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 int cross_validation = 0;	private int nr_fold;	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\n"		+"	1 -- nu-SVC\n"		+"	2 -- one-class SVM\n"		+"	3 -- epsilon-SVR\n"		+"	4 -- nu-SVR\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"		+"-d degree : set degree in kernel function (default 3)\n"		+"-g gamma : set gamma in kernel function (default 1/k)\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 40)\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"		+"-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"		);		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;		// random shuffle		for(i=0;i<prob.l;i++)		{			int j = (int)(Math.random()*(prob.l-i));			svm_node[] tx;			double ty;			tx = prob.x[i];			prob.x[i] = prob.x[j];			prob.x[j] = tx;			ty = prob.y[i];			prob.y[i] = prob.y[j];			prob.y[j] = ty;		}		for(i=0;i<nr_fold;i++)		{			int begin = i*prob.l/nr_fold;			int end = (i+1)*prob.l/nr_fold;			int j,k;			svm_problem subprob = new svm_problem();			subprob.l = prob.l-(end-begin);			subprob.x = new svm_node[subprob.l][];			subprob.y = new double[subprob.l];			k=0;			for(j=0;j<begin;j++)			{				subprob.x[k] = prob.x[j];				subprob.y[k] = prob.y[j];				++k;			}			for(j=end;j<prob.l;j++)			{				subprob.x[k] = prob.x[j];				subprob.y[k] = prob.y[j];				++k;			}			if(param.svm_type == svm_parameter.EPSILON_SVR ||			   param.svm_type == svm_parameter.NU_SVR)			{				svm_model submodel = svm.svm_train(subprob,param);				double error = 0;				for(j=begin;j<end;j++)				{					double v = svm.svm_predict(submodel,prob.x[j]);					double y = prob.y[j];					error += (v-y)*(v-y);					sumv += v;					sumy += y;					sumvv += v*v;					sumyy += y*y;					sumvy += v*y;				}				System.out.print("Mean squared error = "+error/(end-begin)+"\n");				total_error += error;						}			else			{				svm_model submodel = svm.svm_train(subprob,param);				int correct = 0;				for(j=begin;j<end;j++)				{					double v = svm.svm_predict(submodel,prob.x[j]);					if(v == prob.y[j])						++correct;				}				System.out.print("Accuracy = "+100.0*correct/(end-begin)+"% ("+correct+"/"+(end-begin)+")\n");				total_correct += correct;			}		}				if(param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR)		{			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			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();		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)	{		return Double.valueOf(s).doubleValue();	}	private static int atoi(String s)	{		return Integer.parseInt(s);	}	private void parse_command_line(String argv[])	{		int i;		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/k		param.coef0 = 0;		param.nu = 0.5;		param.cache_size = 40;		param.C = 1;		param.eps = 1e-3;		param.p = 0.1;		param.shrinking = 1;		param.nr_weight = 0;		param.weight_label = new int[0];		param.weight = new double[0];		// parse options		for(i=0;i<argv.length;i++)		{			if(argv[i].charAt(0) != '-') break;			++i;			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 = atof(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 '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;				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;				default:					System.err.print("unknown option\n");					exit_with_help();			}		}		// 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 vy = new Vector();		Vector vx = new Vector();		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(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] = (svm_node[])vx.elementAt(i);		prob.y = new double[prob.l];		for(int i=0;i<prob.l;i++)			prob.y[i] = atof((String)vy.elementAt(i));		if(param.gamma == 0)			param.gamma = 1.0/max_index;		fp.close();	}}