From dcb323cfa08c0cfe6e616e1937f605d0fd4815ef Mon Sep 17 00:00:00 2001
From: Ulysse McConnell <ulysse.mcconnell+dev@protonmail.com>
Date: Tue, 2 Jul 2019 16:25:37 +0200
Subject: [PATCH] Add Network class

---
 lib/index.js | 86 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)

diff --git a/lib/index.js b/lib/index.js
index 7665afc..b05b770 100644
--- a/lib/index.js
+++ b/lib/index.js
@@ -212,3 +212,89 @@ export class Layer {
         return this.next;
     }
 }
+
+/**
+ * Neural Network Class
+ * @extends Array
+ * @example
+ * // Import Constructors and helpers
+ * import { Network, Layer, Weights, Biases } from "./index.js";
+ * import { sigmoid } from "./helpers.js";
+ *
+ * // Create network
+ * let network = new Network([2048, 1024, 512, 256]);
+ * // Run network
+ * network
+ *   .addWeights()
+ *   .addBiases()
+ *   .connect()
+ *   .run()
+ *   .apply(sigmoid);
+ */
+export class Network extends Array {
+    /**
+     * Creates a neural network with given layers
+     * @param {Array} layers array of Layer objects or numbers representing layer length
+     */
+    constructor(layers) {
+        super(...layers.map(el => (el instanceof Layer ? el : new Layer(el))));
+    }
+
+    /**
+     * Adds randomly filled bias matrix with correct dimensions to every layer by calling Biases constructor
+     * @returns {Network}
+     */
+    addBiases() {
+        this.forEach((layer, i) => {
+            if (i > 0) {
+                layer.addBiases(new Biases(layer.length).fillRandom());
+            }
+        });
+        return this;
+    }
+
+    /**
+     * Adds randomly filled weight matrix with correct dimensions to every layer by calling Weights constructor
+     * @returns {Network}
+     */
+    addWeights() {
+        this.forEach((layer, i) => {
+            if (i < this.length - 1) {
+                layer.addWeights(
+                    new Weights(layer.length, this[i + 1].length).fillRandom()
+                );
+            }
+        });
+        return this;
+    }
+
+    /**
+     * Applies a function to every layer by calling layer.apply() on every layer
+     * @param {Function} func mapping function called on every layer with layer.apply()
+     * @returns {Network}
+     */
+    apply(func) {
+        this.forEach((layer, i) => (i > 0 ? layer.apply(func) : ""));
+        return this;
+    }
+
+    /**
+     * Connects every layers with its succedding layer by calling layer.connect
+     * @returns {Network}
+     */
+    connect() {
+        this.forEach((layer, i) =>
+            i < this.length - 1 ? layer.connect(this[i + 1]) : ""
+        );
+        return this;
+    }
+
+    /**
+     * Runs the neural network
+     * @returns {Network}
+     */
+    run() {
+        this.forEach(layer => (layer.next ? layer.run() : ""));
+        return this;
+    }
+}