Perceptron Learning Algorithm : Java Implementation

Tutorials: 

1) https://www.youtube.com/watch?v=1XkjVl-j8MM

2) https://www.youtube.com/watch?v=_tMpeah8XGQ

3) https://www.youtube.com/watch?v=4aksMtJHWEQ

Perceptron Learning Algorithm:

• Initialize the weights.
• Pick a learning rate m.
• Do the following until stopping condition is satisfied:
•     For each training instance (example)
•     Calculate localError =  true (known) output - given output
•     adjust weights[] = weights[] + LEARNING_RATE*localError*x[i];
•     adjust weights[] = weights[] + LEARNING_RATE*localError*y[i];
•     update bias (weight[2] = weight[2] + LEARNING_RATE*localError;)
•     update globalError = globalError + localError*localError; (squared error).
• The decision boundary equation is given by weights[0]*x + weights[1]*y + weights[2] = 0;
• Test the adjusted variables on randomly generated inputs.

Java Implementation (Credits : Dr. Noureddin Sadawi) :

public class CaltechEx1 {
    static int MAX_ITER = 100;
    static double LEARNING_RATE = 0.001;
    static int NUM_INSTANCES = 100;
    static int theta = 0;
    
    public static void main(String[] args){
        //three variables (features)
        double[] x = new double[NUM_INSTANCES];
        double[] y = new double[NUM_INSTANCES]; 
        int[] outputs = new int[NUM_INSTANCES];
        
        //fifty random points of class 1
        for(int i=0;i<NUM_INSTANCES/2;++i){
            x[i] = randomNumber(-1, 0);
            y[i] = randomNumber(-1, 0);
            outputs[i] = 1;
            System.out.println(x[i]+"\t"+y[i]+"\t"+outputs[i]);
        }
        
        //fifty random points of class 0
        for(int i=50;i<NUM_INSTANCES;i++){
            x[i] = randomNumber(0.01, 1);
            y[i] = randomNumber(0.01, 1);
            outputs[i] = 0;
            System.out.println(x[i]+"\t"+y[i]+"\t"+outputs[i]);
        }
        
        double[] weights = new double[3]; //2 for input variables and one for bias
        double localError, globalError;
        int p, iteration, output;
        
        weights[0] = randomNumber(0, 1);
        weights[1] = randomNumber(0, 1);
        weights[2] = randomNumber(0, 1); 
        
        
        iteration = 0;
        do{
            iteration++;
            globalError = 0;
            //loop through all instances (complete one epoch)
            for(p = 0;p<NUM_INSTANCES;p++){
                //calculate predicted class
                output = calculateOutput(theta, weights, x[p], y[p]);
                //difference between predicted and actual class values
                localError = outputs[p] - output; 
                //update weights
                weights[0] += LEARNING_RATE*localError*x[p];
                weights[1] += LEARNING_RATE*localError*y[p];
                
                //update bias
                weights[2] += LEARNING_RATE*localError;
                
                globalError += (localError*localError);  
            }
            /*Root Mean Squared Error*/
            System.out.println("Iteration "+iteration+" : RMSE = "+Math.sqrt(globalError/NUM_INSTANCES));
        }while(globalError != 0 && iteration < MAX_ITER);
        
        System.out.println("\n========\nDecision boundary equation:");
        System.out.println(weights[0]+"*x "+weights[1]+"*y + "+weights[2]+" = 0");
        
        
        //generate 10 new random pointns and check their classes
        //notice the range of -1 and 1 means the new point could be of class 1 or 0
        //-10 to 10 covers all the ranges we used in generating the 50 classes of 1s and 0s
        
        for(int j=0;j<100;++j){
            double x1 = randomNumber(-1, 1);
            double y1 = randomNumber(-1, 1);
            
            output = calculateOutput(theta, weights, x1, y1);
            System.out.println("\n=======\nNew Random Point:");
            System.out.println("x = "+x1+",y = "+y1);
            System.out.println("class = "+output);
        }
        
        double avg1 = 0, avg2 = 0;
        for(int j=0;j<10;++j){
        
            int mis=0;

            //P[f(x)!=g(x) for N = 10
            //fifty random points of class 1
            for(int i=0;i<5;++i){
                double x1 = randomNumber(-1, 0);
                double y1 = randomNumber(-1, 0);
                output = calculateOutput(theta, weights, x1, y1);
                if(output != 1)mis++; 
            }

            //fifty random points of class 0
            for(int i=0;i<5;i++){
                double x1 = randomNumber(0.01, 1);
                double y1 = randomNumber(0.01, 1);
                output = calculateOutput(theta, weights, x1, y1);
                if(output != 0)mis++; 
            }
            avg1+=(double)mis/10;
//            System.out.println("P[f(x)!=g(x) (N=10) = "+((double)mis/10));

            
            mis = 0;
            //P[f(x)!=g(x) for N = 100
            //fifty random points of class 1
            for(int i=0;i<50;++i){
                double x1 = randomNumber(-1, 0);
                double y1 = randomNumber(-1, 0);
                output = calculateOutput(theta, weights, x1, y1);
                if(output != 1)mis++; 
            }

            //fifty random points of class 0
            for(int i=0;i<50;i++){
                double x1 = randomNumber(0.1, 1);
                double y1 = randomNumber(0.1, 1);
                output = calculateOutput(theta, weights, x1, y1);
                if(output != 0)mis++; 
            }

            avg2+=(double)mis/100;
        }
        System.out.println("P[f(x)!=g(x) (N=10) over 10*10 samples = "+avg1/10);
        System.out.println("P[f(x)!=g(x) (N=100) over 10*100 samples = "+avg2/10);
    }
    
    /**
     * returns a random double value within a given range
     * @param min the minimum value of the required range(int)
     * @param max the maximum value of the required range(int)
     * @return a random double value between min and max
     */
    public static double randomNumber(double min, double max){
        double d = min+Math.random()*(max-min);
        return d;
    }
    
    /**
     * returns either 1 or 0 using a threshold function 
     * @param theta an integer value for the threshold
     * @param weights the array of weights
     * @param x the x input value
     * @param y the y input value
     * @param z the z input value
     * @return 1 or 0
     */
    static int calculateOutput(int theta, double weights[], double x, double y){
        double sum  = x*weights[0] + y*weights[1] + weights[2];
        return sum>=theta ? 1:0;
    }
}