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Change copy functions over to use (de)serialization feature #98

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Change copy functions over to use (de)serialization feature #98

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94eb331
Change copy functions over to use (de)serialization feature
enginefeeder101 Mar 10, 2018
c12e4c4
Removed copy tests
enginefeeder101 Mar 10, 2018
920d007
Merge branch 'master' into copying
enginefeeder101 Mar 11, 2018
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8 changes: 1 addition & 7 deletions lib/matrix.js
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Original file line number Diff line number Diff line change
Expand Up @@ -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) {
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14 changes: 14 additions & 0 deletions lib/matrix.test.js
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Expand Up @@ -363,6 +363,7 @@ test('static map with row and column params', () => {
]
});
});

test('matrix (de)serialization', () => {
let m = new Matrix(5, 5);
m.randomize();
Expand All @@ -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
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will this work? Won't we have to check the values of the arrays themselves as opposed to the data reference?

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It does, as this does not pass:

  let n = m.copy();
  m.add(1);

  expect(n).toEqual({
    rows: m.rows,
    cols: m.cols,
    data: m.data
  });

schermafdruk_2018-03-10_18-22-24

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Actually expect(n).toEqual(m); works as well 😆

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Oh cool! So Jest does something like a "deep" comparison behind the scenes?

});
});
47 changes: 15 additions & 32 deletions lib/nn.js
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Expand Up @@ -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) {
Expand Down Expand Up @@ -158,7 +141,7 @@ class NeuralNetwork {

// Adding function for neuro-evolution
copy() {
return new NeuralNetwork(this);
return NeuralNetwork.deserialize(this.serialize());
}

mutate(rate) {
Expand Down