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);
}
}
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);
}
}
