@Override
public void updateEstimate(DiscreteMarginalValues<MultiballVariableState> observedValues) {
iteration++;
iteration %= iterationSkips;
// turn variableDomain into an array of X values
Set<MultiballVariableState> states = observedValues.getValues().keySet();
xVals = new double[states.size()];
stateArray = new MultiballVariableState[xVals.length];
int i = 0;
for (MultiballVariableState state : states) {
xVals[i] = state.getValue();
stateArray[i] = state;
i++;
}
// turn marginalFunction into an array of Y values and t values
yVals = new double[xVals.length];
tVals = new double[xVals.length];
for (i = 0; i < xVals.length; i++) {
yVals[i] = observedValues.getValue(stateArray[i]);
tVals[i] = getAndIncrementTVal(stateArray[i]);
}
memory.add(xVals, yVals, tVals);
if ((iteration % iterationSkips) != 0) return;
Matrix transform = HybridGPMSTransform.generate(memory.iterationNum);
Matrix trainY = transform.times(memory.Y);
// create Gaussian process Bayesian Markov chain to estimate worth of setting
// states to new values
GaussianProcessRegressionBMC regression = new GaussianProcessRegressionBMC();
regression.setCovarianceFunction(
new HybridGPMSCovarianceFunction(
SquaredExponentialCovarianceFunction.getInstance(), transform, noise));
BasicPrior[] priors = new BasicPrior[priorMeans.length];
for (i = 0; i < priorMeans.length; i++) {
priors[i] = new BasicPrior(priorSampleCounts[i], priorMeans[i], priorStandardDevs[i]);
}
regression.setPriors(priors);
predictor = regression.calculateRegression(memory.X, trainY);
worstCurrentVal = Double.POSITIVE_INFINITY;
for (i = 0; i < xVals.length; i++) {
double worth = evaluate(xVals[i], tVals[i] + 1);
if (worth < worstCurrentVal) {
worstCurrentVal = worth;
worstCurrentIndex = i;
}
}
bestSoFar = worstCurrentVal;
}
