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];
			model.sv_indices = new int[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];
					model.sv_indices[j] = i+1;
					++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.\n");
			
			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)
					System.err.print("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

			boolean[] nonzero = new boolean[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+"\n");

			model.l = nnz;
			model.SV = new svm_node[nnz][];
			model.sv_indices = new int[nnz];
			p = 0;
			for(i=0;i<l;i++)
				if(nonzero[i])
				{
					model.SV[p] = x[i];
					model.sv_indices[p++] = perm[i] + 1;
				}

			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;
	}