public HybridGPMSMarginalEstimate(
int maxIterationMemory,
int[] priorSampleCounts,
double[] priorMeans,
double[] priorStandardDevs,
double boundErrorProbability,
int testSamples,
NoiseVariance noise,
int iterationSkips) {
iteration = -1;
this.iterationSkips = iterationSkips;
this.testSamples = testSamples;
this.maxIterationMemory = maxIterationMemory;
this.priorSampleCounts = priorSampleCounts;
this.priorMeans = priorMeans;
this.priorStandardDevs = priorStandardDevs;
this.noise = noise;
boundDist = NormalDist.xnormi(boundErrorProbability);
currentDomainTVals = new HashMap<MultiballVariableState, Double>();
memory = new MarginalFunctionMemory(maxIterationMemory);
xVals = null;
yVals = null;
tVals = null;
predictor = null;
stateArray = null;
}
@Override
public TrueMarginalFunctionEstimation copy() {
return new HybridGPMSMarginalEstimate(
maxIterationMemory,
priorSampleCounts,
priorMeans,
priorStandardDevs,
NormalDist.normp(boundDist),
testSamples,
noise,
iterationSkips);
}
private double expectedGain(double currentBound, double mean, double sd) {
return (currentBound
+ ((mean - currentBound) * NormalDist.phi(currentBound, mean, sd))
- (sd * NormalDist.N(currentBound, mean, sd)));
}