Thread [main] (Suspended)	
	ClassNotFoundException(Throwable).<init>(String, Throwable) line: 286	
	ClassNotFoundException(Exception).<init>(String, Throwable) line: not available	
	ClassNotFoundException(ReflectiveOperationException).<init>(String, Throwable) line: not available	
	ClassNotFoundException.<init>(String) line: not available	
	URLClassLoader$1.run() line: not available	
	URLClassLoader$1.run() line: not available	
	AccessController.doPrivileged(PrivilegedExceptionAction<T>, AccessControlContext) line: not available [native method]	
	Launcher$ExtClassLoader(URLClassLoader).findClass(String) line: not available	
	Launcher$ExtClassLoader(ClassLoader).loadClass(String, boolean) line: not available	
	Launcher$AppClassLoader(ClassLoader).loadClass(String, boolean) line: not available	
	Launcher$AppClassLoader.loadClass(String, boolean) line: not available	
	Launcher$AppClassLoader(ClassLoader).loadClass(String) line: not available	
	driver.main(String[]) line: 12	
C:\Program Files\Java\jre7\bin\javaw.exe (Aug 1, 2011 2:23:51 AM)

import java.io.BufferedReader;
import java.io.DataInputStream;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.StringTokenizer;

public class driver {

	public static void main(String[] args) throws IOException {
		double oldError;
		NeuralNetwork myNet = new NeuralNetwork("C:\\Users\\Owner\\Documents\\school\\BU\\big data\\independent study\\class24_set1.txt");
		myNet.trainEpoch();
		/*train the network*/
		do {
			oldError = myNet.getTotalError();
			myNet.trainEpoch();
		} while(myNet.getTotalError() < oldError);
		
		/*get test data*/
		int numEx = myNet.getNumExamples();	//assuming both sets have the same number of examples
		int numIn = myNet.getNumInputs();
		int numOut = myNet.getNumOutputs();
		double[][] data = new double[numEx][numIn];
		FileInputStream fstream;
		fstream = new FileInputStream("C:\\Users\\Owner\\Documents\\school\\BU\\big data\\independent study\\class24_set2.txt");
		DataInputStream in = new DataInputStream(fstream);
		BufferedReader br = new BufferedReader(new InputStreamReader(in));
		String line;
		int i = -1;
		int j;
		while((line = br.readLine()) != null) {
			j = 0;
			i++;
			data[i][j] = 1;	//bias effect
			StringTokenizer st = new StringTokenizer(line, ",");
			while(st.hasMoreTokens()) {
				j++;
				switch(j) {
				case 1:
				case 2:
					data[i][j] = 0.8*(Double.parseDouble(st.nextToken())/3)+0.1;
					break;
				case 4:
				case 5:
					data[i][j-1] = 0.8*(Double.parseDouble(st.nextToken())/3)+0.1;
					break;
				case 14:
					data[i][5] = 0.8*(Double.parseDouble(st.nextToken())/3)+0.1;
				}
			}
		}
		in.close();
		
		/*generate class output*/
		int r, k;
		int numHid = myNet.getNumHidden();
		double sum = 0;
		double[][] hidden = new double[numEx][numHid+1];
		double[][] output = new double[numEx][numOut];
		for(r = 0; r < numEx; r++) {
			hidden[r][0] = 1;
			for(k = 0; k <= numHid; k++) { //input layer to hidden layer
				sum = 0;
				for(i = 0; i < numIn; i++)
					sum = sum + myNet.getWeight1(k,i)*data[r][i];
				hidden[r][k] = 1 / (1 + Math.exp(-1*sum));
			}
			sum = 0;								//hidden layer to output layer
			for(j = 0; j < numOut; j++) {
				for(k = 0; k <= numHid; k++)
					sum = sum + myNet.getWeight2(j,k)*hidden[r][k];
				output[r][j] = 1 / (1 + Math.exp(-1*sum));
			}
		}
		for(r = 0; r < numEx; r++) {
			if(output[r][0] > output[r][1])
				System.out.println("2");
			else
				System.out.println("4");
		}
	}
}