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Implemented Perceptron and made a machine_learning folder #187

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Implemented Perceptron and made a machine_learning folder #187

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111 changes: 111 additions & 0 deletions machine_learning/Perceptron.java
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//this is an implementation of the perceptron learning algorithim
//It is one neuron in a neural network
//@author: rinaldaj

import java.util.Random;

public class Perceptron {
double[] w;


public Perceptron(int size){
//This initializes w to random values
//size is the size of a challenge vector
w = new double[size];
Random rand = new Random();
for( int i = 0; i< size; i++){
w[i] = rand.nextDouble();
}
}
public void train(double[][] challenge, boolean[] good){
//this trains the system
boolean right = false;
int counting = 0;
while(!right){
//System.out.println("This is the " + counting +" of the loop");
counting++;
right = true;
for(int j = 0; j < challenge.length;j++){
double[] x = challenge[j];
double dotProduct = 0;
for(int i = 0; i < w.length;i++){
dotProduct+= w[i] * x[i];
}
int yFound;
if(dotProduct < 0){
yFound = -1;
}else{
yFound = 1;
}
int yReal;
if(good[j]){
yReal = 1;
}else{
yReal = -1;
}
if(yReal != yFound){
right = false;
//System.out.println("Fail occured in " + counting+ " loop");
for(int k = 0; k< w.length; k++){
w[k] = w[k] + yReal * x[k];
}
}

}
}
}
public boolean test(double[] inputVector){
//Takes the vector and outputs whether it is accepted or not
double dotProduct = 0;
for(int i = 0; i < w.length;i++){
dotProduct+= w[i] * inputVector[i];
}
return dotProduct >= 0;
}

public void printW(){
for(double nes:w)
System.out.print(nes + " ");
System.out.println("");
}

public static double[] genTrainSet(int input,int size){
//This is a superflourous function that is used to generate training vectors
//size should be the length of w (can't use w.length because it may not have been initialized)
double[] ret = new double[size];
int step = input;
for(int i = 0;i<size;i++){
if(step % 2 == 0){
step = step /2;
} else {
step = step * 3 + 1;
}
ret[i] = step;
}
return ret;
}
public static boolean goodVector(double[] input){
//this is a superflourous function that is used to check if the input vector is good
double dotProduct = 0;
for(int i = 0; i < input.length;i++){
dotProduct+= (i*1/2) * Math.pow(-1,i) * input[i];
}
return dotProduct >= 0;

}
public static void main(String [] args){
//This generates a training set based on the goodVector function
int size = 4;
int sampSize = 1000;
double[][] trainSet = new double[sampSize][];
boolean[] good = new boolean[sampSize];
for(int i = 0; i<sampSize;i++){
trainSet[i] = Perceptron.genTrainSet(i,size);
good[i] = Perceptron.goodVector(trainSet[i]);
}
Perceptron neuron = new Perceptron(size);
neuron.train(trainSet,good);
//neuron.printW();

}
}