@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;
}
public MarginalValues<?> calculateMarginalMaxFunction(
Map<Variable<?, ?>, MarginalValues<?>> sortedMessages, Variable<?, ?> variable) {
DiscreteVariable<?> destinationVariable = (DiscreteVariable<?>) variable;
DiscreteMarginalValues<?> values = new DiscreteMarginalValues();
for (DiscreteVariableState state : destinationVariable.getDomain()) {
values.put(state, findMaxValue(destinationVariable, state, sortedMessages));
}
for (DiscreteVariable<?> var : function.getVariableExpansionOrder()) {
if (!sortedMessages.containsKey(var)) continue;
DiscreteVariable var2 = (DiscreteVariable) function.getVariableDependency(var);
// if (var2 == null) {
// System.out.println(function.getVariableExpansionOrder());
// System.out.println(var);
// System.out.println(function.getVariableDependencies());
// }
//
// if (!var.getDomain().equals(var2.getDomain())) {
// System.out.println(var.getDomain());
// System.out.println(var2.getDomain());
// throw new IllegalArgumentException();
// }
DiscreteMarginalValues<?> marginalValues =
(DiscreteMarginalValues<?>) sortedMessages.get(var);
if (marginalValues == null) {
System.out.println(sortedMessages);
System.out.println(var);
throw new IllegalArgumentException();
}
// if
// (!marginalValues.getValues().keySet().equals(var.getDomain().getStates()))
// {
// System.out.println(marginalValues);
// System.out.println(var.getDomain());
// throw new IllegalArgumentException();
// }
}
if (validate) {
DiscreteMarginalValues<?> expectedValues =
(DiscreteMarginalValues<?>)
groundTruth.calculateMarginalMaxFunction(sortedMessages, variable);
boolean error = false;
for (DiscreteVariableState state : values.getValues().keySet()) {
if (Math.abs(values.getValues().get(state) - expectedValues.getValues().get(state))
> 1e-10) {
error = true;
break;
}
}
if (error) {
System.out.println(
"Value from OptimisedMarginalMaximisation is not equal to BBDiscreteMarginalMaximisation");
System.out.println(sortedMessages);
System.out.println("Expected: " + expectedValues);
System.out.println("Got " + values);
for (DiscreteVariableState state : values.getValues().keySet()) {
System.out.println(state);
System.out.println(values.getValues().get(state));
System.out.println(expectedValues.getValues().get(state));
}
throw new IllegalArgumentException();
}
}
return values;
}