Added interface to load data

Neural_Network/Main.java

@@ -0,0 +1,16 @@
+class Main {
+    public static void main(String [] args) {        
+        NeuralNetwork nn = new NeuralNetwork(2, 10, 1);
+
+        double[][] training_data_in = nn.getFileData("training_data_in.txt");
+        double[][] training_data_out = nn.getFileData("training_data_out.txt");
+
+        double[][] testing_data_in = nn.getFileData("testing_data_in.txt");
+        double[][] testing_data_out = nn.getFileData("testing_data_out.txt");
+
+        int epochs = 50000;
+        nn.fit(training_data_in,training_data_out,epochs,0);
+
+        nn.testData(training_data_in,training_data_out);
+    }
+}

NeuralNetwork.java → Neural_Network/NeuralNetwork.java

@@ -1,9 +1,14 @@
-
+import java.io.FileNotFoundException;
 import java.util.ArrayList;
+import java.io.PrintWriter;
+import java.util.Scanner;
+import java.util.Arrays;
 import java.util.List;
+import java.io.File;
 
 public class NeuralNetwork {
-
+    private static BasicUtils U = new BasicUtils();
+
     Matrix weights_ih, weights_ho, bias_h, bias_o;
     double l_rate = 0.01;
     boolean useMultiThreading = false;
@@ -14,7 +19,6 @@ public NeuralNetwork(int i, int h, int o) {
 
         bias_h = new Matrix(h, 1);
         bias_o = new Matrix(o, 1);
-
     }
 
     public NeuralNetwork(int i, int h, int o, boolean useMultiThreading) {
@@ -25,7 +29,6 @@ public NeuralNetwork(int i, int h, int o, boolean useMultiThreading) {
         bias_o = new Matrix(o, 1);
 
         this.useMultiThreading = useMultiThreading;
-
     }
 
     public NeuralNetwork(int i, int h, int o, double l_rate) {
@@ -36,7 +39,6 @@ public NeuralNetwork(int i, int h, int o, double l_rate) {
         bias_o = new Matrix(o, 1);
 
         this.l_rate = l_rate;
-
     }
 
     public NeuralNetwork(int i, int h, int o, double l_rate, boolean useMultiThreading) {
@@ -48,7 +50,6 @@ public NeuralNetwork(int i, int h, int o, double l_rate, boolean useMultiThreadi
 
         this.l_rate = l_rate;
         this.useMultiThreading = useMultiThreading;
-
     }
 
     public List<Double> predict(double[] X) {
@@ -75,35 +76,34 @@ public void fit(double[][] X, double[][] Y, int epochs, int verbose) {
         switch (verbose) {
 
             case 0: {
-                System.out.println("Staring training with " + epochs + " epochs");
+                U.println("Staring training with " + epochs + " epochs");
                 long start = System.currentTimeMillis();
                 for (int i = 0; i < epochs; i++) {
                     int sampleN = (int) (Math.random() * X.length);
                     this.train(X[sampleN], Y[sampleN], i + 1 == epochs);
                 }
                 long end = System.currentTimeMillis();
                 long elapsedTime = end - start;
-                System.out.println("Training took : " + (elapsedTime / 1000) + "s");
+                U.println("Training took : " + (elapsedTime / 1000) + "s\n");
 
                 break;
             }
 
             case 1: {
-                System.out.println("Staring training with " + epochs + " epochs");
+                U.println("Staring training with " + epochs + " epochs");
                 long start = System.currentTimeMillis();
                 for (int i = 0; i < epochs; i++) {
-                    System.out.println("Epoch: " + (i + 1));
+                    U.println("Epoch: " + (i + 1));
                     int sampleN = (int) (Math.random() * X.length);
                     this.train(X[sampleN], Y[sampleN], true);
                 }
                 long end = System.currentTimeMillis();
                 long elapsedTime = end - start;
-                System.out.println("Training took : " + (elapsedTime / 1000) + "s");
+                U.println("Training took : " + (elapsedTime / 1000) + "s");
 
                 break;
             }
         }
-
     }
 
     public void train(double[] X, double[] Y, Boolean showLoss) {
@@ -144,7 +144,6 @@ public void train(double[] X, double[] Y, Boolean showLoss) {
 
         weights_ih.add(wih_delta);
         bias_h.add(h_gradient);
-
     }
 
     private void printLoss(Matrix error) {
@@ -156,12 +155,54 @@ private void printLoss(Matrix error) {
             }
         }
 
-        System.out.print("Average Error: " + avg + "\n");
+        U.print("Average Error: " + avg + "\n");
+    }
+
+    public void testData(double[][] testing_in, double[][] testing_out) {
+        int i = 0;
+        List<Double> output;
+        for (double d[] : testing_in) {
+            output = predict(d);
+            U.printf("Q: %s\nIn: %s\nOut: %s\nPrediction: %s\n",i,U.arrStr(d),U.arrStr(testing_out[i++]),output.toString());
+        }
+    }
+
+    public double[][] getFileData(String fileName) {
+        List<String> rawData = U.readFile(fileName);
+        List<ArrayList> filteredData = new ArrayList<ArrayList>();
+
+        for (String n : rawData) {
+            String[] s = n.split(",");
+            List<Double> temp = new ArrayList<Double>();
+            for (String i : s) {
+                temp.add(Double.parseDouble(i));
+            }
+            filteredData.add((ArrayList<Double>) temp);
+        }
+
+        double[][] dataArr = nestedListToNestedArr(filteredData);
+
+        return dataArr;
+    }
+
+    private static double[][] nestedListToNestedArr(List<ArrayList> dataList) {
+        double[][] dataArr = new double[dataList.size()][dataList.get(0).size()];
+        int y = 0;
+        for (List<ArrayList> n : dataList) {
+            int x = 0;
+            for (Object i : n) {
+                dataArr[y][x] = ((Double) i);
+                x++;
+            }
+            y++;
+        }
+        return dataArr;
     }
-
 }
 
 class Matrix {
+    private static BasicUtils U = new BasicUtils();
+
     double[][] data;
     int rows, cols;
 
@@ -179,9 +220,9 @@ public Matrix(int rows, int cols) {
     public void print() {
         for (int i = 0; i < rows; i++) {
             for (int j = 0; j < cols; j++) {
-                System.out.print(this.data[i][j] + " ");
+                U.print(this.data[i][j] + " ");
             }
-            System.out.println();
+            U.println("");
         }
     }
 
@@ -196,7 +237,7 @@ public void add(int scaler) {
 
     public void add(Matrix m) {
         if (cols != m.cols || rows != m.rows) {
-            System.out.println("Shape Mismatch");
+            U.println("Shape Mismatch");
             return;
         }
 
@@ -212,7 +253,6 @@ public static Matrix fromArray(double[] x) {
         for (int i = 0; i < x.length; i++)
             temp.data[i][0] = x[i];
         return temp;
-
     }
 
     public List<Double> toArray() {
@@ -285,15 +325,13 @@ public void multiply(double a) {
                 this.data[i][j] *= a;
             }
         }
-
     }
 
     public void sigmoid() {
         for (int i = 0; i < rows; i++) {
             for (int j = 0; j < cols; j++)
                 this.data[i][j] = 1 / (1 + Math.exp(-this.data[i][j]));
         }
-
     }
 
     public Matrix dsigmoid() {
@@ -303,7 +341,6 @@ public Matrix dsigmoid() {
                 temp.data[i][j] = this.data[i][j] * (1 - this.data[i][j]);
         }
         return temp;
-
     }
 }
 
@@ -357,7 +394,6 @@ public void run() {
 }
 
 class RowMultiplyWorker implements Runnable {
-
     private final Matrix result;
     private Matrix matrix1;
     private Matrix matrix2;
@@ -377,11 +413,57 @@ public void run() {
             result.data[row][i] = 0;
             for (int j = 0; j < matrix1.data[row].length; j++) {
                 result.data[row][i] += matrix1.data[row][j] * matrix2.data[j][i];
-
             }
-
         }
-
     }
+}
 
-}
+final class BasicUtils {
+    private static PrintWriter writer = new PrintWriter(System.out);
+
+    public static List<String> readFile(String fileName) {
+        List<String> out = new ArrayList<String>();
+        try {
+            File file = new File(fileName);
+            Scanner reader = new Scanner(file);
+            while (reader.hasNextLine()) {
+                String data = reader.nextLine();
+                out.add(data);
+            }
+            reader.close();
+        } catch (FileNotFoundException e) {
+            println("An error occurred.");
+            e.printStackTrace();
+        }
+
+        return out;
+    }
+
+    public static String arrStr(double[] arr) {
+        return Arrays.toString(arr);
+    }
+
+    public static void print(Object str) {
+        writer.write(String.valueOf(str));
+        writer.flush();
+    }
+
+    public static void println(Object str) {
+        print(str+"\n");
+    }
+
+    public static void printf(Object mainText, Object... x) {
+        String toPrint = "";
+
+        String[] y = String.valueOf(mainText).split("%s");
+        int i = 0;
+        for (String n : y) {
+            toPrint += n;
+            if (i<x.length) {
+                toPrint += x[i++];
+            }
+        }
+
+        println(toPrint);
+    }
+}

Neural_Network/testing_data_in.txt

@@ -0,0 +1,4 @@
+0,0
+0,1
+1,0
+1,1

Neural_Network/testing_data_out.txt

@@ -0,0 +1,4 @@
+0
+1
+1
+0

Neural_Network/training_data_in.txt

@@ -0,0 +1,4 @@
+0,0
+1,0
+0,1
+1,1

Neural_Network/training_data_out.txt

@@ -0,0 +1,4 @@
+0
+1
+1
+0