def main():
parser = OptionParser()
parser.add_option("-n", "--ngram", dest="ngram",default="undefined")
parser.add_option("-g", "--ga", dest="ga", type="int", default=800)
(options, args) = parser.parse_args()
ga.run(ngram_generate=(lambda: ngram.generate(options.ngram,TWELVE_BAR_BLUES)),num_iter=options.ga)
def main():
parser = OptionParser()
parser.add_option("-n", "--ngram", dest="ngram", default="undefined")
parser.add_option("-g", "--ga", dest="ga", type="int", default=800)
(options, args) = parser.parse_args()
ga.run(ngram_generate=(
lambda: ngram.generate(options.ngram, TWELVE_BAR_BLUES)),
num_iter=options.ga)
def main():
graph = data.generate()
batches = [
Batch(graph, ga.Params(200, 40, 0.4, 0.5, 0.1)),
Batch(graph, ga.Params(500, 100, 0.4, 0.5, 0.1)),
Batch(graph, ga.Params(1000, 200, 0.4, 0.5, 0.1)),
]
for batch in tqdm(batches):
graph = batch.graph
params = batch.params
params_hash = __batch_as_hash(params)
logs_dir = LOGS.joinpath(params_hash)
with Logger(logs_dir) as logger:
ga_stats = ga.run(graph, params)
worst = []
mean = []
best = []
for epoch_stats in ga_stats:
worst.append(np.min(epoch_stats.scores))
mean.append(np.mean(epoch_stats.scores))
best.append(np.max(epoch_stats.scores))
summary = {}
summary['worst'] = worst
summary['mean'] = mean
summary['best'] = best
data_frame = pd.DataFrame.from_dict(summary)
logger.save_csv(data_frame, 'summary')
logger.add_entries(params.as_dict())
return sum
# Problem Definition
problem = structure()
problem.func = f
problem.nvar = len(cities)
problem.varmin = [c[1] for c in cities]
problem.varmax = [c[2] for c in cities]
# Parameters
params = structure()
params.maxit = 100
params.npop = 50
params.beta = 1
params.pc = 1
params.gamma = 0.1
params.mu = 0.01
params.sigma = 0.1
# Run GA
out = ga.run(problem, params, verbose=True)
# Results
print("\nBest Assignment:\n")
best_assignment = out.bestsol.assignment
for i in range(len(best_assignment)):
print("\t {}: {}".format(cities[i][0], best_assignment[i]))
print("\nBest Reward: {}\n".format(out.bestsol.reward))
ga.plot(params.maxit, out.bestreward)
[26, 11, 29, 25, 43, 0, 49, 30, 19, 30],
[44, 51, 45, 27, 10, 49, 0, 42, 48, 35],
[5, 22, 3, 36, 43, 30, 42, 0, 13, 6],
[8, 8, 11, 33, 46, 19, 48, 13, 0, 16],
[9, 23, 9, 32, 37, 30, 35, 6, 16, 0]]
return cities
# Iniicializa a estrutura das cidades
cities = initialize()
# Parametros do algoritmo
config = structure()
#Tamanho da populacao
config.population_size = 20
# Numero de iteracoes que iram ser repetidas
config.number_iterations = 1000
# Probabilidade de ocorrer uma mutacao em um dado gene dos cromossomos filhos
# (0 a 100)
config.mutation_probability = 3
# Numero que representa o total de ocorrencias aleatorias que ocorreram durante
# a selecao de quem ira ou nao continuar na populacao
# Quanto maior for o valor, mais eventos aleatorios acontecerao e em decorrencia
# disso mais aleatorio sera o resultado
# Quanto menor ele for definido maior sera as chances das novas populacoes serem
# escolhidas na selecao dos mais fortes e propensos a continuar a reproducao
config.cut_randomness = 30
#Inicia o GA
run(cities, config)
# Variables according to problem definition
problem = structure()
problem.costfunc = function
problem.nvar = 30 #30 var
problem.varmin = -32 # -32
problem.varmax = 32 # 32
# GA parameters
params = structure()
params.maxit = 100000 # max iterations number
params.accep = 0.01 # acceptance for stop the alg
params.npop = 20 # population number
params.pc = 1 # variation initial population
params.gamma = 0.1 # crossover parameter
params.sigma = 0.3 # mutation parameter
params.mu = 0.03 # mutation % genes
params.beta = 1 # selection for the rolette wheel
#Run GA
output = ga.run(problem, params)
# Results
plt.plot(output.bestcost, 'b', linewidth=3)
plt.semilogy(output.bestcost)
plt.xlim(0, output.it)
plt.xlabel('Iterations')
plt.ylabel('Output')
plt.title('Genetic Algorithm (GA)')
plt.grid(True)
plt.show()
size_features: int = train_x.size(1)
size_labels: int = int(train_y.max().item() - train_y.min().item()) + 1
population, logbook = ga.run(
x=train_x,
y=train_y,
list_sample_by_label=list_sample_by_label,
ratio_min=RATIO_MIN,
ratio_max=RATIO_MAX,
population_size=GA_POPULATION_SIZE,
selection_method=GA_SELECTION_METHOD,
crossover_method=GA_CROSSOVER_METHOD,
crossover_size=GA_CROSSOVER_SIZE,
mutation_method=GA_MUTATION_METHOD,
mutation_rate=GA_MUTATION_RATE,
replacement_method=GA_REPLACEMENT_METHOD,
num_generations=GA_NUM_GENERATIONS,
checkpoint_dir=GA_CHECKPOINT_DIR,
rand_seed=RAND_SEED,
verbose=VERBOSE,
classifier_num_hidden_layers=CLASSIFIER_NUM_HIDDEN_LAYERS,
classifier_batch_size=CLASSIFIER_BATCH_SIZE,
classifier_num_epochs=CLASSIFIER_NUM_EPOCHS,
classifier_run_device=CLASSIFIER_RUN_DEVICE,
classifier_learning_rate=CLASSIFIER_LEARNING_RATE,
classifier_beta_1=CLASSIFIER_BETA_1,
classifier_beta_2=CLASSIFIER_BETA_2)
if GA_CHECKPOINT_DIR is not None:
with open(os.path.join(GA_CHECKPOINT_DIR, "logbook.pkl"),
mode="wb") as fp:
params.npop = 50
params.beta = 1
params.pc = 1
params.gamma = pC
## mutation parameters
params.mu = pM
params.sigma = 0.1
x = 0
max = []
min = []
avg = []
while x < trial_count:
#Run GA
out = ga.run(problem, params, "classic")
max.append(np.max(out.bestcost))
min.append(np.min(out.worstcost))
avg.append(sum(out.bestcost) / len(out.bestcost))
print("Run " + str(x + 1) + " done")
x += 1
print("Max: " + str(np.max(max)))
print("Min: " + str(np.min(min)))
print("Average: " + str(sum(avg) / trial_count))
print(out.bestsol)
ChordProgToPic(out.bestsol)
midi.run(out.bestsol)
#play midi output
for x in range(15):
t = ga.DNAbase()
population.append(t)
ga = ga.GA(population, 1, eval_fun)
ga.ini()
plt.scatter([para_2_x(x.para[0]) for x in ga.population],
[x.score for x in ga.population],
c=(1, 1, 0),
s=50,
marker='*')
for x in range(7):
#fig=plt.figure()
#plt.subplot(111,axisbg=(0.5,0.5,0.5))
#plt.plot([x/10.0 for x in img_x],img_y,label="$xsin(3x)$",color=(1,0,0),linewidth=1)
#plt.scatter([para_2_x(x.para[0]) for x in ga.population],[x.score for x in ga.population],c=(1,1,0),s=50,marker='*')
ga.run()
print(x, end=' ') #代数
for y in ga.population:
print('%.3f' % y.score, end=',[')
for z in y.para:
print('%.2f' % para_2_x(z), end=',')
print('],', end='')
print()
#tmp=0
#for x in ga.population:
#tmp+=1
#xx=[para_2_x(y[0][0]) for y in x.test_rec]
#yy=[y[1] for y in x.test_rec]
#print(xx,yy)
#print(len(xx))
#color=((tmp & 0x04)>>2,(tmp & 2)>>1,tmp & 1)
import ga
if __name__ == '__main__':
POPULATION_SIZE = 5
MAX_ITER = 10
MUTATION_PROB = 10
ga.run(POPULATION_SIZE, MAX_ITER, MUTATION_PROB)