/* eslint-disable */
const _ = require("lodash");
let Network = require('./architecture/network');
let methods = require('./methods/methods');
let config = require('./config');
// Easier variable naming
let selection = methods.selection;
function Population({
template,
size=50,
data=[],
population=[],
fitness=(genome, data) => 1 - genome.test(data).error,
_sorted=false,
_selection=methods.selection.POWER
} = {}) {
let self = this;
_.assignIn(self, {template,size,data,population,fitness});
if(self.template && !self.population.length) _.times(self.size, function() {
self.population.push(Network.fromJSON({ ...self.template.toJSON(), score: undefined }));
});
}
Population.prototype = {
evaluate: async function() {
let self = this;
_.each(self.population, function(genome, index) {
self.population[index].score = self.fitness(genome, self.data);
})
},
// Mates 2 "good genomes"; returns child
mate: function(genomes, options) {
let self = this;
return Network.crossOver(self.getParent(), self.getParent(), true);
},
// Selects a "good genome" by `this.selection`
select: function (options) {
let self = this;
let i;
switch (this.selection) {
case selection.POWER:
if(!self._sorted) self.population = _.sortBy(self.population, ["score"]);
return this.population[Math.floor(Math.pow(Math.random(), self.selection.power) * self.population.length)];
case selection.FITNESS_PROPORTIONATE:
// As negative fitnesses are possible
// https://stackoverflow.com/questions/16186686/genetic-algorithm-handling-negative-fitness-values
// this is unnecessarily run for every individual, should be changed
let totalFitness = 0;
let minimalFitness = 0;
for (i = 0; i < this.population.length; i++) {
let score = this.population[i].score;
minimalFitness = score < minimalFitness ? score : minimalFitness;
totalFitness += score;
}
minimalFitness = Math.abs(minimalFitness);
totalFitness += minimalFitness * this.population.length;
let random = Math.random() * totalFitness;
let value = 0;
for (i = 0; i < this.population.length; i++) {
let genome = this.population[i];
value += genome.score + minimalFitness;
if (random < value) return genome;
}
// if all scores equal, return random genome
return this.population[Math.floor(Math.random() * this.population.length)];
case selection.TOURNAMENT:
if (this.selection.size > this.population_size) {
throw new Error('Your tournament size should be lower than the population size, please change methods.selection.TOURNAMENT.size');
}
// Create a tournament
let individuals = [];
for (i = 0; i < this.selection.size; i++) {
let random = this.population[Math.floor(Math.random() * this.population.length)];
individuals.push(random);
}
// Sort the tournament individuals by score
individuals.sort(function (a, b) {
return b.score - a.score;
});
// Select an individual
for (i = 0; i < this.selection.size; i++) {
if (Math.random() < this.selection.probability || i === this.selection.size - 1) {
return individuals[i];
}
}
}
},
test: function(dataset, options) {},
/**
* @typedef {Object} state
* @prop {number[]} state.information
* @prop {number[]} state.action
* @prop {number[]} state.reward
*/
/**
* @typedef {Object} data
* @prop {number[]} data.input
* @prop {number[]} data.output
*/
/**
* @typedef {data[]|state[]} Dataset
*/
/**
* @typedef {Object} EvolutionaryPeriod
* @prop {number} generations
* @prop {number} time
* @prop {number} error
* @prop {Object} networks
* @prop {Network} networks.best
* @prop {Network} networks.worst
* @prop {Network[]} networks.all
*/
/**
* @param {number} [options.error=0.1] Target error for networks in population - _`evolve()` will stop running when ONE network reaches `options.error`, or `options.iterations` is reached_
* @param {number} [options.iterations] Maximum number of generations over which population evolves - _`evolve()` will stop running if `options.error` is reached before `options.iterations`_
* @param {number} [options.elitism=0.05] Percentage of networks copied into next generations (epoch) - _larger `elitism` leads to **faster learning**, but **lower accuracy**_
* @param {number} [options.provenance=0] Percentage of "stem networks" (`this.template`) introduced into new generations (epoch) - _larger `provenance` leads to **slower learning**, but **higher accuracy**_
* @param {number} [options.mutation.rate=0.05] Percentage of networks genomically mutated (changed) per generation (epoch)
* @param {number} [options.mutation.amount=1] Number of genetic mutations (changes) introduced into mutated networks per generation (epoch)
* @param {Array<methods.mutation>} [options.mutation.methods=methods.mutation.FFW] List of allowed genetic mutations (changes) to a network per generation (epoch)
* @param {boolean} [options.networks.train=false] Trains every network in the populus, every generation - _VERY SLOW_
* @param {boolean} [options.networks.evolve=false] Evolves every network in the populus, every generation - _VERY SLOW_
* @param {number} [options.nodes.max=Infinity] Maximum number of nodes allowed in a network
* @param {number} [options.nodes.min=0] Minimum number or nodes allowed in a network
* @param {number} [options.connections.max=Infinity] Maximum number of connections allowed in a network
* @param {number} [options.connections.min=0] Minimum number or connections allowed in a network
* @param {number} [options.gates.max=Infinity] Maximum number of gates allowed in a network
* @param {number} [options.gates.min=0] Minimum number or gates allowed in a network
* @param {number} [options.log] Will log the populations performance every `options.log` generations as it evolves - _`options.log = true` will default to `options.log = 1`_
* @param {number} [options.threads] Specifies the number of threads that can be used to train/evolve networks/populations - _defaults to the number of CPU cores
* @param {number} [options.schedule.iterations] Frequency with which `options.schedule.function` is run
* @param {Function} [options.schedule.function] A function that will run every `options.schedule.iterations`
*
* @returns {EvolutionaryPeriod}
*/
evolve: async function(dataset, options) {
let self = this;
// OVERLOADS: Population.prototype.evolve(options)
if(!options && _.isPlainObject(dataset)) {
options = dataset;
dataset = undefined;
}
// OVERLOADS: Population.prototype.evolve()
// OVERLOADS: Population.prototype.evolve(options)
// OVERLOADS: Population.prototype.evolve((undefined || null),options)
if(!dataset && self.dataset.length) {
dataset = self.dataset;
}
// CASE: No dataset; Can not train population.
else {
throw new ReferenceError(`Parameter "dataset" was not passed & "this.dataset" was not declared; can not train "Population" without parameter "dataset" or "this.dataset".`)
}
}
}
module.exports = Population;
