nb_generations = 100
size_population = 100
# size_population = len(authors_features.keys())
population = tw.init_population_mask(size_population, len(base_vector), mask_chromosome, 500)
fitness_population = [float(f) for f in run_chromosomes(population, cmd, history)]
history = make_history(population, fitness_population)
for i in xrange(nb_generations) :
# print 'select'
selected = tw.selection(population, fitness_population)
#mutate
mutants = []
for chromosome in selected :
if random.randint(0,1) == 1 :
mutants.append(tw.mutate(chromosome, 500))
current = selected + mutants
#lobotomize
lobos = []
for chromosome in current :
if random.randint(0,1) == 0 :
lobos.append(tw.lobotomize(chromosome))
#trisofy
trisos = []
for chromosome in current :
if random.randint(0,1) == 0 :
trisos.append(tw.trisofy(chromosome))
size_population = 10
population = tw.init_population(size_population, len(base_vector))
fitness_population = [float(f) for f in run_chromosomes(population,cmd)]
# for chromosome in population :
# fitness_population.append(run_chromosome(chromosome, cmd))# tw.fitness(chromosome, base_vector, authors_features, authors_test))
print fitness_population
for i in xrange(nb_generations) :
selected = tw.selection(population, fitness_population)
#mutate
mutants = []
for chromosome in selected :
if random.randint(0,1) == 1 :
mutants.append(tw.mutate(chromosome))
#lobotomize
lobos = []
for chromosome in selected :
if random.randint(0,1) == 1 :
lobos.append(tw.lobotomize(chromosome))
#crossover
crossed = []
for chromosome1, chromosome2 in itertools.combinations(selected, 2) :
if random.randint(0,10) == 1 :
crossed.append(tw.crossover(chromosome1, chromosome2))
population = mutants + crossed + selected + lobos
# fitness_population = []
fitness_population = [float(f) for f in run_chromosomes(population,cmd)]
#crossover
crossed = []
for chromosome1, chromosome2 in itertools.combinations(selected, 2):
if random.randint(0, 10) == 1:
offspring1, offspring2 = tw.crossover2(chromosome1, chromosome2)
crossed.append(offspring1)
crossed.append(offspring2)
# crossed.append(tw.crossover(chromosome1, chromosome2))
current = selected + crossed
#mutate
mutants = []
for chromosome in current:
if random.randint(0, 1) == 1:
mutants.append(tw.mutate(chromosome))
#lobotomize
# lobos = []
# for chromosome in current :
# if random.randint(0,1) == 1 :
# lobos.append(tw.lobotomize(chromosome))
#trisofy
# trisos = []
# for chromosome in current :
# if random.randint(0,1) == 1 :
# trisos.append(tw.trisofy(chromosome))
population = selected + crossed + mutants # + lobos + trisos
# population = selected + mutants + crossed
population = tw.init_population_mask(size_population, len(base_vector),
mask_chromosome, 500)
fitness_population = [
float(f) for f in run_chromosomes(population, cmd, history)
]
history = make_history(population, fitness_population)
for i in xrange(nb_generations):
# print 'select'
selected = tw.selection(population, fitness_population)
#mutate
mutants = []
for chromosome in selected:
if random.randint(0, 1) == 1:
mutants.append(tw.mutate(chromosome, 500))
current = selected + mutants
#lobotomize
lobos = []
for chromosome in current:
if random.randint(0, 1) == 0:
lobos.append(tw.lobotomize(chromosome))
#trisofy
trisos = []
for chromosome in current:
if random.randint(0, 1) == 0:
trisos.append(tw.trisofy(chromosome))
