From 94eb331c220a85f08b6964660fcaa767245d5c46 Mon Sep 17 00:00:00 2001
From: Engine Feeder <enginefeeder101@users.noreply.github.com>
Date: Sat, 10 Mar 2018 17:27:58 +0100
Subject: [PATCH] Change copy functions over to use (de)serialization feature

The (de)serialzation functions already have all the code needed to make a deep copy
of the NeuralNetwork. This commit also adds a test for the `Matrix.copy()` function.
This also copies the learning rate and if #97 is merged the activation function is
copied as well.
---
 lib/matrix.js      |  8 +-------
 lib/matrix.test.js | 14 ++++++++++++++
 lib/nn.js          | 47 +++++++++++++++-------------------------------
 3 files changed, 30 insertions(+), 39 deletions(-)

diff --git a/lib/matrix.js b/lib/matrix.js
index 34b2d97..dd396eb 100644
--- a/lib/matrix.js
+++ b/lib/matrix.js
@@ -9,13 +9,7 @@ class Matrix {
   }
 
   copy() {
-    let m = new Matrix(this.rows, this.cols);
-    for (let i = 0; i < this.rows; i++) {
-      for (let j = 0; j < this.cols; j++) {
-        m.data[i][j] = this.data[i][j];
-      }
-    }
-    return m;
+    return Matrix.deserialize(this.serialize());
   }
 
   static fromArray(arr) {
diff --git a/lib/matrix.test.js b/lib/matrix.test.js
index 4933895..49fc653 100644
--- a/lib/matrix.test.js
+++ b/lib/matrix.test.js
@@ -363,6 +363,7 @@ test('static map with row and column params', () => {
     ]
   });
 });
+
 test('matrix (de)serialization', () => {
   let m = new Matrix(5, 5);
   m.randomize();
@@ -375,3 +376,16 @@ test('matrix (de)serialization', () => {
     data: m.data
   });
 });
+
+test('matrix copy', () => {
+  let m = new Matrix(5, 5);
+  m.randomize();
+
+  let n = m.copy();
+
+  expect(n).toEqual({
+    rows: m.rows,
+    cols: m.cols,
+    data: m.data
+  });
+});
diff --git a/lib/nn.js b/lib/nn.js
index 0260280..ba9124c 100644
--- a/lib/nn.js
+++ b/lib/nn.js
@@ -19,39 +19,22 @@ 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;
-
-      this.weights_ih = new Matrix(this.hidden_nodes, this.input_nodes);
-      this.weights_ho = new Matrix(this.output_nodes, this.hidden_nodes);
-      this.weights_ih.randomize();
-      this.weights_ho.randomize();
-
-      this.bias_h = new Matrix(this.hidden_nodes, 1);
-      this.bias_o = new Matrix(this.output_nodes, 1);
-      this.bias_h.randomize();
-      this.bias_o.randomize();
-    }
-
-    // TODO: copy these as well
+  constructor(input_nodes, hidden_nodes, output_nodes) {
+    this.input_nodes = input_nodes;
+    this.hidden_nodes = hidden_nodes;
+    this.output_nodes = output_nodes;
+
+    this.weights_ih = new Matrix(this.hidden_nodes, this.input_nodes);
+    this.weights_ho = new Matrix(this.output_nodes, this.hidden_nodes);
+    this.weights_ih.randomize();
+    this.weights_ho.randomize();
+
+    this.bias_h = new Matrix(this.hidden_nodes, 1);
+    this.bias_o = new Matrix(this.output_nodes, 1);
+    this.bias_h.randomize();
+    this.bias_o.randomize();
     this.setLearningRate();
     this.setActivationFunction();
-
-
   }
 
   predict(input_array) {
@@ -158,7 +141,7 @@ class NeuralNetwork {
 
   // Adding function for neuro-evolution
   copy() {
-    return new NeuralNetwork(this);
+    return NeuralNetwork.deserialize(this.serialize());
   }
 
   mutate(rate) {