gradlew

@@ -31,7 +31,7 @@
 #
 #       Busybox and similar reduced shells will NOT work, because this script
 #       requires all of these POSIX shell features:
-#         * de.lluni.javann.functions;
+#         * 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/ActivationFunctions.java

@@ -1,5 +1,3 @@
-package de.lluni.javann.functions;
-
 import org.ejml.simple.SimpleMatrix;
 
 public class ActivationFunctions {
@@ -54,20 +52,4 @@ 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/ActivationLayer.java

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

src/main/java/BlankLayer.java

@@ -1,12 +1,10 @@
-package de.lluni.javann.layers;
-
 import org.ejml.simple.SimpleMatrix;
 
 import java.util.Random;
 
 /**
  * Goal: initialize layer without any neurons. Not yet implemented.
- * de.lluni.javann.layers.Layer initialized with 1 neuron.
+ * 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/ExampleGradientDescent.java

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

src/main/java/ExampleXOR.java

@@ -1,10 +1,3 @@
-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/ExampleXORBlankLayers.java

@@ -1,10 +1,3 @@
-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/FCLayer.java

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

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

src/main/java/Layer.java

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

src/main/java/LossFunctions.java

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

src/main/java/Network.java

@@ -1,7 +1,3 @@
-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;
@@ -28,9 +24,9 @@ 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 de.lluni.javann.layers.BlankLayer");
+            System.out.println("This layer is not a BlankLayer");
         } else if (!(this.layers.get(layer + 2) instanceof FCLayer)) {
-            System.out.println("The next layer is not a de.lluni.javann.layers.BlankLayer");
+            System.out.println("The next layer is not a BlankLayer");
         }
         ((FCLayer) this.layers.get(layer)).addNeuron(n);
         ((FCLayer) this.layers.get(layer + 2)).updateInputSize(n);

src/main/java/Utilities.java

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

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

@@ -1,66 +0,0 @@
-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();
-    }
-}