From 1bbb6f5200d9b63fa98d7dd17c1e5c8742dc698f Mon Sep 17 00:00:00 2001
From: Kim Feichtinger <KimFeichtinger@Kims-MacBook-Pro.local>
Date: Mon, 5 Mar 2018 21:03:28 +0000
Subject: [PATCH] Removing main from project

* main class was there to test the NN functionality by solving XOR
---
 .gitignore                       |  3 +-
 README.md                        |  2 +-
 src/main/java/Main.java          | 52 --------------------------------
 src/main/java/NeuralNetwork.java | 16 +++++++---
 4 files changed, 15 insertions(+), 58 deletions(-)
 delete mode 100644 src/main/java/Main.java

diff --git a/.gitignore b/.gitignore
index ec376bb..6f68e91 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 .idea
-target
\ No newline at end of file
+target
+out
\ No newline at end of file
diff --git a/README.md b/README.md
index 88c049a..62fcd73 100644
--- a/README.md
+++ b/README.md
@@ -11,4 +11,4 @@ This is a very basic Java Neural Network library using EJML (Efficient Java Matr
 
 ## TODO
 
-- add support for multy-layered Neural Networks
\ No newline at end of file
+- Add support for multy-layered Neural Net works
\ No newline at end of file
diff --git a/src/main/java/Main.java b/src/main/java/Main.java
deleted file mode 100644
index 78b472c..0000000
--- a/src/main/java/Main.java
+++ /dev/null
@@ -1,52 +0,0 @@
-import java.util.Random;
-
-/**
- * Created by KimFeichtinger on 04.03.18.
- */
-public class Main {
-
-    // Example program to test the basic neural network by letting it solve XOR
-    public static void main(String args[]){
-
-        Random r = new Random();
-
-        // Training Data
-        double[][] trainingData = {
-                {0, 0},
-                {0, 1},
-                {1, 0},
-                {1, 1},
-        };
-
-        double[][] trainingDataTargets = {
-                {0},
-                {1},
-                {1},
-                {0},
-        };
-
-        // Testing Data
-        double[][] testingData = {
-                {0, 0},
-                {0, 1},
-                {1, 0},
-                {1, 1},
-        };
-
-        NeuralNetwork nn = new NeuralNetwork(2,2, 1);
-
-        // training
-        for (int i = 0; i < 50000; i++) {
-            // training in random order
-            int random = r.nextInt(4);
-            nn.train(trainingData[random], trainingDataTargets[random]);
-        }
-
-        // testing the nn
-        for (int i = 0; i < testingData.length; i++) {
-            System.out.println("Guess for " + testingData[i][0] + ", " + testingData[i][1] + ": \n" + nn.feedForward(testingData[i]));
-        }
-    }
-
-
-}
diff --git a/src/main/java/NeuralNetwork.java b/src/main/java/NeuralNetwork.java
index f3acbe3..394a409 100644
--- a/src/main/java/NeuralNetwork.java
+++ b/src/main/java/NeuralNetwork.java
@@ -1,7 +1,6 @@
 import org.ejml.simple.SimpleMatrix;
 import utilities.Sigmoid;
 
-import java.util.Arrays;
 import java.util.Random;
 
 /**
@@ -9,10 +8,10 @@
  */
 public class NeuralNetwork {
 
-    private static final double LEARNING_RATE = 0.1;
-
     Random r = new Random();
 
+    private double learningRate = 0.1;
+
     // "size" of the neural network
     private int inputNodes;
     private int hiddenNodes;
@@ -93,7 +92,7 @@ private SimpleMatrix calculateLayer(SimpleMatrix weights, SimpleMatrix bias, Sim
     private SimpleMatrix calculateGradient(SimpleMatrix layer, SimpleMatrix error){
         SimpleMatrix gradient = Sigmoid.applySigmoid(layer, true);
         gradient = gradient.elementMult(error);
-        return gradient.scale(LEARNING_RATE);
+        return gradient.scale(learningRate);
     }
 
     private SimpleMatrix calculateDeltas(SimpleMatrix gradient, SimpleMatrix layer){
@@ -104,4 +103,13 @@ private SimpleMatrix arrayToMatrix(double[] i){
         double[][] input = {i};
         return new SimpleMatrix(input).transpose();
     }
+
+    public double getLearningRate() {
+        return learningRate;
+    }
+
+    public void setLearningRate(double learningRate) {
+        this.learningRate = learningRate;
+    }
+
 }