我設法寫我自己的解決方案。您可以在這裏找到它：https://github.com/developer239/neural-network-playground/tree/master/neatXOR
與文檔解決方案的主要區別在於，您可以在genetic.js中動態更改學習過程。
這是入口文件：
const genetic = require('./genetic')
genetic.generateRandomPopulation()
for (let iteration = 0; iteration < 1000; iteration += 1) {
genetic.live()
genetic.evolve()
}
const genom = genetic.neat.population[0]
console.log(`
Result for genom with index 0 in the newest population. Note that selection/mutation happened
after we called last evolve function so this is not necessarily the best genome in the population.
[0, 0] = ${genom.activate([0, 0])} (should be 0)
[1, 1] = ${genom.activate([1, 1])} (should be 0)
[0, 1] = ${genom.activate([0, 1])} (should be 1)
[1, 0] = ${genom.activate([1, 0])} (should be 1)
`)
這是genetic.js文件：
const { Neat, architect } = require('neataptic')
module.exports = {
neat: null, // https://wagenaartje.github.io/neataptic/docs/neat/
possibleInputs: [
[0, 0], // expected output 0
[1, 1], // expected output 0
[0, 1], // expected output 1
[1, 0], // expected output 1
],
generateRandomPopulation: function() {
this.neat = new Neat(
2, // number of inputs
1, // number of outputs
null, // fitnessFunction - in this example we are calculating fitness inside live method
{
elitism: 5, // this sets how many genomes in population will be passed into next generation without mutation https://www.researchgate.net/post/What_is_meant_by_the_term_Elitism_in_the_Genetic_Algorithm
mutationRate: 0.3, // sets the mutation rate. If set to 0.3, 30% of the new population will be mutated. Default is 0.3
network: // https://wagenaartje.github.io/neataptic/docs/architecture/network/
new architect.Random(
2,
3,
1,
),
},
)
},
// the closer the output gets to expectedOutput the better
// note that optimal fitness in this example is 0 neural network seems to work fine though
calculateFitness: function (expectedOutput, output) {
let closeCount = Math.abs(expectedOutput - output)
let fitness = closeCount * -1
return fitness
},
live: function() {
// increment generation index
this.neat.generation += 1
// loop through each genome
for (let genomeIndex in this.neat.population) {
const possibleInputs = this.possibleInputs
const genome = this.neat.population[genomeIndex]
genome.score = 0
// loop through each input
for (let i = 0; i < possibleInputs.length; i += 1) {
let input = possibleInputs[i]
// test each input
let output = genome.activate(input)[0]
// calculate fitness for each output
// we have 4 different inputs so the total score is sum of 4 different fitness values
if (i <= 1) {
genome.score += this.calculateFitness(0, output)
} else {
genome.score += this.calculateFitness(1, output)
}
}
}
},
evolve: function() {
const neat = this.neat
console.log(`[generation ${neat.generation}] Average score: ${neat.getAverage()} (the closer to zero the better)`)
// sort by genome.score in descending order
neat.sort()
// our new population will be here
let newPopulation = []
// we want to push neat.elitism number of best genomes into the new population automatically
for (let i = 0; i < neat.elitism; i++) {
newPopulation.push(neat.population[i])
}
// we want to get offspring from the current population and push it into the new population
for (let i = 0; i < neat.popsize - neat.elitism; i++) {
newPopulation.push(neat.getOffspring())
}
// set new population
neat.population = newPopulation
// mutate the population
neat.mutate()
},
}