From 40e469ff0b451103465fbcdd6897d5359aab865f Mon Sep 17 00:00:00 2001
From: Maksuel Boni <maksuel@gmail.com>
Date: Wed, 28 Mar 2018 11:25:58 +0200
Subject: [PATCH] handle errors and prepare for the implementation of hidden
 multi-layers

Added "Rest parameters" for the future implementation of hidden multi-layers, where you will pass arguments like this:
new NeuralNetwork(3,6,8,6,4)
new NeuralNetwork(input, hidden_1, hidden_2, hidden_3..., output)

Added "handle errors" in constructor method.
---
 lib/nn.js | 50 ++++++++++++++++++++++++++++++++------------------
 1 file changed, 32 insertions(+), 18 deletions(-)

diff --git a/lib/nn.js b/lib/nn.js
index 0260280..f864d7b 100644
--- a/lib/nn.js
+++ b/lib/nn.js
@@ -19,22 +19,36 @@ let tanh = new ActivationFunction(
 
 
 class NeuralNetwork {
-  // TODO: document what a, b, c are
-  constructor(a, b, c) {
-    if (a instanceof NeuralNetwork) {
-      this.input_nodes = a.input_nodes;
-      this.hidden_nodes = a.hidden_nodes;
-      this.output_nodes = a.output_nodes;
-
-      this.weights_ih = a.weights_ih.copy();
-      this.weights_ho = a.weights_ho.copy();
-
-      this.bias_h = a.bias_h.copy();
-      this.bias_o = a.bias_o.copy();
-    } else {
-      this.input_nodes = a;
-      this.hidden_nodes = b;
-      this.output_nodes = c;
+  /**
+   * Construct method.
+   * 
+   * The user can enter 3 parameters (integer) to create a new NeuralNetwork,
+   * where: the first parameter represents the number of inputs, the second
+   * parameter represents the number of hidden nodes and the third parameter
+   * represents the number of outputs of the network.
+   * The user can copy an instance of NeuralNetwork by passing the same as an
+   * argument to the constructor.
+   * 
+   * @param {NeuralNetwork|integer} args (Rest parameters)
+   */
+  constructor(...args) {
+    if (args.length === 1 && args[0] instanceof NeuralNetwork) {
+      this.input_nodes  = args[0].input_nodes;
+      this.hidden_nodes = args[0].hidden_nodes;
+      this.output_nodes = args[0].output_nodes;
+
+      this.weights_ih = args[0].weights_ih.copy();
+      this.weights_ho = args[0].weights_ho.copy();
+
+      this.bias_h = args[0].bias_h.copy();
+      this.bias_o = args[0].bias_o.copy();
+    } else if(args.length === 3) {
+      if(!args.every( v => Number.isInteger(v) )) {
+        throw new Error('All arguments must be integer.');
+      }
+      this.input_nodes  = args[0];
+      this.hidden_nodes = args[1];
+      this.output_nodes = args[2];
 
       this.weights_ih = new Matrix(this.hidden_nodes, this.input_nodes);
       this.weights_ho = new Matrix(this.output_nodes, this.hidden_nodes);
@@ -45,13 +59,13 @@ class NeuralNetwork {
       this.bias_o = new Matrix(this.output_nodes, 1);
       this.bias_h.randomize();
       this.bias_o.randomize();
+    } else {
+      throw new Error('Invalid arguments. Read the documentation!');
     }
 
     // TODO: copy these as well
     this.setLearningRate();
     this.setActivationFunction();
-
-
   }
 
   predict(input_array) {