www.pudn.com > javasvm.rar > svm_predict.java

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=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]);
			if(predict_probability == 1)
				if(svm.svm_check_probability_model(model)==0)
				{
					System.err.print("Model does not support probabiliy estimates\n");
					System.exit(1);
				}
			predict(input,output,model,predict_probability);
			input.close();
			output.close();
		} 
		catch(FileNotFoundException e) 
		{
			exit_with_help();
		}
		catch(ArrayIndexOutOfBoundsException e) 
		{
			exit_with_help();
		}
	}
}