//Methods
/// <summary>
/// Computes weighted averaged output of the cluster member networks
/// </summary>
/// <param name="predictors">Input predictors</param>
/// <param name="memberOutputCollection">Collection of outputs of cluster member networks</param>
public double Compute(double[] predictors, out double[] memberOutputCollection)
{
//Init member output collection
memberOutputCollection = new double[Members.Count];
if (!BinaryOutput)
{
//Result is exact value
WeightedAvg weightedResult = new WeightedAvg();
//Loop cluster members
int clusterMemberIdx = 0;
foreach (TrainedNetwork clusterMember in Members)
{
//Compute member
double computedValue = clusterMember.Network.Compute(predictors)[0];
//Store sub-results
memberOutputCollection[clusterMemberIdx] = computedValue;
//Add sub-result to weighted average
weightedResult.AddSampleValue(computedValue, Weights[clusterMemberIdx]);
++clusterMemberIdx;
}
//Return weighted average result
return(weightedResult.Avg);
}
else
{
//Result is a probability -> use probability mixer
double[] probabilities = new double[Members.Count];
double[] weights = new double[Members.Count];
for (int i = 0; i < Members.Count; i++)
{
memberOutputCollection[i] = Members[i].Network.Compute(predictors)[0];
probabilities[i] = PMixer.ProbabilityRange.Rescale(memberOutputCollection[i], DataRange);
weights[i] = Weights[i];
}
//Scale weights to ensure their sum is equal to 1
weights.Scale(1d / weights.Sum());
//Return resulting mixed probability rescalled back to members' result range
return(DataRange.Rescale(PMixer.MixP(probabilities, weights), PMixer.ProbabilityRange));
}
}
