ext/libsvm/svm.cpp in rb-libsvm-1.0.8 vs ext/libsvm/svm.cpp in rb-libsvm-1.0.9

@@ -735,11 +735,11 @@
 			// reconstruct the whole gradient to calculate objective value
 			reconstruct_gradient();
 			active_size = l;
 			info("*");
 		}
-		info("\nWARNING: reaching max number of iterations");
+		fprintf(stderr,"\nWARNING: reaching max number of iterations\n");
 	}
 
 	// calculate rho
 
 	si->rho = calculate_rho();
@@ -1004,11 +1004,11 @@
 //
 // Solver for nu-svm classification and regression
 //
 // additional constraint: e^T \alpha = constant
 //
-class Solver_NU : public Solver
+class Solver_NU: public Solver
 {
 public:
 	Solver_NU() {}
 	void Solve(int l, const QMatrix& Q, const double *p, const schar *y,
 		   double *alpha, double Cp, double Cn, double eps,
@@ -2105,16 +2105,18 @@
 		for(i=0;i<prob->l;i++)
 			if(fabs(f.alpha[i]) > 0) ++nSV;
 		model->l = nSV;
 		model->SV = Malloc(svm_node *,nSV);
 		model->sv_coef[0] = Malloc(double,nSV);
+		model->sv_indices = Malloc(int,nSV);
 		int j = 0;
 		for(i=0;i<prob->l;i++)
 			if(fabs(f.alpha[i]) > 0)
 			{
 				model->SV[j] = prob->x[i];
 				model->sv_coef[0][j] = f.alpha[i];
+				model->sv_indices[j] = i+1;
 				++j;
 			}		
 
 		free(f.alpha);
 	}
@@ -2252,13 +2254,18 @@
 		
 		info("Total nSV = %d\n",total_sv);
 
 		model->l = total_sv;
 		model->SV = Malloc(svm_node *,total_sv);
+		model->sv_indices = Malloc(int,total_sv);
 		p = 0;
 		for(i=0;i<l;i++)
-			if(nonzero[i]) model->SV[p++] = x[i];
+			if(nonzero[i])
+			{
+				model->SV[p] = x[i];
+				model->sv_indices[p++] = perm[i] + 1;
+			}
 
 		int *nz_start = Malloc(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];
@@ -2440,9 +2447,21 @@
 void svm_get_labels(const svm_model *model, int* label)
 {
 	if (model->label != NULL)
 		for(int i=0;i<model->nr_class;i++)
 			label[i] = model->label[i];
+}
+
+void svm_get_sv_indices(const svm_model *model, int* indices)
+{
+	if (model->sv_indices != NULL)
+		for(int i=0;i<model->l;i++)
+			indices[i] = model->sv_indices[i];
+}
+
+int svm_get_nr_sv(const svm_model *model)
+{
+	return model->l;
 }
 
 double svm_get_svr_probability(const svm_model *model)
 {
 	if ((model->param.svm_type == EPSILON_SVR || model->param.svm_type == NU_SVR) &&