diff --git a/lib/matrix.js b/lib/matrix.js
index b232d36..62fade9 100644
--- a/lib/matrix.js
+++ b/lib/matrix.js
@@ -8,14 +8,14 @@ class Matrix {
     this.data = Array(this.rows).fill().map(() => Array(this.cols).fill(0));
   }
 
-  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];
+  static copy(matrix) {
+    let matrixCopy = new Matrix(matrix.rows, matrix.cols);
+    for (let i = 0; i < matrix.rows; i++) {
+      for (let j = 0; j < matrix.cols; j++) {
+        matrixCopy.data[i][j] = matrix.data[i][j];
       }
     }
-    return m;
+    return matrixCopy;
   }
 
   static fromArray(arr) {
diff --git a/lib/matrix.test.js b/lib/matrix.test.js
index 49fc653..9b6fdee 100644
--- a/lib/matrix.test.js
+++ b/lib/matrix.test.js
@@ -381,7 +381,7 @@ test('matrix copy', () => {
   let m = new Matrix(5, 5);
   m.randomize();
 
-  let n = m.copy();
+  let n = Matrix.copy(m);
 
   expect(n).toEqual({
     rows: m.rows,
diff --git a/lib/nn.js b/lib/nn.js
index 0cb4527..2ceccb8 100644
--- a/lib/nn.js
+++ b/lib/nn.js
@@ -30,11 +30,10 @@ class NeuralNetwork {
       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();
+      this.weights_ih = Matrix.copy(a.weights_ih);
+      this.weights_ho = Matrix.copy(a.weights_ho);
+      this.bias_h = Matrix.copy(a.bias_h);
+      this.bias_o = Matrix.copy(a.bias_o);
     } else {
       this.input_nodes = in_nodes;
       this.hidden_nodes = hid_nodes;