train = False
env = gym.make("gym-snake-v0")
# env = ObservationWrapper(env)
env.set_params(
reward=(0, 0, 1, -1),
obs="simple",
size=15,
termination=75,
spawn="random",
add_len=1,
start_length=3,
)
model = nn.Sequential(nn.Linear(10, 24, bias=False), nn.ReLU(),
nn.Linear(24, 4, bias=False))
if train:
r = ContinuousRepresentation(-1, 1, getNumWeights(model))
ga = GA(r, lambda x: objective(env, model, x), debug=1)
ga.initPopulation(300)
ga.evolve(400)
best_weights = ga.getSolution()[1]
for i in range(5):
objective(env, model, best_weights, render=True)
# save model
np.save("experiments/GA/weights_2layer.npy", best_weights)
else:
weights = np.load("experiments/GA/weights_2layer.npy")
for i in range(10):
objective(env, model, weights, render=True)
import matplotlib.pyplot as plt
import numpy as np
from GA import GA
from TSP import TSP
N_CITIES = 20 # DNA size
CROSS_RATE = 0.1
MUTATE_RATE = 0.02
POP_SIZE = 500
N_GENERATIONS = 500
ga = GA(DNA_size=N_CITIES,
pcross=CROSS_RATE,
pmutation=MUTATE_RATE,
pop_size=POP_SIZE)
env = TSP(N_CITIES)
for gen in range(N_GENERATIONS):
lx, ly = ga.translateDNA(ga.pop, env.city_position)
fitness, total_distance = ga.get_fitness(lx, ly)
ga.evolve(fitness)
best_idx = np.argmax(fitness) #返回最大值的索引
print(
'Gen:',
gen,
'| best fit: %.2f' % fitness[best_idx],
)
env.plotting(lx[best_idx], ly[best_idx], total_distance[best_idx])
#绘制出每一代的最优路径
plt.ioff()
plt.show()
if loadit:
docs = pickle.load(open('docs.pkl', 'rb'))
else:
data_path = '../data/github_issues.csv'
df = pd.read_csv(data_path)
docs = Documents()
# docs.load(list(df['description'])[:300])
docs.load(list(df['description']))
docs.vectorise()
pickle.dump(docs, open('docs.pkl', 'wb+'))
print("No. of documents loaded: {}".format(docs.get_doc_size()))
corpus = docs.get_vectors()
dictionary = docs.get_dictionary()
GA = GA(corpus,
dictionary,
pop_size=30,
fitness_budget=10000,
objective='coherence')
GA.initialise_population()
GA.evolve()
fittest_gene = GA.get_fittest()
# model = GA.get_model()
# docs.assign_labels(model)
print('Fittest Gene discovered {} topics with score of {:.8f}'.format(
fittest_gene.n, GA.fitness))
# Start of GA configuration
ga = GA(Population_size, Chromosome_length, True)
# Set the GA Configuration
ga.initialization_impl = RandomInitialization()
ga.mutation_impl = PerGeneMutation(Mutation_rate)
ga.selection_impl = TournamentSelection()
ga.crossover_impl = FastSinglePointCrossover()
ga.termination_impl = GenerationTermination(Total_generations)
# ga.evaluation_impl = SumOfDiffEvaluation2(60)
ga.evaluation_impl = TestEvaluation()
ga.initialize()
while not ga.is_over():
ga.evolve()
best = ga.get_best()
print("Curent Trial " + str(Current_trial))
print("Generation #" + str(ga.generation))
print("\tBest Fitness = " + str(best[1]))
# ---------- Each Chromosome ---------- #
for i in range(len(ga.population)):
chromosome = ga.population[i]
# Get the fitness of the current chromosome
fitness = ga.get_fitness(chromosome)
# Add to Database list
chromosome_string = ' '.join(map(str, chromosome))
# Print Chromosome - Fitness
print("\t" + str(ga.population[i]) + " - " +
str(ga.get_fitness(ga.population[i])))
