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) throws IOException	{		int correct = 0;		int total = 0;		double error = 0;		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;		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 = svm.svm_predict(model,x);			if(v == target)				++correct;			error += (v-target)*(v-target);			sumv += v;			sumy += target;			sumvv += v*v;			sumyy += target*target;			sumvy += v*target;			++total;			output.writeBytes(v+"\n");		}		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"			);	}	public static void main(String argv[]) throws IOException	{		if(argv.length != 3)		{			System.err.print("usage: svm-predict test_file model_file output_file\n");			System.exit(1);		}		BufferedReader input = new BufferedReader(new FileReader(argv[0]));		DataOutputStream output = new DataOutputStream(new FileOutputStream(argv[2]));		svm_model model = svm.svm_load_model(argv[1]);		predict(input,output,model);	}}