public void Train(DenseMatrix X, DenseVector d, DenseVector Kd) { int R = X.RowCount; int N = X.ColumnCount; int U = 0; //the number of neurons in the structure var c = new DenseMatrix(R, 1); var sigma = new DenseMatrix(R, 1); var Q = new DenseMatrix((R + 1), (R + 1)); var O = new DenseMatrix(1, (R + 1)); var pT_n = new DenseMatrix((R + 1), 1); double maxPhi = 0; int maxIndex; var Psi = new DenseMatrix(N, 1); Console.WriteLine("Running..."); //for each observation n in X for (int i = 0; i < N; i++) { Console.WriteLine(100*(i/(double) N) + "%"); var x = new DenseVector(R); X.Column(i, x); //if there are neurons in structure, //update structure recursively. if (U == 0) { c = (DenseMatrix) x.ToColumnMatrix(); sigma = new DenseMatrix(R, 1, SigmaZero); U = 1; Psi = CalculatePsi(X, c, sigma); UpdateStructure(X, Psi, d, ref Q, ref O); pT_n = (DenseMatrix) (CalculateGreatPsi((DenseMatrix) x.ToColumnMatrix(), (DenseMatrix) Psi.Row(i).ToRowMatrix())) .Transpose(); } else { StructureRecurse(X, Psi, d, i, ref Q, ref O, ref pT_n); } bool KeepSpinning = true; while (KeepSpinning) { //Calculate the error and if-part criteria double ee = pT_n.Multiply(O)[0, 0]; double approximationError = Math.Abs(d[i] - ee); DenseVector Phi; double SumPhi; CalculatePhi(x, c, sigma, out Phi, out SumPhi); maxPhi = Phi.Maximum(); maxIndex = Phi.MaximumIndex(); if (approximationError > delta) { if (maxPhi < threshold) { var tempSigma = new DenseVector(R); sigma.Column(maxIndex, tempSigma); double minSigma = tempSigma.Minimum(); int minIndex = tempSigma.MinimumIndex(); sigma[minIndex, maxIndex] = k_sigma*minSigma; Psi = CalculatePsi(X, c, sigma); UpdateStructure(X, Psi, d, ref Q, ref O); var psi = new DenseVector(Psi.ColumnCount); Psi.Row(i, psi); pT_n = (DenseMatrix) CalculateGreatPsi((DenseMatrix) x.ToColumnMatrix(), (DenseMatrix) psi.ToRowMatrix()) .Transpose(); } else { //add a new neuron and update strucutre double distance = 0; var cTemp = new DenseVector(R); var sigmaTemp = new DenseVector(R); //foreach input variable for (int j = 0; j < R; j++) { distance = Math.Abs(x[j] - c[j, 0]); int distanceIndex = 0; //foreach neuron past 1 for (int k = 1; k < U; k++) { if ((Math.Abs(x[j] - c[j, k])) < distance) { distanceIndex = k; distance = Math.Abs(x[j] - c[j, k]); } } if (distance < Kd[j]) { cTemp[j] = c[j, distanceIndex]; sigmaTemp[j] = sigma[j, distanceIndex]; } else { cTemp[j] = x[j]; sigmaTemp[j] = distance; } } //end foreach c = (DenseMatrix) c.InsertColumn(c.ColumnCount - 1, cTemp); sigma = (DenseMatrix) sigma.InsertColumn(sigma.ColumnCount - 1, sigmaTemp); Psi = CalculatePsi(X, c, sigma); UpdateStructure(X, Psi, d, ref Q, ref O); U++; KeepSpinning = false; } } else { if (maxPhi < threshold) { var tempSigma = new DenseVector(R); sigma.Column(maxIndex, tempSigma); double minSigma = tempSigma.Minimum(); int minIndex = tempSigma.MinimumIndex(); sigma[minIndex, maxIndex] = k_sigma*minSigma; Psi = CalculatePsi(X, c, sigma); UpdateStructure(X, Psi, d, ref Q, ref O); var psi = new DenseVector(Psi.ColumnCount); Psi.Row(i, psi); pT_n = (DenseMatrix) CalculateGreatPsi((DenseMatrix) x.ToColumnMatrix(), (DenseMatrix) psi.ToRowMatrix()) .Transpose(); } else { KeepSpinning = false; } } } } out_C = c; out_O = O; out_Sigma = sigma; Console.WriteLine("Done."); }