Changed project structure

gradlew

@@ -31,7 +31,7 @@
 #
 #       Busybox and similar reduced shells will NOT work, because this script
 #       requires all of these POSIX shell features:
-#         * functions;
+#         * de.lluni.javann.functions;
 #         * expansions «$var», «${var}», «${var:-default}», «${var+SET}»,
 #           «${var#prefix}», «${var%suffix}», and «$( cmd )»;
 #         * compound commands having a testable exit status, especially «case»;

src/main/java/de/lluni/javann/Network.java

@@ -1,3 +1,7 @@
+package de.lluni.javann;
+
+import de.lluni.javann.layers.FCLayer;
+import de.lluni.javann.layers.Layer;
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.ArrayList;
@@ -24,9 +28,9 @@ public class Network {
      */
     public void addNeuron(int layer, int n) {
         if (!(this.layers.get(layer) instanceof FCLayer)) {
-            System.out.println("This layer is not a BlankLayer");
+            System.out.println("This layer is not a de.lluni.javann.layers.BlankLayer");
         } else if (!(this.layers.get(layer + 2) instanceof FCLayer)) {
-            System.out.println("The next layer is not a BlankLayer");
+            System.out.println("The next layer is not a de.lluni.javann.layers.BlankLayer");
         }
         ((FCLayer) this.layers.get(layer)).addNeuron(n);
         ((FCLayer) this.layers.get(layer + 2)).updateInputSize(n);

src/main/java/de/lluni/javann/examples/ExampleGradientDescent.java

@@ -1,3 +1,6 @@
+package de.lluni.javann.examples;
+
+import de.lluni.javann.util.GradientDescent;
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.function.Function;

src/main/java/de/lluni/javann/examples/ExampleSine.java

@@ -0,0 +1,66 @@
+package de.lluni.javann.examples;
+
+import de.lluni.javann.Network;
+import de.lluni.javann.functions.ActivationFunctions;
+import de.lluni.javann.functions.LossFunctions;
+import de.lluni.javann.layers.ActivationLayer;
+import de.lluni.javann.layers.FCLayer;
+import de.lluni.javann.util.Utilities;
+import org.ejml.simple.SimpleMatrix;
+import org.knowm.xchart.SwingWrapper;
+import org.knowm.xchart.XYChart;
+import org.knowm.xchart.XYChartBuilder;
+
+import java.util.Random;
+
+public class ExampleSine {
+    private static final int TRAINING_SIZE = 100000;
+    private static final int TEST_SIZE = 1000;
+
+    public static void main(String[] args) {
+        SimpleMatrix[] X_train = new SimpleMatrix[TRAINING_SIZE];
+        SimpleMatrix[] y_train = new SimpleMatrix[TRAINING_SIZE];
+        double[] X_test_linspace = Utilities.linspace(0, 2 * Math.PI, TEST_SIZE);
+        double[] y_test_true = new double[TEST_SIZE];
+        double[] y_test_pred = new double[TEST_SIZE];
+        SimpleMatrix[] X_test = new SimpleMatrix[TEST_SIZE];
+        SimpleMatrix[] y_test = new SimpleMatrix[TEST_SIZE];
+        Random random = new Random();
+        for (int i = 0; i < TRAINING_SIZE; i++) {
+            double temp = random.nextDouble(0, 2 * Math.PI);
+            X_train[i] = new SimpleMatrix(new double[][]{{temp}});
+            y_train[i] = new SimpleMatrix(new double[][]{{Math.sin(temp)}});
+        }
+        for (int i = 0; i < TEST_SIZE; i++) {
+            X_test[i] = new SimpleMatrix(new double[][]{{X_test_linspace[i]}});
+            y_test[i] = new SimpleMatrix(new double[][]{{Math.sin(X_test_linspace[i])}});
+            y_test_true[i] = Math.sin(X_test_linspace[i]);
+        }
+
+        // create network and add layers
+        Network network = new Network();
+        network.addLayer(new FCLayer(8));
+        network.addLayer(new ActivationLayer(ActivationFunctions::LeakyReLu, ActivationFunctions::LeakyReLuPrime));
+        network.addLayer(new FCLayer(8));
+        network.addLayer(new ActivationLayer(ActivationFunctions::LeakyReLu, ActivationFunctions::LeakyReLuPrime));
+        network.addLayer(new FCLayer(1));
+
+        network.use(LossFunctions::MSE, LossFunctions::MSEPrime);
+        network.fit(X_train, y_train, 100, 0.05d);
+
+        SimpleMatrix[] output = network.predict(X_test);
+        for (int i = 0; i < output.length; i++) {
+            y_test_pred[i] = output[i].get(0);
+            System.out.println("Prediction for x=" + X_test[i].get(0) + " (correct value: " + y_test[i].get(0) + "):");
+            for (int j = 0; j < output[i].getNumElements(); j++) {
+                System.out.println(output[i].get(j));
+            }
+            System.out.println();
+        }
+
+        XYChart chart = new XYChartBuilder().title("sin(x) predictions").xAxisTitle("x").yAxisTitle("y").build();
+        chart.addSeries("sin(x) true", X_test_linspace, y_test_true);
+        chart.addSeries("sin(x) predictions", X_test_linspace, y_test_pred);
+        new SwingWrapper<>(chart).displayChart();
+    }
+}

src/main/java/de/lluni/javann/examples/ExampleXOR.java

@@ -1,3 +1,10 @@
+package de.lluni.javann.examples;
+
+import de.lluni.javann.Network;
+import de.lluni.javann.functions.ActivationFunctions;
+import de.lluni.javann.functions.LossFunctions;
+import de.lluni.javann.layers.ActivationLayer;
+import de.lluni.javann.layers.FCLayer;
 import org.ejml.simple.SimpleMatrix;
 
 public class ExampleXOR {

src/main/java/de/lluni/javann/examples/ExampleXORBlankLayers.java

@@ -1,3 +1,10 @@
+package de.lluni.javann.examples;
+
+import de.lluni.javann.Network;
+import de.lluni.javann.functions.ActivationFunctions;
+import de.lluni.javann.functions.LossFunctions;
+import de.lluni.javann.layers.ActivationLayer;
+import de.lluni.javann.layers.FCLayer;
 import org.ejml.simple.SimpleMatrix;
 
 public class ExampleXORBlankLayers {

src/main/java/de/lluni/javann/functions/ActivationFunctions.java

@@ -1,3 +1,5 @@
+package de.lluni.javann.functions;
+
 import org.ejml.simple.SimpleMatrix;
 
 public class ActivationFunctions {
@@ -52,4 +54,20 @@ public class ActivationFunctions {
         }
         return B;
     }
+
+    public static SimpleMatrix LeakyReLu(SimpleMatrix A) {
+        SimpleMatrix B = new SimpleMatrix(A);
+        for (int i = 0; i < A.getNumElements(); i++) {
+            B.set(i, Math.max(0.001 * A.get(i), A.get(i)));
+        }
+        return B;
+    }
+
+    public static SimpleMatrix LeakyReLuPrime(SimpleMatrix A) {
+        SimpleMatrix B = new SimpleMatrix(A);
+        for (int i = 0; i < A.getNumElements(); i++) {
+            B.set(i, A.get(i) < 0 ? 0.001 : 1);
+        }
+        return B;
+    }
 }

src/main/java/de/lluni/javann/functions/LossFunctions.java

@@ -1,3 +1,5 @@
+package de.lluni.javann.functions;
+
 import org.ejml.simple.SimpleMatrix;
 
 public class LossFunctions {

src/main/java/de/lluni/javann/layers/ActivationLayer.java

@@ -1,3 +1,5 @@
+package de.lluni.javann.layers;
+
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.function.Function;

src/main/java/de/lluni/javann/layers/BlankLayer.java

@@ -1,10 +1,12 @@
+package de.lluni.javann.layers;
+
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.Random;
 
 /**
  * Goal: initialize layer without any neurons. Not yet implemented.
- * Layer initialized with 1 neuron.
+ * de.lluni.javann.layers.Layer initialized with 1 neuron.
  * Assumes that each new neuron is fully connected to every previous neuron (this will be changed in the future).
  */
 public class BlankLayer extends Layer {

src/main/java/de/lluni/javann/layers/FCLayer.java

@@ -1,3 +1,6 @@
+package de.lluni.javann.layers;
+
+import de.lluni.javann.util.Utilities;
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.Random;
@@ -14,11 +17,8 @@ public class FCLayer extends Layer {
     }
 
     private void initialize(int inputSize) {
-        Random random = new Random();
+        this.weights = Utilities.gaussianMatrix(inputSize, numNeurons, 0, 1, 0.1d);
-        this.weights = new SimpleMatrix(inputSize, numNeurons, true,
+        this.biases = Utilities.ones(1, numNeurons);
-                random.doubles((long) inputSize*numNeurons, -1, 1).toArray());
-        this.biases = new SimpleMatrix(1, numNeurons, true,
-                random.doubles(numNeurons, -1, 1).toArray());
         this.isInitialized = true;
     }
 

src/main/java/de/lluni/javann/layers/Layer.java

@@ -1,3 +1,5 @@
+package de.lluni.javann.layers;
+
 import org.ejml.simple.SimpleMatrix;
 
 public abstract class Layer {

src/main/java/de/lluni/javann/util/GradientDescent.java

@@ -1,3 +1,5 @@
+package de.lluni.javann.util;
+
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.function.Function;

src/main/java/de/lluni/javann/util/Utilities.java

@@ -1,3 +1,5 @@
+package de.lluni.javann.util;
+
 import com.opencsv.CSVReader;
 import com.opencsv.exceptions.CsvValidationException;
 import org.ejml.simple.SimpleMatrix;