code: import libsvm.*;
  import java.io.*;
  import java.util.*;
  
  class svm_train {
  	private svm_parameter param;		// set by parse_command_line
  	private svm_problem prob;		// set by read_problem
  	private svm_model model;
  	private String input_file_name;		// set by parse_command_line
  	private String model_file_name;		// set by parse_command_line
  	private String error_msg;
  	private int cross_validation;
  	private int nr_fold;
  
  	private static svm_print_interface svm_print_null = new svm_print_interface()
  	{
  		public void print(String s) {}
  	};
  
  	private static void exit_with_help()
  	{
  		System.out.print(
  		 "Usage: svm_train [options] training_set_file [model_file]\n"
  		+"options:\n"
  		+"-s svm_type : set type of SVM (default 0)\n"
  		+"	0 -- C-SVC		(multi-class classification)\n"
  		+"	1 -- nu-SVC		(multi-class classification)\n"
  		+"	2 -- one-class SVM\n"
  		+"	3 -- epsilon-SVR	(regression)\n"
  		+"	4 -- nu-SVR		(regression)\n"
  		+"-t kernel_type : set type of kernel function (default 2)\n"
  		+"	0 -- linear: u'*v\n"
  		+"	1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
  		+"	2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
  		+"	3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
  		+"	4 -- precomputed kernel (kernel values in training_set_file)\n"
  		+"-d degree : set degree in kernel function (default 3)\n"
  		+"-g gamma : set gamma in kernel function (default 1/num_features)\n"
  		+"-r coef0 : set coef0 in kernel function (default 0)\n"
  		+"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
  		+"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
  		+"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
  		+"-m cachesize : set cache memory size in MB (default 100)\n"
  		+"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
  		+"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
  		+"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
  		+"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
  		+"-v n : n-fold cross validation mode\n"
  		+"-q : quiet mode (no outputs)\n"
  		);
  		System.exit(1);
  	}
  
  	private void do_cross_validation()
  	{
  		int i;
  		int total_correct = 0;
  		double total_error = 0;
  		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
  		double[] target = new double[prob.l];
  
  		svm.svm_cross_validation(prob,param,nr_fold,target);
  		if(param.svm_type == svm_parameter.EPSILON_SVR ||
  		   param.svm_type == svm_parameter.NU_SVR)
  		{
  			for(i=0;i<prob.l;i++)
  			{
  				double y = prob.y[i];
  				double v = target[i];
  				total_error += (v-y)*(v-y);
  				sumv += v;
  				sumy += y;
  				sumvv += v*v;
  				sumyy += y*y;
  				sumvy += v*y;
  			}
  			System.out.print("Cross Validation Mean squared error = "+total_error/prob.l+"\n");
  			System.out.print("Cross Validation Squared correlation coefficient = "+
  				((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
  				((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))+"\n"
  				);
  		}
  		else
  		{
  			for(i=0;i<prob.l;i++)
  				if(target[i] == prob.y[i])
  					++total_correct;
  			System.out.print("Cross Validation Accuracy = "+100.0*total_correct/prob.l+"%\n");
  		}
  	}
  	
  	private void run(String argv[]) throws IOException
  	{
  		parse_command_line(argv);
  		read_problem();
  		error_msg = svm.svm_check_parameter(prob,param);
  
  		if(error_msg != null)
  		{
  			System.err.print("ERROR: "+error_msg+"\n");
  			System.exit(1);
  		}
  
  		if(cross_validation != 0)
  		{
  			do_cross_validation();
  		}
  		else
  		{
  			model = svm.svm_train(prob,param);
  			svm.svm_save_model(model_file_name,model);
  		}
  	}
  
  	public static void main(String argv[]) throws IOException
  	{
  		svm_train t = new svm_train();
  		t.run(argv);
  	}
  
  	private static double atof(String s)
  	{
  		double d = Double.valueOf(s).doubleValue();
  		if (Double.isNaN(d) || Double.isInfinite(d))
  		{
  			System.err.print("NaN or Infinity in input\n");
  			System.exit(1);
  		}
  		return(d);
  	}
  
  	private static int atoi(String s)
  	{
  		return Integer.parseInt(s);
  	}
  
  	private void parse_command_line(String argv[])
  	{
  		int i;
  		svm_print_interface print_func = null;	// default printing to stdout
  
  		param = new svm_parameter();
  		// default values
  		param.svm_type = svm_parameter.C_SVC;
  		param.kernel_type = svm_parameter.RBF;
  		param.degree = 3;
  		param.gamma = 0;	// 1/num_features
  		param.coef0 = 0;
  		param.nu = 0.5;
  		param.cache_size = 100;
  		param.C = 1;
  		param.eps = 1e-3;
  		param.p = 0.1;
  		param.shrinking = 1;
  		param.probability = 0;
  		param.nr_weight = 0;
  		param.weight_label = new int[0];
  		param.weight = new double[0];
  		cross_validation = 0;
  
  		// parse options
  		for(i=0;i<argv.length;i++)
  		{
  			if(argv[i].charAt(0) != '-') break;
  			if(++i>=argv.length)
  				exit_with_help();
  			switch(argv[i-1].charAt(1))
  			{
  				case 's':
  					param.svm_type = atoi(argv[i]);
  					break;
  				case 't':
  					param.kernel_type = atoi(argv[i]);
  					break;
  				case 'd':
  					param.degree = atoi(argv[i]);
  					break;
  				case 'g':
  					param.gamma = atof(argv[i]);
  					break;
  				case 'r':
  					param.coef0 = atof(argv[i]);
  					break;
  				case 'n':
  					param.nu = atof(argv[i]);
  					break;
  				case 'm':
  					param.cache_size = atof(argv[i]);
  					break;
  				case 'c':
  					param.C = atof(argv[i]);
  					break;
  				case 'e':
  					param.eps = atof(argv[i]);
  					break;
  				case 'p':
  					param.p = atof(argv[i]);
  					break;
  				case 'h':
  					param.shrinking = atoi(argv[i]);
  					break;
  				case 'b':
  					param.probability = atoi(argv[i]);
  					break;
  				case 'q':
  					print_func = svm_print_null;
  					i--;
  					break;
  				case 'v':
  					cross_validation = 1;
  					nr_fold = atoi(argv[i]);
  					if(nr_fold < 2)
  					{
  						System.err.print("n-fold cross validation: n must >= 2\n");
  						exit_with_help();
  					}
  					break;
  				case 'w':
  					++param.nr_weight;
  					{
  						int[] old = param.weight_label;
  						param.weight_label = new int[param.nr_weight];
  						System.arraycopy(old,0,param.weight_label,0,param.nr_weight-1);
  					}
  
  					{
  						double[] old = param.weight;
  						param.weight = new double[param.nr_weight];
  						System.arraycopy(old,0,param.weight,0,param.nr_weight-1);
  					}
  
  					param.weight_label[param.nr_weight-1] = atoi(argv[i-1].substring(2));
  					param.weight[param.nr_weight-1] = atof(argv[i]);
  					break;
  				default:
  					System.err.print("Unknown option: " + argv[i-1] + "\n");
  					exit_with_help();
  			}
  		}
  
  		svm.svm_set_print_string_function(print_func);
  
  		// determine filenames
  
  		if(i>=argv.length)
  			exit_with_help();
  
  		input_file_name = argv[i];
  
  		if(i<argv.length-1)
  			model_file_name = argv[i+1];
  		else
  		{
  			int p = argv[i].lastIndexOf('/');
  			++p;	// whew...
  			model_file_name = argv[i].substring(p)+".model";
  		}
  	}
  
  	// read in a problem (in svmlight format)
  
  	private void read_problem() throws IOException
  	{
  		BufferedReader fp = new BufferedReader(new FileReader(input_file_name));
  		Vector<Double> vy = new Vector<Double>();
  		Vector<svm_node[]> vx = new Vector<svm_node[]>();
  		int max_index = 0;
  
  		while(true)
  		{
  			String line = fp.readLine();
  			if(line == null) break;
  
  			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
  
  			vy.addElement(atof(st.nextToken()));
  			int m = st.countTokens()/2;
  			svm_node[] x = new svm_node[m];
  			for(int j=0;j<m;j++)
  			{
  				x[j] = new svm_node();
  				x[j].index = atoi(st.nextToken());
  				x[j].value = atof(st.nextToken());
  			}
  			if(m>0) max_index = Math.max(max_index, x[m-1].index);
  			vx.addElement(x);
  		}
  
  		prob = new svm_problem();
  		prob.l = vy.size();
  		prob.x = new svm_node[prob.l][];
  		for(int i=0;i<prob.l;i++)
  			prob.x[i] = vx.elementAt(i);
  		prob.y = new double[prob.l];
  		for(int i=0;i<prob.l;i++)
  			prob.y[i] = vy.elementAt(i);
  
  		if(param.gamma == 0 && max_index > 0)
  			param.gamma = 1.0/max_index;
  
  		if(param.kernel_type == svm_parameter.PRECOMPUTED)
  			for(int i=0;i<prob.l;i++)
  			{
  				if (prob.x[i][0].index != 0)
  				{
  					System.err.print("Wrong kernel matrix: first column must be 0:sample_serial_number\n");
  					System.exit(1);
  				}
  				if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
  				{
  					System.err.print("Wrong input format: sample_serial_number out of range\n");
  					System.exit(1);
  				}
  			}
  
  		fp.close();
  	}
  }
  