/// <summary> /// Computes the composite output of the cluster. /// </summary> private double[] ComputeCompositeOutput(List <Tuple <int, double[]> > memberNetOutputs) { if (Output == TNRNet.OutputType.Real) { //Real output double[] output = new double[NumOfOutputs]; for (int outIdx = 0; outIdx < NumOfOutputs; outIdx++) { //Compute weighted average of members single output WeightedAvg wAvg = new WeightedAvg(); for (int i = 0; i < memberNetOutputs.Count; i++) { //Add sub-result to weighted average wAvg.AddSample(memberNetOutputs[i].Item2[outIdx], _memberNetWeights[i]); } //Store averaged output output[outIdx] = wAvg.Result; } //Return averaged outputs return(output); } else { //Probabilistic or SingleBool output int numOfOutputProbabilities = memberNetOutputs[0].Item2.Length; double[] outProbabilities = new double[numOfOutputProbabilities]; for (int pIdx = 0; pIdx < numOfOutputProbabilities; pIdx++) { double[] memberPs = new double[NumOfMembers]; for (int i = 0; i < NumOfMembers; i++) { memberPs[i] = PMixer.ProbabilityRange.Rescale(memberNetOutputs[i].Item2[pIdx], OutputDataRange); } //Compute members mixed probability outProbabilities[pIdx] = PMixer.MixP(memberPs, _memberNetWeights); } if (numOfOutputProbabilities > 1) { outProbabilities.ScaleToNewSum(1d); } //Rescale probabilities to output range for (int pIdx = 0; pIdx < numOfOutputProbabilities; pIdx++) { outProbabilities[pIdx] = OutputDataRange.Rescale(outProbabilities[pIdx], PMixer.ProbabilityRange); } return(outProbabilities); } }
//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)); } }