import libsvm.*;import java.io.*;import java.util.*;class svm_predict {	private static double atof(String s)	{		return Double.valueOf(s).doubleValue();	}	private static int atoi(String s)	{		return Integer.parseInt(s);	}	private static void predict(BufferedReader input, DataOutputStream output, svm_model model, int predict_probability) throws IOException	{		int correct = 0;		int total = 0;		double error = 0;		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;		int svm_type=svm.svm_get_svm_type(model);		int nr_class=svm.svm_get_nr_class(model);		int[] labels=new int[nr_class];		double[] prob_estimates=null;		if(predict_probability == 1)		{			if(svm_type == svm_parameter.EPSILON_SVR ||			   svm_type == svm_parameter.NU_SVR)			{				System.out.print("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+svm.svm_get_svr_probability(model)+"\n");			}			else			{				svm.svm_get_labels(model,labels);				prob_estimates = new double[nr_class];				output.writeBytes("labels");				for(int j=0;j<nr_class;j++)					output.writeBytes(" "+labels[j]);				output.writeBytes("\n");			}		}		while(true)		{			String line = input.readLine();			if(line == null) break;			StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");			double target = 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());			}			double v;			if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC))			{				v = svm.svm_predict_probability(model,x,prob_estimates);				output.writeBytes(v+" ");				for(int j=0;j<nr_class;j++)					output.writeBytes(prob_estimates[j]+" ");				output.writeBytes("\n");			}			else			{				v = svm.svm_predict(model,x);				output.writeBytes(v+"\n");			}			if(v == target)				++correct;			error += (v-target)*(v-target);			sumv += v;			sumy += target;			sumvv += v*v;			sumyy += target*target;			sumvy += v*target;			++total;		}		System.out.print("Accuracy = "+(double)correct/total*100+				 "% ("+correct+"/"+total+") (classification)\n");		System.out.print("Mean squared error = "+error/total+" (regression)\n");		System.out.print("Squared correlation coefficient = "+			((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/			((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))+" (regression)\n"			);	}	private static void exit_with_help()	{		System.err.print("usage: svm_predict [options] test_file model_file output_file\n"	        +"options:\n"                +"-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n");		System.exit(1);	}	public static void main(String argv[]) throws IOException	{		int i, predict_probability=0;		// parse options		for(i=0;i<argv.length;i++)		{			if(argv[i].charAt(0) != '-') break;			++i;			switch(argv[i-1].charAt(1))			{			        case 'b':					predict_probability = atoi(argv[i]);					break;				default:					System.err.print("unknown option\n");					exit_with_help();			}		}		if(i>=argv.length)			exit_with_help();		try 		{			BufferedReader input = new BufferedReader(new FileReader(argv[i]));			DataOutputStream output = new DataOutputStream(new FileOutputStream(argv[i+2]));			svm_model model = svm.svm_load_model(argv[i+1]);			predict(input,output,model,predict_probability);		} 		catch(FileNotFoundException e) 		{			exit_with_help();		}		catch(ArrayIndexOutOfBoundsException e) 		{			exit_with_help();		}	}}