability > lMaxProb * 0.99 / lFullProb) {
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| 333 | fMostProbableUSHyps.add(hyp);
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| 334 | lMostProbableHypFullProb += normalizedProbability;
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| 335 | }
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| 336 | }
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| 337 | if (fUseMostProbableHypsOnly) {
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| 338 | for (int i = 0; i < fMostProbableUSHyps.size(); i++) {
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| 339 | UtilitySpaceHypothesis hyp = fMostProbableUSHyps.get(i);
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| 340 | double normalizedProbability = hyp.getProbability()
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| 341 | / lMostProbableHypFullProb;
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| 342 | hyp.setProbability(normalizedProbability);
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| 343 | }
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| 344 | }
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| 345 |
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| 346 | /*
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| 347 | * sortHyps(); for(int i=0;i<10;i++) {
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| 348 | * System.out.println(fUSHyps.get(i).toString()); }
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| 349 | */
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| 350 | System.out.println("BA: Using "
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| 351 | + String.valueOf(fMostProbableUSHyps.size()) + " out of "
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| 352 | + String.valueOf(fUSHyps.size()) + "hyps");
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| 353 | System.out.println(getMaxHyp().toString());
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| 354 | // calculate utility of the next partner's bid according to the
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| 355 | // concession functions
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| 356 | fPreviousBidUtility = fPreviousBidUtility - EXPECTED_CONCESSION_STEP;
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| 357 | // findMinMaxUtility();
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| 358 | }
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| 359 |
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| 360 | private void buildEvaluationHypsRecursive(
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| 361 | ArrayList<EvaluatorHypothesis[]> pHyps,
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| 362 | EvaluatorHypothesis[] pEval, int m) {
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| 363 | if (m == 0) {
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| 364 | ArrayList<EvaluatorHypothesis> lEvalHyps = fEvaluatorHyps.get(fUS
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| 365 | .getNrOfEvaluators() - 1);
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| 366 | for (int i = 0; i < lEvalHyps.size(); i++) {
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| 367 | pEval[fUS.getNrOfEvaluators() - 1] = lEvalHyps.get(i);
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| 368 | EvaluatorHypothesis[] lTmp = new EvaluatorHypothesis[fUS
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| 369 | .getNrOfEvaluators()];
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| 370 | // copy to temp array
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| 371 | for (int j = 0; j < lTmp.length; j++)
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| 372 | lTmp[j] = pEval[j];
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| 373 | pHyps.add(lTmp);
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| 374 | }
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| 375 | } else {
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| 376 | ArrayList<EvaluatorHypothesis> lEvalHyps = fEvaluatorHyps.get(fUS
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| 377 | .getNrOfEvaluators() - m - 1);
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| 378 | for (int i = 0; i < lEvalHyps.size(); i++) {
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| 379 | pEval[fUS.getNrOfEvaluators() - m - 1] = lEvalHyps.get(i);
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| 380 | buildEvaluationHypsRecursive(pHyps, pEval, m - 1);
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| 381 | }
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| 382 | }
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| 383 | }
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| 384 |
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| 385 | private void buildEvaluationHyps() {
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| 386 | fEvalHyps = new ArrayList<EvaluatorHypothesis[]>();
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| 387 | EvaluatorHypothesis[] lTmp = new EvaluatorHypothesis[fUS
|
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| 388 | .getNrOfEvaluators()];
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| 389 | buildEvaluationHypsRecursive(fEvalHyps, lTmp,
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| 390 | fUS.getNrOfEvaluators() - 1);
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| 391 | }
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| 392 |
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| 393 | public double getExpectedUtility(Bid pBid) throws Exception {
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| 394 | double lExpectedUtility = 0;
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| 395 | if (fUseMostProbableHypsOnly && (fMostProbableUSHyps != null)) {
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| 396 | for (int i = 0; i < fMostProbableUSHyps.size(); i++) {
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| 397 | UtilitySpaceHypothesis lUSHyp = fMostProbableUSHyps.get(i);
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| 398 | double p = lUSHyp.getProbability();
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| 399 | double u = lUSHyp.getUtility(pBid);
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| 400 | lExpectedUtility += p * u;
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| 401 | }
|
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| 402 | } else {
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| 403 | for (int i = 0; i < fUSHyps.size(); i++) {
|
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| 404 | UtilitySpaceHypothesis lUSHyp = fUSHyps.get(i);
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| 405 | double p = lUSHyp.getProbability();
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| 406 | double u = lUSHyp.getUtility(pBid);
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| 407 | lExpectedUtility += p * u;
|
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| 408 | }
|
|---|
| 409 | }
|
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| 410 | return lExpectedUtility;
|
|---|
| 411 | }
|
|---|
| 412 |
|
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| 413 | public double getExpectedWeight(int pIssueNumber) {
|
|---|
| 414 | double lExpectedWeight = 0;
|
|---|
| 415 | for (int i = 0; i < fUSHyps.size(); i++) {
|
|---|
| 416 | UtilitySpaceHypothesis lUSHyp = fUSHyps.get(i);
|
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| 417 | double p = lUSHyp.getProbability();
|
|---|
| 418 | double u = lUSHyp.getHeightHyp().getWeight(pIssueNumber);
|
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| 419 | lExpectedWeight += p * u;
|
|---|
| 420 | }
|
|---|
| 421 | return lExpectedWeight;
|
|---|
| 422 | }
|
|---|
| 423 |
|
|---|
| 424 | public double getNormalizedWeight(Issue i, int startingNumber) {
|
|---|
| 425 | double sum = 0;
|
|---|
| 426 | for (Issue issue : fDomain.getIssues()) {
|
|---|
| 427 | sum += getExpectedWeight(issue.getNumber() - startingNumber);
|
|---|
| 428 | }
|
|---|
| 429 | return (getExpectedWeight(i.getNumber() - startingNumber)) / sum;
|
|---|
| 430 | }
|
|---|
| 431 |
|
|---|
| 432 | private UtilitySpaceHypothesis getMaxHyp() {
|
|---|
| 433 | UtilitySpaceHypothesis lHyp = fUSHyps.get(0);
|
|---|
| 434 | for (int i = 0; i < fUSHyps.size(); i++) {
|
|---|
| 435 | if (lHyp.getProbability() < fUSHyps.get(i).getProbability())
|
|---|
| 436 | lHyp = fUSHyps.get(i);
|
|---|
| 437 | }
|
|---|
| 438 | return lHyp;
|
|---|
| 439 | }
|
|---|
| 440 |
|
|---|
| 441 | /*
|
|---|
| 442 | * public double getExpectedUtility(Bid pBid) { double lExpectedUtility = 0;
|
|---|
| 443 | * for(int i=0;i<fWeightHyps.length;i++) { WeightHypothesis lWeightHyp =
|
|---|
| 444 | * fWeightHyps[i]; double p = lWeightHyp.getProbability(); double u = 0;
|
|---|
| 445 | * for(int j=0;j<fEvalHyps.size();j++) { EvaluatorHypothesis[] lHyp =
|
|---|
| 446 | * fEvalHyps.get(j); //calculate evaluation value and probability for(int
|
|---|
| 447 | * k=0;k<lHyp.length;k++) { p = p*lHyp[k].getProbability(); u = u +
|
|---|
| 448 | * lWeightHyp
|
|---|
| 449 | * .getWeight(k)*(Double)(lHyp[k].getEvaluator().getEvaluation(fUS, pBid,
|
|---|
| 450 | * k)); } lExpectedUtility = lExpectedUtility+ p*u; } } return 0; }
|
|---|
| 451 | */
|
|---|
| 452 | // Evaluate n!
|
|---|
| 453 | private int factorial(int n) {
|
|---|
| 454 | if (n <= 1) // base case
|
|---|
| 455 | return 1;
|
|---|
| 456 | else
|
|---|
| 457 | return n * factorial(n - 1);
|
|---|
| 458 | }
|
|---|
| 459 |
|
|---|
| 460 | public void setMostProbableUSHypsOnly(boolean value) {
|
|---|
| 461 | fUseMostProbableHypsOnly = value;
|
|---|
| 462 | }
|
|---|
| 463 |
|
|---|
| 464 | protected class HypsComparator implements java.util.Comparator {
|
|---|
| 465 | public int compare(Object o1, Object o2) throws ClassCastException {
|
|---|
| 466 | if (!(o1 instanceof UtilitySpaceHypothesis)) {
|
|---|
| 467 | throw new ClassCastException();
|
|---|
| 468 | }
|
|---|
| 469 | if (!(o2 instanceof UtilitySpaceHypothesis)) {
|
|---|
| 470 | throw new ClassCastException();
|
|---|
| 471 | }
|
|---|
| 472 | double d1 = ((UtilitySpaceHypothesis) o1).getProbability();
|
|---|
| 473 | double d2 = ((UtilitySpaceHypothesis) o2).getProbability();
|
|---|
| 474 |
|
|---|
| 475 | if (d1 > d2) {
|
|---|
| 476 | return -1;
|
|---|
| 477 | } else if (d1 < d2) {
|
|---|
| 478 | return 1;
|
|---|
| 479 | } else {
|
|---|
| 480 | return 0;
|
|---|
| 481 | }
|
|---|
| 482 | }
|
|---|
| 483 | }
|
|---|
| 484 |
|
|---|
| 485 | }
|
|---|
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