Constants.java

package neuralnetwork;

public class Constants {

	private Constants(){
		
	}
	
	public static final int NUM_OF_FEATURES = 2;
	public static final int NUM_OF_DATA = 4;
	public static final double LEARNING_RATE = 0.1;
	public static final int THREASHOLD = 1;
	
}

And.java

package neuralnetwork;

public class And {

	private int[][] trainingData;
	private int[] trainingTargets;
	private double[] weights;
	private double error = 1;
	
	public And(int[][] trainingData, int[] trainingTargets, double[] weights){
		this.trainingData = trainingData;
		this.trainingTargets = trainingTargets;
		this.weights = weights;
	}
	
	public void trainNetwork(){
		
		while( error != 0 ){
			
			for(int rowIndex=0;rowIndex<Constants.NUM_OF_DATA;++rowIndex){
				
				double weightedSum = 0;
				weightedSum = getWeightedSum(rowIndex, weightedSum);
				
				int outputValue = activationFunction(weightedSum);
				
				error = this.trainingTargets[rowIndex] - outputValue;
				
				// update the weights
				for(int j=0;j<this.weights.length;++j){
					this.weights[j] += Constants.LEARNING_RATE * error * trainingData[rowIndex][j];
				}	
			}	
			
			System.out.println("Edge weights are: " + weights[0]+" - " + weights[1]);
		}
		
		System.out.println("Final edge weights are: " + weights[0]+"-"+weights[1]);
	}

	private int activationFunction(double weightedSum) {
		
		if( weightedSum >= Constants.THREASHOLD ){
			return 1;
		}
		
		return 0;
	}

	private double getWeightedSum(int index, double weightedSum) {
		
		for(int j=0;j<Constants.NUM_OF_FEATURES;++j){
			weightedSum = weightedSum + trainingData[index][j] * weights[j];
		}
		
		return weightedSum;
	}
}

App.java

package neuralnetwork;

public class App {

	public static void main(String[] args) {

		double[] weights = { 0 , 0 };
		int[][] trainingData = { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } };
		int[] trainingTargets = { 0, 0, 0, 1 };
		
		And problem = new And(trainingData, trainingTargets, weights);
		problem.trainNetwork();
		
	}
}

Output of the program is :

Edge weights are: 0.1 - 0.1
Edge weights are: 0.2 - 0.2
Edge weights are: 0.30000000000000004 - 0.30000000000000004
Edge weights are: 0.4 - 0.4
Edge weights are: 0.5 - 0.5
Edge weights are: 0.5 - 0.5
Final edge weights are: 0.5-0.5

You can download the complete source code here : Source Code

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