示例#1
0
文件: gene.go 项目: snyderep/genreco
func makeRandomPopulation(size int, accountId int64, originalPerson *database.Person) (pop *Population) {
	db := database.OpenDB()
	defer db.Close()

	pop = &Population{}

	genomes := make([]*Genome, size)

	for i := 0; i < size; i++ {
		// seed with the original person
		persMap := make(map[string]*database.Person)
		persMap[originalPerson.MonetateId] = originalPerson

		// seed with the original person's products
		prodMap := make(map[string]*database.Product)
		// TODO: Don't requery for every genome
		products := database.QueryProductsViewedAndPurchased(db, accountId, originalPerson)
		for i := 0; i < len(products); i++ {
			prodMap[products[i].Pid] = products[i]
		}

		rs := &RecoSet{products: prodMap, people: persMap}
		genome := &Genome{rs: rs, score: 0.0}
		genome.addRandomTrait()
		genomes[i] = genome
	}

	pop.genomes = genomes

	return
}
示例#2
0
文件: gene.go 项目: snyderep/genreco
func (pop *Population) evolve(maxPopulation int, maxGenerations int, accountId int64,
	originalPerson *database.Person) {

	db := database.OpenDB()
	defer db.Close()

	for g := 0; g < maxGenerations; g++ {
		fmt.Printf("processing generation %d\n", g)

		ch := make(chan bool)
		for i := 0; i < len(pop.genomes); i++ {
			go func(ch chan bool, genome *Genome) {
				// apply the update of the last (current) trait a genome
				genome.getCurrentTrait().update(db, genome.rs, accountId, originalPerson)
				genome.checkFitness(db, accountId, originalPerson)

				ch <- true
			}(ch, pop.genomes[i])
		}
		// drain the channel
		for i := 0; i < len(pop.genomes); i++ {
			<-ch
		}

		pop.display()

		if g == (maxGenerations - 1) {
			pop.displayFinal()
		} else {
			// select genomes to carry forward to the next generation
			pop.makeSelection()

			// add new traits to the surviving genomes
			for i := 0; i < len(pop.genomes); i++ {
				pop.genomes[i].addRandomTrait()
			}

			// have the successful ones reproduce to fill out the remainder of the population
			childrenGenomes := make([]*Genome, 0)
			for i := 0; i < (maxPopulation - len(pop.genomes)); i++ {
				r1 := rand.Intn(len(pop.genomes))
				r2 := rand.Intn(len(pop.genomes))
				newGenome := reproduce(pop.genomes[r1], pop.genomes[r2])
				childrenGenomes = append(childrenGenomes, newGenome)
			}
			pop.appendGenomes(childrenGenomes)
		}
	}
}