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brain.js
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brain.js
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function uid() {
var uuidTemplate = "xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";
return uuidTemplate.replace(/[xy]/g, function (c) {
var r = (Math.random() * 16) | 0;
var v = c === "x" ? r : (r & 0x3) | 0x8;
return v.toString(16);
});
}
class Neuron {
id;
bias;
squash;
cost;
incoming;
outgoing;
_output;
output;
error;
_error;
constructor(bias) {
this.id = uid();
this.bias = bias == undefined ? Math.random() * 2 - 1 : bias;
this.squash;
this.cost;
this.incoming = {
targets: {},
weights: {},
};
this.outgoing = {
targets: {},
weights: {},
};
this._output;
this.output;
this.error;
this._error;
}
connect(neuron, weight) {
this.outgoing.targets[neuron.id] = neuron;
neuron.incoming.targets[this.id] = this;
this.outgoing.weights[neuron.id] = neuron.incoming.weights[this.id] =
weight == undefined ? Math.random() * 2 - 1 : weight;
}
activate(input) {
const self = this;
function sigmoid(x) {
return 1 / (1 + Math.exp(-x));
}
function _sigmoid(x) {
return sigmoid(x) * (1 - sigmoid(x));
}
if (input != undefined) {
this._output = 1;
this.output = input;
} else {
const sum = Object.keys(this.incoming.targets).reduce(function (
total,
target,
index
) {
return (total +=
self.incoming.targets[target].output * self.incoming.weights[target]);
},
this.bias);
this._output = _sigmoid(sum);
this.output = sigmoid(sum);
}
return this.output;
}
propagate(target, rate = 0.3) {
const self = this;
const sum =
target == undefined
? Object.keys(this.outgoing.targets).reduce(function (total, target, index) {
self.outgoing.targets[target].incoming.weights[self.id] =
self.outgoing.weights[target] -=
rate * self.outgoing.targets[target].error * self.output;
return (total +=
self.outgoing.targets[target].error * self.outgoing.weights[target]);
}, 0)
: this.output - target;
this.error = sum * this._output;
this.bias -= rate * this.error;
return this.error;
}
}
class Network {
inputs;
hiddens;
outputs;
constructor(inputs = [], hiddens = [], outputs = []) {
this.inputs = inputs;
this.hiddens = hiddens;
this.outputs = outputs;
}
connectAll() {
this.inputs.forEach((input, i) => {
this.hiddens.forEach((hidden, j) => {
this.inputs[i].connect(this.hiddens[j]);
});
});
this.hiddens.forEach((hidden, i) => {
this.outputs.forEach((output, j) => {
this.hiddens[i].connect(this.outputs[j]);
});
});
}
activate(input) {
this.inputs.forEach((neuron, i) => neuron.activate(input[i]));
this.hiddens.forEach((neuron) => neuron.activate());
return this.outputs.map((neuron) => neuron.activate());
}
propagate(target) {
this.outputs.forEach((neuron, t) => neuron.propagate(target[t]));
this.hiddens.forEach((neuron) => neuron.propagate());
return this.inputs.forEach((neuron) => neuron.propagate());
}
train(iterations = 1, dataset) {
while (iterations > 0) {
dataset.map((datum) => {
this.activate(datum.inputs);
this.propagate(datum.outputs);
});
iterations--;
}
}
}