// // Interface functions // public static svm_model svm_train(svm_problem prob, svm_parameter param) { var model = new svm_model(); model.param = param; if (param.svm_type == svm_parameter.ONE_CLASS || param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR) { // regression or one-class-svm model.nr_class = 2; model.label = null; model.nSV = null; model.probA = null; model.probB = null; model.sv_coef = new double[1][]; if (param.probability == 1 && (param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR)) { model.probA = new double[1]; model.probA[0] = svm_svr_probability(prob, param); } decision_function f = svm_train_one(prob, param, 0, 0); model.rho = new double[1]; model.rho[0] = f.rho; int nSV = 0; int i; for (i = 0; i < prob.l; i++) if (Math.Abs(f.alpha[i]) > 0) ++nSV; model.l = nSV; model.SV = new svm_node[nSV][]; model.sv_coef[0] = new double[nSV]; int j = 0; for (i = 0; i < prob.l; i++) if (Math.Abs(f.alpha[i]) > 0) { model.SV[j] = prob.x[i]; model.sv_coef[0][j] = f.alpha[i]; ++j; } } else { // classification // find out the number of classes int l = prob.l; int max_nr_class = 16; int nr_class = 0; var label = new int[max_nr_class]; var count = new int[max_nr_class]; var index = new int[l]; int i; for (i = 0; i < l; i++) { //UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"' var this_label = (int) prob.y[i]; int j; for (j = 0; j < nr_class; j++) if (this_label == label[j]) { ++count[j]; break; } index[i] = j; if (j == nr_class) { if (nr_class == max_nr_class) { max_nr_class *= 2; var new_data = new int[max_nr_class]; Array.Copy(label, 0, new_data, 0, label.Length); label = new_data; new_data = new int[max_nr_class]; Array.Copy(count, 0, new_data, 0, count.Length); count = new_data; } label[nr_class] = this_label; count[nr_class] = 1; ++nr_class; } } // group training data of the same class var start = new int[nr_class]; start[0] = 0; for (i = 1; i < nr_class; i++) start[i] = start[i - 1] + count[i - 1]; var x = new svm_node[l][]; for (i = 0; i < l; i++) { x[start[index[i]]] = prob.x[i]; ++start[index[i]]; } start[0] = 0; for (i = 1; i < nr_class; i++) start[i] = start[i - 1] + count[i - 1]; // calculate weighted C var weighted_C = new double[nr_class]; for (i = 0; i < nr_class; i++) weighted_C[i] = param.C; for (i = 0; i < param.nr_weight; i++) { int j; for (j = 0; j < nr_class; j++) if (param.weight_label[i] == label[j]) break; if (j == nr_class) Console.Error.Write("warning: class label " + param.weight_label[i] + " specified in weight is not found\n"); else weighted_C[j] *= param.weight[i]; } // train k*(k-1)/2 models var nonzero = new bool[l]; for (i = 0; i < l; i++) nonzero[i] = false; var f = new decision_function[nr_class*(nr_class - 1)/2]; double[] probA = null, probB = null; if (param.probability == 1) { probA = new double[nr_class*(nr_class - 1)/2]; probB = new double[nr_class*(nr_class - 1)/2]; } int p = 0; for (i = 0; i < nr_class; i++) for (int j = i + 1; j < nr_class; j++) { var sub_prob = new svm_problem(); int si = start[i], sj = start[j]; int ci = count[i], cj = count[j]; sub_prob.l = ci + cj; sub_prob.x = new svm_node[sub_prob.l][]; sub_prob.y = new double[sub_prob.l]; int k; for (k = 0; k < ci; k++) { sub_prob.x[k] = x[si + k]; sub_prob.y[k] = + 1; } for (k = 0; k < cj; k++) { sub_prob.x[ci + k] = x[sj + k]; sub_prob.y[ci + k] = - 1; } if (param.probability == 1) { var probAB = new double[2]; svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB); probA[p] = probAB[0]; probB[p] = probAB[1]; } f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]); for (k = 0; k < ci; k++) if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0) nonzero[si + k] = true; for (k = 0; k < cj; k++) if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0) nonzero[sj + k] = true; ++p; } // build output model.nr_class = nr_class; model.label = new int[nr_class]; for (i = 0; i < nr_class; i++) model.label[i] = label[i]; model.rho = new double[nr_class*(nr_class - 1)/2]; for (i = 0; i < nr_class*(nr_class - 1)/2; i++) model.rho[i] = f[i].rho; if (param.probability == 1) { model.probA = new double[nr_class*(nr_class - 1)/2]; model.probB = new double[nr_class*(nr_class - 1)/2]; for (i = 0; i < nr_class*(nr_class - 1)/2; i++) { model.probA[i] = probA[i]; model.probB[i] = probB[i]; } } else { model.probA = null; model.probB = null; } int nnz = 0; var nz_count = new int[nr_class]; model.nSV = new int[nr_class]; for (i = 0; i < nr_class; i++) { int nSV = 0; for (int j = 0; j < count[i]; j++) if (nonzero[start[i] + j]) { ++nSV; ++nnz; } model.nSV[i] = nSV; nz_count[i] = nSV; } Console.Out.Write("Total nSV = " + nnz + "\n"); model.l = nnz; model.SV = new svm_node[nnz][]; p = 0; for (i = 0; i < l; i++) if (nonzero[i]) model.SV[p++] = x[i]; var nz_start = new int[nr_class]; nz_start[0] = 0; for (i = 1; i < nr_class; i++) nz_start[i] = nz_start[i - 1] + nz_count[i - 1]; model.sv_coef = new double[nr_class - 1][]; for (i = 0; i < nr_class - 1; i++) model.sv_coef[i] = new double[nnz]; p = 0; for (i = 0; i < nr_class; i++) for (int j = i + 1; j < nr_class; j++) { // classifier (i,j): coefficients with // i are in sv_coef[j-1][nz_start[i]...], // j are in sv_coef[i][nz_start[j]...] int si = start[i]; int sj = start[j]; int ci = count[i]; int cj = count[j]; int q = nz_start[i]; int k; for (k = 0; k < ci; k++) if (nonzero[si + k]) model.sv_coef[j - 1][q++] = f[p].alpha[k]; q = nz_start[j]; for (k = 0; k < cj; k++) if (nonzero[sj + k]) model.sv_coef[i][q++] = f[p].alpha[ci + k]; ++p; } } return model; }
internal static decision_function svm_train_one(svm_problem prob, svm_parameter param, double Cp, double Cn) { var alpha = new double[prob.l]; var si = new Solver.SolutionInfo(); switch (param.svm_type) { case svm_parameter.C_SVC: solve_c_svc(prob, param, alpha, si, Cp, Cn); break; case svm_parameter.NU_SVC: solve_nu_svc(prob, param, alpha, si); break; case svm_parameter.ONE_CLASS: solve_one_class(prob, param, alpha, si); break; case svm_parameter.EPSILON_SVR: solve_epsilon_svr(prob, param, alpha, si); break; case svm_parameter.NU_SVR: solve_nu_svr(prob, param, alpha, si); break; } Console.Out.Write("obj = " + si.obj + ", rho = " + si.rho + "\n"); // output SVs int nSV = 0; int nBSV = 0; for (int i = 0; i < prob.l; i++) { if (Math.Abs(alpha[i]) > 0) { ++nSV; if (prob.y[i] > 0) { if (Math.Abs(alpha[i]) >= si.upper_bound_p) ++nBSV; } else { if (Math.Abs(alpha[i]) >= si.upper_bound_n) ++nBSV; } } } Console.Out.Write("nSV = " + nSV + ", nBSV = " + nBSV + "\n"); var f = new decision_function(); f.alpha = alpha; f.rho = si.rho; return f; }
static decision_function svm_train_one(Problem prob, Parameter param, double Cp, double Cn) { double[] alpha = new double[prob.Count]; Solver.SolutionInfo si = new Solver.SolutionInfo(); switch (param.SvmType) { case SvmType.C_SVC: solve_c_svc(prob, param, alpha, si, Cp, Cn); break; case SvmType.NU_SVC: solve_nu_svc(prob, param, alpha, si); break; case SvmType.ONE_CLASS: solve_one_class(prob, param, alpha, si); break; case SvmType.EPSILON_SVR: solve_epsilon_svr(prob, param, alpha, si); break; case SvmType.NU_SVR: solve_nu_svr(prob, param, alpha, si); break; } Procedures.info("obj = " + si.obj + ", rho = " + si.rho + "\n"); // output SVs int nSV = 0; int nBSV = 0; for (int i = 0; i < prob.Count; i++) { if (Math.Abs(alpha[i]) > 0) { ++nSV; if (prob.Y[i] > 0) { if (Math.Abs(alpha[i]) >= si.upper_bound_p) ++nBSV; } else { if (Math.Abs(alpha[i]) >= si.upper_bound_n) ++nBSV; } } } Procedures.info("nSV = " + nSV + ", nBSV = " + nBSV + "\n"); decision_function f = new decision_function(); f.alpha = alpha; f.rho = si.rho; return f; }
// // Interface functions // public static Model svm_train(Problem prob, Parameter param) { Model model = new Model(); model.Parameter = param; if (param.SvmType == SvmType.ONE_CLASS || param.SvmType == SvmType.EPSILON_SVR || param.SvmType == SvmType.NU_SVR) { // regression or one-class-svm model.NumberOfClasses = 2; model.ClassLabels = null; model.NumberOfSVPerClass = null; model.PairwiseProbabilityA = null; model.PairwiseProbabilityB = null; model.SupportVectorCoefficients = new double[1][]; if (param.Probability && (param.SvmType == SvmType.EPSILON_SVR || param.SvmType == SvmType.NU_SVR)) { model.PairwiseProbabilityA = new double[1]; model.PairwiseProbabilityA[0] = svm_svr_probability(prob, param); } decision_function f = svm_train_one(prob, param, 0, 0); model.Rho = new double[1]; model.Rho[0] = f.rho; int nSV = 0; int i; for (i = 0; i < prob.Count; i++) if (Math.Abs(f.alpha[i]) > 0) ++nSV; model.SupportVectorCount = nSV; model.SupportVectors = new Node[nSV][]; model.SupportVectorCoefficients[0] = new double[nSV]; int j = 0; for (i = 0; i < prob.Count; i++) if (Math.Abs(f.alpha[i]) > 0) { model.SupportVectors[j] = prob.X[i]; model.SupportVectorCoefficients[0][j] = f.alpha[i]; ++j; } } else { // classification int l = prob.Count; int[] tmp_nr_class = new int[1]; int[][] tmp_label = new int[1][]; int[][] tmp_start = new int[1][]; int[][] tmp_count = new int[1][]; int[] perm = new int[l]; // group training data of the same class svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm); int nr_class = tmp_nr_class[0]; int[] label = tmp_label[0]; int[] start = tmp_start[0]; int[] count = tmp_count[0]; Node[][] x = new Node[l][]; int i; for (i = 0; i < l; i++) x[i] = prob.X[perm[i]]; // calculate weighted C double[] weighted_C = new double[nr_class]; for (i = 0; i < nr_class; i++) weighted_C[i] = param.C; foreach (int weightedLabel in param.Weights.Keys) { int index = Array.IndexOf<int>(label, weightedLabel); if (index < 0) Console.Error.WriteLine("warning: class label " + weightedLabel + " specified in weight is not found"); else weighted_C[index] *= param.Weights[weightedLabel]; } // train k*(k-1)/2 models bool[] nonzero = new bool[l]; for (i = 0; i < l; i++) nonzero[i] = false; decision_function[] f = new decision_function[nr_class * (nr_class - 1) / 2]; double[] probA = null, probB = null; if (param.Probability) { probA = new double[nr_class * (nr_class - 1) / 2]; probB = new double[nr_class * (nr_class - 1) / 2]; } int p = 0; for (i = 0; i < nr_class; i++) for (int j = i + 1; j < nr_class; j++) { Problem sub_prob = new Problem(); int si = start[i], sj = start[j]; int ci = count[i], cj = count[j]; sub_prob.Count = ci + cj; sub_prob.X = new Node[sub_prob.Count][]; sub_prob.Y = new double[sub_prob.Count]; int k; for (k = 0; k < ci; k++) { sub_prob.X[k] = x[si + k]; sub_prob.Y[k] = +1; } for (k = 0; k < cj; k++) { sub_prob.X[ci + k] = x[sj + k]; sub_prob.Y[ci + k] = -1; } if (param.Probability) { double[] probAB = new double[2]; svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB); probA[p] = probAB[0]; probB[p] = probAB[1]; } f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]); for (k = 0; k < ci; k++) if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0) nonzero[si + k] = true; for (k = 0; k < cj; k++) if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0) nonzero[sj + k] = true; ++p; } // build output model.NumberOfClasses = nr_class; model.ClassLabels = new int[nr_class]; for (i = 0; i < nr_class; i++) model.ClassLabels[i] = label[i]; model.Rho = new double[nr_class * (nr_class - 1) / 2]; for (i = 0; i < nr_class * (nr_class - 1) / 2; i++) model.Rho[i] = f[i].rho; if (param.Probability) { model.PairwiseProbabilityA = new double[nr_class * (nr_class - 1) / 2]; model.PairwiseProbabilityB = new double[nr_class * (nr_class - 1) / 2]; for (i = 0; i < nr_class * (nr_class - 1) / 2; i++) { model.PairwiseProbabilityA[i] = probA[i]; model.PairwiseProbabilityB[i] = probB[i]; } } else { model.PairwiseProbabilityA = null; model.PairwiseProbabilityB = null; } int nnz = 0; int[] nz_count = new int[nr_class]; model.NumberOfSVPerClass = new int[nr_class]; for (i = 0; i < nr_class; i++) { int nSV = 0; for (int j = 0; j < count[i]; j++) if (nonzero[start[i] + j]) { ++nSV; ++nnz; } model.NumberOfSVPerClass[i] = nSV; nz_count[i] = nSV; } Procedures.info("Total nSV = " + nnz + "\n"); model.SupportVectorCount = nnz; model.SupportVectors = new Node[nnz][]; p = 0; for (i = 0; i < l; i++) if (nonzero[i]) model.SupportVectors[p++] = x[i]; int[] nz_start = new int[nr_class]; nz_start[0] = 0; for (i = 1; i < nr_class; i++) nz_start[i] = nz_start[i - 1] + nz_count[i - 1]; model.SupportVectorCoefficients = new double[nr_class - 1][]; for (i = 0; i < nr_class - 1; i++) model.SupportVectorCoefficients[i] = new double[nnz]; p = 0; for (i = 0; i < nr_class; i++) for (int j = i + 1; j < nr_class; j++) { // classifier (i,j): coefficients with // i are in sv_coef[j-1][nz_start[i]...], // j are in sv_coef[i][nz_start[j]...] int si = start[i]; int sj = start[j]; int ci = count[i]; int cj = count[j]; int q = nz_start[i]; int k; for (k = 0; k < ci; k++) if (nonzero[si + k]) model.SupportVectorCoefficients[j - 1][q++] = f[p].alpha[k]; q = nz_start[j]; for (k = 0; k < cj; k++) if (nonzero[sj + k]) model.SupportVectorCoefficients[i][q++] = f[p].alpha[ci + k]; ++p; } } return model; }
// // Interface functions // public static svm_model svm_train(svm_problem prob, svm_parameter param) { svm_model model = new svm_model(); model.param = param; if (param.svm_type == svm_parameter.ONE_CLASS || param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR) { // regression or one-class-svm model.nr_class = 2; model.label = null; model.nSV = null; model.probA = null; model.probB = null; model.sv_coef = new double[1][]; if (param.probability == 1 && (param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR)) { model.probA = new double[1]; model.probA[0] = svm_svr_probability(prob, param); } decision_function f = svm_train_one(prob, param, 0, 0); model.rho = new double[1]; model.rho[0] = f.rho; int nSV = 0; int i; for (i = 0; i < prob.l; i++) if (Math.Abs(f.alpha[i]) > 0) ++nSV; model.l = nSV; model.SV = new svm_node[nSV][]; model.sv_coef[0] = new double[nSV]; int j = 0; for (i = 0; i < prob.l; i++) if (Math.Abs(f.alpha[i]) > 0) { model.SV[j] = prob.x[i]; model.sv_coef[0][j] = f.alpha[i]; ++j; } } else { // classification int l = prob.l; int[] tmp_nr_class = new int[1]; int[][] tmp_label = new int[1][]; int[][] tmp_start = new int[1][]; int[][] tmp_count = new int[1][]; int[] perm = new int[l]; // group training data of the same class svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm); int nr_class = tmp_nr_class[0]; int[] label = tmp_label[0]; int[] start = tmp_start[0]; int[] count = tmp_count[0]; if (nr_class == 1) svm.info("WARNING: training data in only one class. See README for details." + Environment.NewLine); svm_node[][] x = new svm_node[l][]; int i; for (i = 0; i < l; i++) x[i] = prob.x[perm[i]]; // calculate weighted C double[] weighted_C = new double[nr_class]; for (i = 0; i < nr_class; i++) weighted_C[i] = param.C; for (i = 0; i < param.nr_weight; i++) { int j; for (j = 0; j < nr_class; j++) if (param.weight_label[i] == label[j]) break; if (j == nr_class) Console.Error.WriteLine("WARNING: class label " + param.weight_label[i] + " specified in weight is not found"); else weighted_C[j] *= param.weight[i]; } // train k*(k-1)/2 models bool[] nonzero = new bool[l]; for (i = 0; i < l; i++) nonzero[i] = false; decision_function[] f = new decision_function[nr_class * (nr_class - 1) / 2]; double[] probA = null, probB = null; if (param.probability == 1) { probA = new double[nr_class * (nr_class - 1) / 2]; probB = new double[nr_class * (nr_class - 1) / 2]; } int p = 0; for (i = 0; i < nr_class; i++) for (int j = i + 1; j < nr_class; j++) { svm_problem sub_prob = new svm_problem(); int si = start[i], sj = start[j]; int ci = count[i], cj = count[j]; sub_prob.l = ci + cj; sub_prob.x = new svm_node[sub_prob.l][]; sub_prob.y = new double[sub_prob.l]; int k; for (k = 0; k < ci; k++) { sub_prob.x[k] = x[si + k]; sub_prob.y[k] = +1; } for (k = 0; k < cj; k++) { sub_prob.x[ci + k] = x[sj + k]; sub_prob.y[ci + k] = -1; } if (param.probability == 1) { double[] probAB = new double[2]; svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB); probA[p] = probAB[0]; probB[p] = probAB[1]; } f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]); for (k = 0; k < ci; k++) if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0) nonzero[si + k] = true; for (k = 0; k < cj; k++) if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0) nonzero[sj + k] = true; ++p; } // build output model.nr_class = nr_class; model.label = new int[nr_class]; for (i = 0; i < nr_class; i++) model.label[i] = label[i]; model.rho = new double[nr_class * (nr_class - 1) / 2]; for (i = 0; i < nr_class * (nr_class - 1) / 2; i++) model.rho[i] = f[i].rho; if (param.probability == 1) { model.probA = new double[nr_class * (nr_class - 1) / 2]; model.probB = new double[nr_class * (nr_class - 1) / 2]; for (i = 0; i < nr_class * (nr_class - 1) / 2; i++) { model.probA[i] = probA[i]; model.probB[i] = probB[i]; } } else { model.probA = null; model.probB = null; } int nnz = 0; int[] nz_count = new int[nr_class]; model.nSV = new int[nr_class]; for (i = 0; i < nr_class; i++) { int nSV = 0; for (int j = 0; j < count[i]; j++) if (nonzero[start[i] + j]) { ++nSV; ++nnz; } model.nSV[i] = nSV; nz_count[i] = nSV; } svm.info("Total nSV = " + nnz + Environment.NewLine); model.l = nnz; model.SV = new svm_node[nnz][]; p = 0; for (i = 0; i < l; i++) if (nonzero[i]) model.SV[p++] = x[i]; int[] nz_start = new int[nr_class]; nz_start[0] = 0; for (i = 1; i < nr_class; i++) nz_start[i] = nz_start[i - 1] + nz_count[i - 1]; model.sv_coef = new double[nr_class - 1][]; for (i = 0; i < nr_class - 1; i++) model.sv_coef[i] = new double[nnz]; p = 0; for (i = 0; i < nr_class; i++) for (int j = i + 1; j < nr_class; j++) { // classifier (i,j): coefficients with // i are in sv_coef[j-1][nz_start[i]...], // j are in sv_coef[i][nz_start[j]...] int si = start[i]; int sj = start[j]; int ci = count[i]; int cj = count[j]; int q = nz_start[i]; int k; for (k = 0; k < ci; k++) if (nonzero[si + k]) model.sv_coef[j - 1][q++] = f[p].alpha[k]; q = nz_start[j]; for (k = 0; k < cj; k++) if (nonzero[sj + k]) model.sv_coef[i][q++] = f[p].alpha[ci + k]; ++p; } } return model; }