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‎RNA/index.js

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//Funções de ativações.
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const ArcTan = valor => Math.atan(valor)
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const BentIdentity = valor => ((Math.sqrt((valor ** 2) + 1) - 1) / 2) + valor
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const BinaryStep = valor => ((valor < 0) ? 0 : 1)
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const Gaussian = valor => Math.exp(-(valor ** 2))
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const Identity = valor => valor
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const LeakyReLU = valor => PReLU(valor, 0.01)
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const ReLU = valor => ((valor < 0) ? 0 : valor)
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const SELU = valor => 1.0507 * ELU(valor, 1.67326)
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const Sigmoid = valor => 1.0 / (1.0 + Math.exp(-valor))
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const SigmoidRcional = valor => valor / (1.0 + Math.sqrt(1.0 + valor * valor))
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const SiLU = valor => valor * Sigmoid(valor)
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const Sinc = valor => (valor = 0) ? 1 : (Math.sin(valor) / valor)
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const Sinusoid = valor => Math.sin(valor)
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const SoftSign = valor => valor / (1 + Math.abs(valor))
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const SoftPlus = valor => Math.log(1 + Math.exp(valor))
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const TanH = valor => (Math.exp(valor) - Math.exp(-valor)) + (Math.exp(valor) + Math.exp(-valor))
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const PReLU = (valor, arg) => ((valor < 0) ? (arg * valor) : valor)
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const ELU = (valor, arg) => PReLU(arg, Math.expm1(valor))
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const Pipe = (valor, ...fns) => fns.reduce((acc, current) => acc = current(valor))
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/*
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Gaussian_Derivative: valor => -2.0 * valor * Gaussian(valor),
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Sigmoid_Derivative: valor => Sigmoid(valor) * (1 - Sigmoid(valor)),
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SigmoidRcional_Derivative: valor => 1.0 / (Math.sqrt(1.0 + valor * valor) * (1 + Math.sqrt(1.0 + valor * valor)))
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*/
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//Função interna
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const calcular = (pesos, entradas, vies = 0) => {
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const result = pesos.map((peso, i) => peso * entradas[i])
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return result.reduce((acc, current) => acc + current) + vies
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}
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//Função interna
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const randpesos = (...entradas) => {
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const min = Math.ceil(-1000)
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const max = Math.floor(1000)
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return entradas[0].map(entrada => entrada = Math.floor(Math.random() * (max - min + 1)) + min)
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}
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const Softmax = genoma => {
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const gens = genoma.map(x => Math.exp(x - Math.max(...genoma)))
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return gens.map(y => y / gens.reduce((acc, current) => acc + current))
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}
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const entradas = entradas => {
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let pesos = randpesos(entradas) || [0]
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let vies = 0
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return {
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vies: {
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get: () => vies,
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set: (valor) => vies = valor || 0
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},
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pesos: {
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get: () => pesos,
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set: (valores) => pesos = valores || [0]
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},
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saida: callback => {
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if (!callback) return calcular(pesos, entradas, vies)
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if (typeof callback === 'function') return callback(calcular(pesos, entradas, vies))
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}
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}
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}
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//console.log(mutar([1,2,3,4,5,6,7,8,9], 1))
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const mutar = (genoma, vezes = 1) => {
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if (vezes <= 0) vezes = 1
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const indexList = Array(vezes).fill(0).map(vez => Math.floor(Math.random()*genoma.length))
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const filho = genoma.map((gen, index) => indexList.includes(index) ? randpesos([0])[0] : gen)
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return filho
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}
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//console.log(crossover([1,2,8,9,10], [6,7,3,4,5], 2))
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const crossover = (genoma1, genoma2, index) => [
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genoma1.slice(0, index).concat(genoma2.slice(index)),
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genoma2.slice(0, index).concat(genoma1.slice(index))
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]
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const neuronios = (entrada, contia) => Array(contia).fill(0).map(_ => entradas(entrada))
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const saidas = (neuronios, fn, contia) => {
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return Array(contia).fill(0).map(_ => entradas(neuronios.map(entrada => entrada.saida(fn))))
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}
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const setPesos = (neuronio, pesos) => neuronio.map((neuro, index) => neuro.pesos.set(pesos[index]))
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//console.log(genConcat(neuronio, oculta))
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const genConcat = (...neuronios) => {
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return neuronios.map(gen => gen.map(g => g.pesos.get()))
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.reduce((acc, current) => acc.concat(current))
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.reduce((acc, current) => acc.concat(current))
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}
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module.exports = {
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entradas,
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mutar,
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crossover,
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Softmax,
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calcular,
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neuronios,
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saidas,
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setPesos,
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genConcat,
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fn: {
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ArcTan,
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BentIdentity,
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BinaryStep,
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Gaussian,
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Identity,
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LeakyReLU,
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ReLU,
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SELU,
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Sigmoid,
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SigmoidRcional,
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SiLU,
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Sinc,
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Sinusoid,
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SoftSign,
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SoftPlus,
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TanH,
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PReLU,
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ELU,
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Pipe
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}
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}

‎exemplo-1.js

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const RNA = require('./RNA')
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const entrada = [200, 15, 8]
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const neuronio = [
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RNA.entradas(entrada),
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RNA.entradas(entrada)
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]
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//neuronio[0].vies.set(3)
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neuronio[0].pesos.set([100, 1, 5])
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neuronio[1].pesos.set([-200, 5, 10])
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const oculta = [
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RNA.entradas([
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neuronio[0].saida(RNA.fn.ReLU),
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neuronio[1].saida(RNA.fn.ReLU)
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]),
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RNA.entradas([
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neuronio[0].saida(RNA.fn.ReLU),
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neuronio[1].saida(RNA.fn.ReLU)
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])
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]
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oculta[0].pesos.set([1, 500])
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oculta[1].pesos.set([0, -500])
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console.log('Entradas:', entrada)
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console.log('Pesos:', {
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'Neuronio 1': neuronio[0].pesos.get(),
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'Neuronio 2': neuronio[1].pesos.get(),
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'Oculta 1': oculta[0].pesos.get(),
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'Oculta 2': oculta[1].pesos.get(),
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})
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console.log('Saida 1:', oculta[0].saida())
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console.log('Saida 2:', oculta[1].saida())
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const genoma = RNA.genConcat(neuronio, oculta)
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/*
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const genoma = [
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neuronio[0].pesos.get(),
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neuronio[1].pesos.get(),
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oculta[0].pesos.get(),
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oculta[1].pesos.get()
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].reduce((a, b) => a.concat(b))
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*/
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console.log('Genoma:', genoma)
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const genoma2 = RNA.mutar(genoma, 2)
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console.log('Mutação:', genoma2)
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console.log('Cross Over:', RNA.crossover(genoma, genoma2, 3))

‎exemplo-2.js

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const RNA = require('./RNA')
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const entrada = [200, 15, 8]
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const neuronio = RNA.neuronios(entrada, 2)
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RNA.setPesos(neuronio, [[100, 1, 5], [-200, 5, 10]])
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const oculta = RNA.saidas(neuronio, RNA.fn.ReLU, 2)
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RNA.setPesos(oculta, [[1, 500], [0, -500]])
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console.log('Entradas:', entrada)
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console.log('Pesos:', {
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'Neuronio 1': neuronio[0].pesos.get(),
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'Neuronio 2': neuronio[1].pesos.get(),
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'Oculta 1': oculta[0].pesos.get(),
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'Oculta 2': oculta[1].pesos.get(),
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})
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console.log('Saida 1:', oculta[0].saida())
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console.log('Saida 2:', oculta[1].saida())
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const genoma = RNA.genConcat(neuronio, oculta)
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console.log('Genoma:', genoma)

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