public CMAESCandidate(double[] weightIN, double[][] trainingDataIN, double[][] targetsIN, PUFObjectiveFunction functionIN) { //WeightVector = (double[])weightIN.Clone(); //TrainingData = (double[][])trainingDataIN.Clone(); //Targets = (double[][])targetsIN.Clone(); WeightVector = weightIN; TrainingData = trainingDataIN; Targets = targetsIN; FunctionP = functionIN; ObjFunctionValue = FunctionP.ObjFunValue(WeightVector, TrainingData, Targets); }
public static CMAESCandidate ComputeCMAES(int dimensionNumber, PUFObjectiveFunction pFunction, double[][] trainingData, double[][] targets, double[] initialWeightVector, Random randomGenerator) { int n = dimensionNumber; //number of weights + epsilon Matrix <double> xMean = Matrix <double> .Build.Dense(n, 1); CMAESCandidate c11 = new CMAESCandidate(initialWeightVector, trainingData, targets, pFunction); Console.Out.WriteLine("Starting Point Accuracy"); Console.Out.WriteLine(c11.GetObjectiveFunctionValue().ToString()); CMAESCandidate globalBestCandidate = null; double sigma = 0.5; //double stopfitness = 1e-10; //double stopfitness = 0.99; double stopfitness = double.MaxValue; int stopeval = 1000; //originally was 30000 //double stopeval = AppConstants.MaxEvaluations //Strategy parameter setting: Selection int lambdaVal = (int)(4.0 + Math.Floor(3.0 * Math.Log(n))); //population size, note lambda keyword reserved by python so lambda->lambdaVal //lambdaVal = AppConstants.PopulationSizeCMAES #change by KRM cause I think we need more sampling double mu = lambdaVal / 2.0; Matrix <double> weights = Matrix <double> .Build.Dense((int)mu, 1); //weights = numpy.matrix(weightsPrime, dtype = float).reshape(mu, 1) for (int i = 0; i < (int)mu; i++) { weights[i, 0] = Math.Log(mu + 0.5) - Math.Log(i + 1); //weights[i, 0] = math.log(mu + 0.5) - math.log((i + 1))#use i+1 instead of i in this case to match matlab indexing } mu = Math.Floor(mu); double sumWeights = 0.0; for (int i = 0; i < weights.RowCount; i++) { sumWeights = sumWeights + weights[i, 0]; } //Divide by the sum for (int i = 0; i < weights.RowCount; i++) { weights[i, 0] = weights[i, 0] / sumWeights; } //Computation for the mueff variable double mueffNum = 0.0; double mueffDem = 0.0; for (int i = 0; i < weights.RowCount; i++) { mueffNum = weights[i, 0] + mueffNum; mueffDem = weights[i, 0] * weights[i, 0] + mueffDem; } mueffNum = mueffNum * mueffNum; double mueff = mueffNum / mueffDem; // Strategy parameter setting: Adaptation double cc = (4.0 + mueff / n) / (n + 4.0 + 2.0 * mueff / n); //#time constant for cumulation for C double cs = (mueff + 2.0) / (n + mueff + 5.0); //#t-const for cumulation for sigma control double c1 = 2.0 / ((n + 1.3) * (n + 1.3) + mueff); //#learning rate for rank-one update of C double cmu = Math.Min(1.0 - c1, 2.0 * (mueff - 2.0 + 1.0 / mueff) / ((n + 2) * (n + 2) + mueff)); //# and for rank-mu update double damps = 1.0 + 2.0 * Math.Max(0, Math.Sqrt((mueff - 1) / (n + 1)) - 1) + cs; //# damping for sigma //Initialize dynamic (internal) strategy parameters and constants //evolution paths for C and sigma Matrix <double> pc = Matrix <double> .Build.Dense(n, 1); Matrix <double> ps = Matrix <double> .Build.Dense(n, 1); Matrix <double> D = Matrix <double> .Build.Dense(n, 1); for (int i = 0; i < n; i++) { pc[i, 0] = 0; ps[i, 0] = 0; D[i, 0] = 1.0; } //Create B Matrix Matrix <double> B = Matrix <double> .Build.Dense(n, n); for (int i = 0; i < n; i++) { for (int j = 0; j < n; j++) { if (i == j) { B[i, j] = 1.0; } else { B[i, j] = 0.0; } } } //Create C Matrix Matrix <double> dSquare = Matrix <double> .Build.Dense(n, 1); for (int i = 0; i < n; i++) { dSquare[i, 0] = D[i, 0] * D[i, 0]; } Matrix <double> C = B * Diagonalize1DMatrix(dSquare) * B.Transpose(); //C = B * self.diag(DSquare) * numpy.transpose(B) //Create invertsqrtC Matrix Matrix <double> oneOverD = Matrix <double> .Build.Dense(n, 1); for (int i = 0; i < n; i++) { oneOverD[i, 0] = 1.0 / D[i, 0]; } Matrix <double> invsqrtC = B * Diagonalize1DMatrix(oneOverD) * B.Transpose(); double eigeneval = 0; //track update of B and D double chiN = Math.Pow(n, 0.5) * (1.0 - 1.0 / (4.0 * n) + 1.0 / (21.0 * Math.Pow(n, 2.0))); int counteval = 0; //the next 40 lines contain the 20 lines of interesting code //CMAESCandidate[] candidateArray = new CMAESCandidate[lambdaVal]; List <CMAESCandidate> candidateArray = new List <CMAESCandidate>(); for (int i = 0; i < lambdaVal; i++) { candidateArray.Add(new CMAESCandidate()); } while (counteval < stopeval) { Matrix <double> arx = Matrix <double> .Build.Dense(n, lambdaVal); //fill in the initial solutions for (int i = 0; i < lambdaVal; i++) { Matrix <double> randD = Matrix <double> .Build.Dense(n, 1); for (int j = 0; j < n; j++) { randD[j, 0] = D[j, 0] * GenerateRandomNormalVariableForCMAES(randomGenerator, 0, 1.0); } Matrix <double> inputVector = xMean + sigma * B * randD; double[] tempWeightVector = new double[inputVector.RowCount]; for (int k = 0; k < inputVector.RowCount; k++) { tempWeightVector[k] = inputVector[k, 0]; } candidateArray[i] = new CMAESCandidate(tempWeightVector, trainingData, targets, pFunction); counteval = counteval + 1; } candidateArray.Sort(); //This maybe problematic, not sure about sorting in C# candidateArray.Reverse(); Matrix <double> xOld = xMean.Clone(); //Get the new mean value Matrix <double> arxSubset = Matrix <double> .Build.Dense(n, (int)mu); //in Maltab this variable would be "arx(:,arindex(1:mu)) //This replaces line arxSubset[:, i] = CandidateList[i].InputVector for (int i = 0; i < mu; i++) { for (int j = 0; j < n; j++) { arxSubset[j, i] = candidateArray[i].GetWeightVector()[j]; } } xMean = arxSubset * weights; //Line 76 Matlab //Cumulation: Update evolution paths ps = (1 - cs) * ps + Math.Sqrt(cs * (2.0 - cs) * mueff) * invsqrtC * (xMean - xOld) / sigma; //Compute ps.^2 equivalent double psSquare = 0; for (int i = 0; i < ps.RowCount; i++) { psSquare = psSquare + ps[i, 0] * ps[i, 0]; } //Compute hsig double hSig = 0.0; double term1ForHsig = psSquare / (1.0 - Math.Pow(1.0 - cs, 2.0 * counteval / lambdaVal)) / n; double term2ForHsig = 2.0 + 4.0 / (n + 1.0); if (term1ForHsig < term2ForHsig) { hSig = 1.0; } //Compute pc, Line 82 Matlab pc = (1.0 - cc) * pc + hSig * Math.Sqrt(cc * (2.0 - cc) * mueff) * (xMean - xOld) / sigma; //Adapt covariance matrix C Matrix <double> repmatMatrix = Tile((int)mu, xOld); //NOT SURE IF THIS IS RIGHT IN C# FIX repmatMatrix = numpy.tile(xold, mu) Matrix <double> artmp = (1.0 / sigma) * (arxSubset - repmatMatrix); // C = (1-c1-cmu) * C + c1 * (pc * pc' + (1-hsig) * cc*(2-cc) * C) + cmu * artmp * diag(weights) * artmp' #This is the original Matlab line for reference C = (1.0 - c1 - cmu) * C + c1 * (pc * pc.Transpose() + (1 - hSig) * cc * (2.0 - cc) * C) + cmu * artmp * Diagonalize1DMatrix(weights) * artmp.Transpose(); //Adapt step size sigma //sigma = sigma * Math.Exp((cs / damps) * (numpy.linalg.norm(ps) / chiN - 1)) sigma = sigma * Math.Exp((cs / damps) * (ps.L2Norm() / chiN - 1)); //NOT SURE IF THIS IS RIGHT FIX IN C# //Update B and D from C if ((counteval - eigeneval) > (lambdaVal / (c1 + cmu) / n / 10.0)) { eigeneval = counteval; //C = numpy.triu(C) + numpy.transpose(numpy.triu(C, 1)) #enforce symmetry C = C.UpperTriangle() + C.StrictlyUpperTriangle().Transpose(); //NOT SURE IF THIS IS RIGHT FIX IN C# //eigen decomposition Evd <double> eigen = C.Evd(); B = eigen.EigenVectors; Vector <System.Numerics.Complex> vectorEigenValues = eigen.EigenValues; for (int i = 0; i < vectorEigenValues.Count; i++) { D[i, 0] = vectorEigenValues[i].Real; } //take sqrt of D for (int i = 0; i < vectorEigenValues.Count; i++) { D[i, 0] = Math.Sqrt(D[i, 0]); } for (int i = 0; i < n; i++) { oneOverD[i, 0] = 1.0 / D[i, 0]; } Matrix <double> middleTerm = Diagonalize1DMatrix(oneOverD); //#Built in Numpy function doesn't create the right size matrix in this case (ex: Numpy gives 1x1 but should be 5x5) invsqrtC = B * middleTerm * B.Transpose(); } globalBestCandidate = candidateArray[0]; //Termination Conditions //bestCandidate = candidateArray[0]; //Array index 0 has the smallest objective function value //if (bestCandidate.GetObjectiveFunctionValue() < currentBestValue) //{ // currentBestValue = bestCandidate.GetObjectiveFunctionValue(); // globalBestCandidate = (CMAESCandidate)bestCandidate.Clone(); //} //if (bestCandidate.GetObjectiveFunctionValue() >= stopfitness) //{ // return globalBestCandidate; //} //Add in extra termination conditions //if (bestCandidate.GetObjectiveFunctionValue() == currentBestValue) //{ // repeatCount = repeatCount + 1; //} //else //{ // repeatCount = 0; //} ////we have repeated too many times //if (repeatCount > 10) //{ // Console.Out.WriteLine("CMA-ES terminated to due to repeated best."); // return globalBestCandidate; //} Console.Out.WriteLine("Iteration #" + counteval.ToString()); //Console.Out.WriteLine("Best Value=" + (1.0 - currentBestValue).ToString()); Console.Out.WriteLine("Current Value=" + (candidateArray[candidateArray.Count - 1].GetObjectiveFunctionValue()).ToString()); }//end while loop return(globalBestCandidate); //just in case everything terminates }