Added JavaDoc comments

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

@@ -21,8 +21,7 @@ public class Network {
     }
 
     /**
-     * Adds n neurons to a specific layer and also updates this and the next layer's weights and biases.
-     * Only works if there are two successive BlankLayers.
+     * Adds n neurons to a specific layer and also updates this and the next layer's weights and biases
      * @param layer index of layer in the ArrayList layers
      * @param n amount how many new neurons should be added
      */
@@ -36,11 +35,22 @@ public class Network {
         ((FCLayer) this.layers.get(layer + 2)).updateInputSize(n);
     }
 
+    /**
+     * Sets loss function used to evaluate the network
+     * Chosen from {@link de.lluni.javann.functions.LossFunctions}
+     * @param loss loss function
+     * @param lossPrime derivative of the loss function
+     */
     public void use(BiFunction<SimpleMatrix, SimpleMatrix, Double> loss, BiFunction<SimpleMatrix, SimpleMatrix, SimpleMatrix> lossPrime) {
         this.loss = loss;
         this.lossPrime = lossPrime;
     }
 
+    /**
+     * Predicts labels corresponding to the given inputs
+     * @param inputs inputs
+     * @return array of predictions
+     */
     public SimpleMatrix[] predict(SimpleMatrix[] inputs) {
         SimpleMatrix[] result = new SimpleMatrix[inputs.length];
         SimpleMatrix output;
@@ -58,6 +68,13 @@ public class Network {
         return result;
     }
 
+    /**
+     * Trains the network on a set of training data
+     * @param X_train inputs
+     * @param y_train labels
+     * @param epochs amount of training iterations
+     * @param learningRate step size of gradient descent
+     */
     public void fit(SimpleMatrix[] X_train, SimpleMatrix[] y_train, int epochs, double learningRate) {
         int samples = X_train.length;
 

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

@@ -4,10 +4,18 @@ import org.ejml.simple.SimpleMatrix;
 
 import java.util.function.Function;
 
+/**
+ * Layer to pass each output of every neuron from the previous layer through an activation function
+ */
 public class ActivationLayer extends Layer {
     Function<SimpleMatrix, SimpleMatrix> activation;
     Function<SimpleMatrix, SimpleMatrix> activationPrime;
 
+    /**
+     * Creates new activation layer with an activation function and its derivative from {@link de.lluni.javann.functions.ActivationFunctions}
+     * @param activation activation function
+     * @param activationPrime derivative of the activation function to compute gradient for backpropagation
+     */
     public ActivationLayer(Function<SimpleMatrix, SimpleMatrix> activation, Function<SimpleMatrix, SimpleMatrix> activationPrime) {
         this.activation = activation;
         this.activationPrime = activationPrime;

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

@@ -5,12 +5,19 @@ import org.ejml.simple.SimpleMatrix;
 
 import java.util.Random;
 
+/**
+ * Fully connected layer with n Neurons
+ */
 public class FCLayer extends Layer {
     private SimpleMatrix weights;
     private SimpleMatrix biases;
     private int numNeurons;
     private boolean isInitialized;
 
+    /**
+     * Creates a fully connected layer with numNeurons neurons
+     * @param numNeurons amount of neurons in this layer
+     */
     public FCLayer(int numNeurons) {
         this.numNeurons = numNeurons;
         isInitialized = false;

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

@@ -14,12 +14,27 @@ import java.util.Random;
 public class Utilities {
     private static final double STANDARD_GAUSSIAN_FACTOR = 1.0d;
 
+    /**
+     * Creates a matrix filled with ones
+     * @param rows amount of rows
+     * @param columns amount of columns
+     * @return matrix filled with ones
+     */
     public static SimpleMatrix ones(int rows, int columns) {
         SimpleMatrix mat = new SimpleMatrix(rows, columns);
         Arrays.fill(mat.getDDRM().data, 1);
         return mat;
     }
 
+    /**
+     * Creates a matrix with Gaussian distributed values
+     * @param rows amount of rows
+     * @param columns amount of columns
+     * @param mean mean of Gaussian distribution
+     * @param stddev standard deviation of Gaussian distribution
+     * @param factor factor to multiply each Gaussian distributed value with
+     * @return matrix of Gaussian distributed values
+     */
     public static SimpleMatrix gaussianMatrix(int rows, int columns, double mean, double stddev, double factor) {
         SimpleMatrix mat = new SimpleMatrix(rows, columns);
         Random random = new Random();
@@ -35,6 +50,13 @@ public class Utilities {
         return gaussianMatrix(rows, columns, mean, stddev, STANDARD_GAUSSIAN_FACTOR);
     }
 
+    /**
+     * Creates an array of evenly spaced values from the interval [start, end)
+     * @param start start value
+     * @param end end value
+     * @param num amount of values
+     * @return array of values
+     */
     public static double[] linspace(double start, double end, int num) {
         double[] result = new double[num];
         double stepSize = Math.abs(end - start) / num;