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Handle errors and prepare for the implementation of hidden multi-layers #105

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50 changes: 32 additions & 18 deletions lib/nn.js
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Original file line number Diff line number Diff line change
Expand Up @@ -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);
Expand All @@ -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) {
Expand Down