diff --git a/machine_learning/Perceptron.java b/machine_learning/Perceptron.java
new file mode 100644
index 000000000000..bc8089cf1961
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+++ b/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();
+
+	}
+}