Ejemplo n.º 1
0
  public void updateParentsWithProposals() {
    double scoreParent;
    double scoreProposal;
    int nModelEvals = evalResults.getNumberOfEvalResults();

    double logOfUnifRandDraw;
    double[] parameterCombination;
    double objScore;
    double[][] sim = null;

    for (int iPop = 0; iPop < nPop; iPop++) {
      scoreParent = parents.getObjScore(iPop);
      scoreProposal = proposals.getObjScore(iPop);
      logOfUnifRandDraw = Math.log(generator.nextDouble());
      int sampleIdentifier = nModelEvals + iPop;
      int firstOccurrence = -1;
      if (scoreProposal - scoreParent >= logOfUnifRandDraw) {
        // accept proposal
        parameterCombination = proposals.getParameterCombination(iPop);
        objScore = proposals.getObjScore(iPop);
        if (modelIsDynamic) {
          sim = proposals.getModelResult(iPop);
        }
        firstOccurrence = nModelEvals + iPop;
      } else {
        // reject proposal
        parameterCombination = parents.getParameterCombination(iPop);
        objScore = parents.getObjScore(iPop);
        if (modelIsDynamic) {
          sim = parents.getModelResult(iPop);
        }
        firstOccurrence = parents.getFirstOccurrence(iPop);
      }

      parents.setParameterCombination(iPop, parameterCombination);
      parents.setObjScore(iPop, objScore);
      parents.setFirstOccurrence(iPop, firstOccurrence);
      if (modelIsDynamic) {
        parents.setModelResults(iPop, sim);
      }

      // add most recent sample to the record array evalResults:
      EvalResult evalResult;
      if (modelIsDynamic) {
        evalResult =
            new EvalResult(
                sampleIdentifier,
                firstOccurrence,
                parameterCombination.clone(),
                objScore,
                sim.clone());
      } else {
        evalResult =
            new EvalResult(
                sampleIdentifier, firstOccurrence, parameterCombination.clone(), objScore);
      }
      evalResults.add(evalResult);
    }
  }
Ejemplo n.º 2
0
  public void initializeParents() {

    // take uniform random samples of the parameter space and add them to the parents array:
    for (int iPop = 0; iPop < nPop; iPop++) {
      double[] parameterCombination = parSpace.takeUniformRandomSample(generator);
      parents.setParameterCombination(iPop, parameterCombination);
    }

    if (modelIsDynamic) {
      // if the model in question is dynamic, generate the model prediction by running the model.
      // Then feed the list of model
      // predictions into a function that runs the likelihoodfunction on the prediction, yielding a
      // list of objective scores
      parents.calcModelResults();
      parents.calcObjScores(obs);
    } else {
      // if the model in question is not dynamic, use the likelihood function directly to calculate
      // an objective score for each entry in parents.
      parents.calcObjScores();
    }

    // now add the initial values of parents to the record, i.e. evalResults
    for (int iPop = 0; iPop < nPop; iPop++) {
      int sampleIdentifier = iPop;
      double[] parameterCombination = parents.getParameterCombination(iPop).clone();
      double objScore = parents.getObjScore(iPop);
      EvalResult evalResult = null;
      if (modelIsDynamic) {
        evalResult =
            new EvalResult(
                sampleIdentifier,
                iPop,
                parameterCombination,
                objScore,
                parents.getModelResult(iPop));
      } else {
        evalResult = new EvalResult(sampleIdentifier, iPop, parameterCombination, objScore);
      }
      evalResults.add(evalResult);
    }
  }