def cross_prob(ind_a, ind_b, mate_prob):
new_ind_a = []
new_ind_b = []
for k in range(0,len(ind_a)):
if rand() < mate_prob:
new_ind_a.append(ind_b[k])
else:
new_ind_a.append(ind_a[k])
if rand() < mate_prob:
new_ind_b.append(ind_a[k])
else:
new_ind_b.append(ind_b[k])
return (creator.individual(new_ind_a),creator.individual(new_ind_b))
def generate_individual(creator, number_endmembers, number_pixels, number_rows,
number_columns):
individual = numpy.random.choice(list(range(0, number_pixels)),
number_endmembers)
return creator.individual(individual)
def gaussian_mutation(individual, toolbox, number_rows, number_columns):
gene_x = (np.array(individual) / number_rows).astype(int)
gene_y = (np.array(individual) % number_rows)
mut_x = abs(toolbox.gaussian_mutation_op(gene_x.copy())[0] % number_columns-1)
mut_y = abs(toolbox.gaussian_mutation_op(gene_y.copy())[0] % number_rows-1)
mutant = mut_x*number_rows+mut_y
return creator.individual(mutant)
def cross_hadamard(ind_a, ind_b, mate_prob, virtual_prob):
new_ind_a = []
new_ind_b = []
for k in range(0,len(ind_a)):
if rand() < mate_prob:
if rand() < virtual_prob:
new_ind_a.append((ind_a[k][0],ind_b[k][1]))
else:
new_ind_a.append(ind_b[k])
else:
new_ind_a.append(ind_a[k])
if rand() < mate_prob:
if rand() < virtual_prob:
new_ind_b.append((ind_b[k][0],ind_a[k][1]))
else:
new_ind_b.append(ind_a[k])
else:
new_ind_b.append(ind_b[k])
return (creator.individual(new_ind_a),creator.individual(new_ind_b))
def GAEEII_IVFm(data, dimensions, number_endmembers):
start_time = time.time()
number_rows = int(dimensions[0])
number_columns = int(dimensions[1])
number_bands = int(dimensions[2])
number_pixels = number_rows * number_columns
sigma_MAX = max(number_rows, number_generations)
data_proj = numpy.asarray(affine_projection(data, number_endmembers))
creator.create("max_fitness", base.Fitness, weights=(-1.0, -1.0))
creator.create("individual", list, fitness=creator.max_fitness)
toolbox = base.Toolbox()
toolbox.register("create_individual", generate_individual, creator,
number_endmembers, number_pixels, number_rows,
number_columns)
toolbox.register("initialize_population", tools.initRepeat, list,
toolbox.create_individual)
toolbox.register("evaluate_individual",
multi_fitness,
data=data,
data_proj=data_proj,
number_endmembers=number_endmembers)
toolbox.register("cross_twopoints", tools.cxTwoPoint)
toolbox.register("selNSGA2", tools.selNSGA2)
toolbox.register("gaussian_mutation_op",
tools.mutGaussian,
mu=0,
sigma=0,
indpb=mutation_probability)
toolbox.register("gaussian_mutation",
gaussian_mutation,
toolbox=toolbox,
number_rows=number_rows,
number_columns=number_columns)
toolbox.register("random_mutation",
random_mutation,
number_pixels=number_pixels,
number_endmembers=number_endmembers,
mutation_probability=mutation_probability)
population = toolbox.initialize_population(n=population_size)
population_fitnesses = [
toolbox.evaluate_individual(individual) for individual in population
]
for individual, fitness in zip(population, population_fitnesses):
individual.fitness.values = fitness
hof = tools.HallOfFame(3)
hof.update(population)
current_generation = 0
current_sigma = sigma_MAX
generations_fitness_1 = []
generations_fitness_2 = []
generations_population = []
stop_criteria = deque(maxlen=5)
stop_criteria.extend([1, 2, 3, 4, 5])
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean, axis=0)
stats.register("std", numpy.std, axis=0)
stats.register("min", numpy.min, axis=0)
stats.register("max", numpy.max, axis=0)
logbook = tools.Logbook()
logbook.header = "gen", "evals", "std", "min", "avg", "max"
record = stats.compile(population)
logbook.record(gen=0, evals=len(population), **record)
while current_generation < number_generations and numpy.var(
numpy.array(stop_criteria)) > 0.000001:
toolbox.unregister("gaussian_mutation_op")
toolbox.register("gaussian_mutation_op",
tools.mutGaussian,
mu=0,
sigma=current_sigma,
indpb=mutation_probability)
offspring = tools.selRandom(population, k=int(population_size / 2))
offspring = list(map(toolbox.clone, offspring))
# Crossing
for child_1, child_2 in zip(offspring[::2], offspring[1::2]):
if random.random() < crossing_probability:
toolbox.cross_twopoints(child_1, child_2)
del child_1.fitness.values
del child_2.fitness.values
# Mutation
for mutant in offspring:
if random.random() < mutation_probability:
toolbox.gaussian_mutation(mutant)
del mutant.fitness.values
# Fitness
offspring_fitnesses = [
toolbox.evaluate_individual(individual) for individual in offspring
]
for individual, fitness in zip(offspring, offspring_fitnesses):
individual.fitness.values = fitness
# In Vitro Fertilization Module
ivfoffspring = list(map(toolbox.clone, population))
ivffits = [ind.fitness.values[0] for ind in ivfoffspring]
fatheridx = numpy.argmax(ivffits)
# fatherfit = numpy.max(ivffits)
father = creator.individual(ivfoffspring[fatheridx].copy())
for ind in ivfoffspring[::2]:
toolbox.random_mutation(ind)
del ind.fitness.values
for child1 in ivfoffspring:
child2 = creator.individual(father.copy())
toolbox.cross_twopoints(child1, child2)
del child1.fitness.values
del child2.fitness.values
ivffitnesses = [
toolbox.evaluate_individual(ind) for ind in ivfoffspring
]
for ind, fit in zip(ivfoffspring, ivffitnesses):
ind.fitness.values = fit
popmax = max(offspring_fitnesses)
for ind in ivfoffspring:
if (ind.fitness.values >= popmax):
population.append(ind)
new_population = population + offspring
# Selection
selected = toolbox.selNSGA2(new_population, population_size)
selected = list(map(toolbox.clone, selected))
population[:] = selected
hof.update(population)
record = stats.compile(population)
logbook.record(gen=current_generation, evals=len(population), **record)
# print(logbook.stream)
# Statistics
fits_1 = [ind.fitness.values[0] for ind in population]
fits_2 = [ind.fitness.values[1] for ind in population]
mean_1_offspring = sum(fits_1) / len(population)
mean_2_offspring = sum(fits_2) / len(population)
generations_fitness_1.append(numpy.log10(mean_1_offspring))
generations_fitness_2.append(numpy.log(mean_2_offspring))
stop_criteria.append(numpy.log10(mean_1_offspring))
generations_population.append(population.copy())
current_generation += 1
current_sigma = sigma_MAX / ((current_generation + 1) / 1.5)
best_individual = tools.selNSGA2(population, 1)[0]
# print('Result:', multi_fitness(best_individual,data, data_proj, number_endmembers))
M = data[:, best_individual]
duration = time.time() - start_time
return M, duration, [
generations_fitness_1, generations_fitness_2, generations_population,
current_generation
]
def GAEEII(data, dimensions, number_endmembers):
start_time = time.time()
population_size = 10
number_generations = 100
crossing_probability = 1
mutation_probability = 0.5
stop_criteria_MAX = 20
random.seed(64)
number_endmembers = number_endmembers
number_rows = int(dimensions[0])
number_columns = int(dimensions[1])
number_bands = int(dimensions[2])
number_pixels = number_rows * number_columns
sigma_MAX = max(number_rows, number_generations)
data_proj = numpy.asarray(affine_tranform(data, number_endmembers))
# data = numpy.asarray(data)
# _coeff, score, _latent = princomp(data.T)
# data_proj = numpy.squeeze(score[0:number_endmembers,:])
creator.create("min_fitness", base.Fitness, weights=(1.0, ))
creator.create("individual", list, fitness=creator.min_fitness)
toolbox = base.Toolbox()
toolbox.register("create_individual", generate_individual, creator,
number_endmembers, number_pixels, number_rows,
number_columns)
toolbox.register("initialize_population", tools.initRepeat, list,
toolbox.create_individual)
toolbox.register("evaluate_individual",
multi_fitness,
data=data,
data_proj=data_proj,
number_endmembers=number_endmembers)
toolbox.register("cross_twopoints", tools.cxTwoPoint)
toolbox.register("selNSGA2", tools.selNSGA2)
toolbox.register("gaussian_mutation_op",
tools.mutGaussian,
mu=0,
sigma=0,
indpb=mutation_probability)
toolbox.register("gaussian_mutation",
gaussian_mutation,
toolbox=toolbox,
number_rows=number_rows,
number_columns=number_columns)
population = toolbox.initialize_population(n=population_size)
ensemble_pop = []
for i in range(0, 5):
[_, duration, other] = classical.VCA(data, dimensions,
number_endmembers)
# print(other[0])
# print('Time GAEE:',duration)
ensemble_pop.append(creator.individual(other[0]))
population[5:] = ensemble_pop
population_fitnesses = [
toolbox.evaluate_individual(individual) for individual in population
]
for individual, fitness in zip(population, population_fitnesses):
individual.fitness.values = fitness
hof = tools.HallOfFame(3)
hof.update(population)
current_generation = 0
current_sigma = sigma_MAX
generations_fitness_1 = []
generations_fitness_2 = []
generations_population = []
stop_criteria = deque(maxlen=stop_criteria_MAX)
stop_criteria.extend(list(range(1, stop_criteria_MAX)))
while current_generation < number_generations and numpy.var(
numpy.array(stop_criteria)) > 0.000001:
toolbox.unregister("gaussian_mutation_op")
toolbox.register("gaussian_mutation_op",
tools.mutGaussian,
mu=0,
sigma=current_sigma,
indpb=mutation_probability)
offspring = tools.selRandom(population, k=int(population_size / 2))
offspring = list(map(toolbox.clone, offspring))
# Crossing
for child_1, child_2 in zip(offspring[::2], offspring[1::2]):
if random.random() < crossing_probability:
toolbox.cross_twopoints(child_1, child_2)
del child_1.fitness.values
del child_2.fitness.values
# Mutation
for mutant in offspring:
if random.random() < mutation_probability:
toolbox.gaussian_mutation(mutant)
del mutant.fitness.values
# Fitness
offspring_fitnesses = [
toolbox.evaluate_individual(individual) for individual in offspring
]
for individual, fitness in zip(offspring, offspring_fitnesses):
individual.fitness.values = fitness
new_population = population + offspring
# Selection
selected = toolbox.selNSGA2(new_population, population_size)
selected = list(map(toolbox.clone, selected))
population[:] = selected
hof.update(population)
# Statistics
fits_1 = [ind.fitness.values[0] for ind in population]
# fits_2 = [ind.fitness.values[1] for ind in population]
mean_1_offspring = sum(fits_1) / len(population)
# mean_2_offspring = sum(fits_2) / len(population)
generations_fitness_1.append(numpy.log10(mean_1_offspring))
# generations_fitness_2.append(numpy.log(mean_2_offspring))
generations_population.append(population.copy())
stop_criteria.append(numpy.log10(mean_1_offspring))
current_generation += 1
current_sigma = sigma_MAX / ((current_generation + 1) / 4)
best_individual = tools.selBest(population, 1)[0]
# best_individual = hof[0]
# print('Result:', multi_fitness(best_individual,data, data_proj,number_endmembers))
M = data[:, best_individual]
duration = time.time() - start_time
return M, duration, [
generations_fitness_1, generations_fitness_2, generations_population,
current_generation
]
def GAEE_IVFm(data, dimensions, number_endmembers):
start_time = time.time()
number_rows = int(dimensions[0])
number_columns = int(dimensions[1])
number_bands = int(dimensions[2])
number_pixels = number_rows * number_columns
tournsize = 3
data = numpy.asarray(data)
_coeff, score, _latent = princomp(data.T)
data_pca = numpy.squeeze(score[0:number_endmembers - 1, :])
creator.create("max_fitness", base.Fitness, weights=(1.0, ))
creator.create("individual", list, fitness=creator.max_fitness)
toolbox = base.Toolbox()
toolbox.register("create_individual", generate_individual, creator,
number_endmembers, number_pixels, number_rows,
number_columns)
toolbox.register("initialize_population", tools.initRepeat, list,
toolbox.create_individual)
toolbox.register("evaluate_simplex_volume",
mono_fitness,
data_pca=data_pca,
number_endmembers=number_endmembers)
toolbox.register("cross_twopoints", tools.cxTwoPoint)
toolbox.register("tournament_select",
tools.selTournament,
tournsize=tournsize)
toolbox.register("random_mutation",
random_mutation,
number_pixels=number_pixels,
number_endmembers=number_endmembers,
mutation_probability=mutation_probability)
population = toolbox.initialize_population(n=population_size)
population_fitnesses = [
toolbox.evaluate_simplex_volume(individual)
for individual in population
]
for individual, fitness in zip(population, population_fitnesses):
individual.fitness.values = fitness
current_generation = 0
generations_fitness = []
generations_population = []
stop_criteria = deque(maxlen=5)
stop_criteria.extend([1, 2, 3, 4, 5])
while current_generation < number_generations and numpy.var(
numpy.array(stop_criteria)) > 0.000001:
offspring = list(map(toolbox.clone, population))
# Crossing
for child_1, child_2 in zip(offspring[::2], offspring[1::2]):
if random.random() < crossing_probability:
toolbox.cross_twopoints(child_1, child_2)
del child_1.fitness.values
del child_2.fitness.values
# Mutation
for mutant in offspring:
if random.random() < mutation_probability:
toolbox.random_mutation(mutant)
del mutant.fitness.values
# Fitness
offspring_fitnesses = [
toolbox.evaluate_simplex_volume(individual)
for individual in offspring
]
for individual, fitness in zip(offspring, offspring_fitnesses):
individual.fitness.values = fitness
# In Vitro Fertilization Module
ivfoffspring = list(map(toolbox.clone, population))
ivffits = [ind.fitness.values[0] for ind in ivfoffspring]
fatheridx = numpy.argmax(ivffits)
fatherfit = numpy.max(ivffits)
father = creator.individual(ivfoffspring[fatheridx].copy())
for ind in ivfoffspring[::2]:
toolbox.random_mutation(ind)
del ind.fitness.values
for child1 in ivfoffspring:
child2 = creator.individual(father.copy())
toolbox.cross_twopoints(child1, child2)
del child1.fitness.values
del child2.fitness.values
ivffitnesses = [
toolbox.evaluate_simplex_volume(ind) for ind in ivfoffspring
]
for ind, fit in zip(ivfoffspring, ivffitnesses):
ind.fitness.values = fit
popmax = max(offspring_fitnesses)
for ind in ivfoffspring:
if (ind.fitness.values >= popmax):
population.append(ind)
new_population = population + offspring
# Selection
selected = toolbox.tournament_select(new_population, population_size)
population_selected = list(map(toolbox.clone, selected))
population[:] = population_selected
# Statistics
fits = [ind.fitness.values[0] for ind in population]
mean_offspring = sum(fits) / len(population)
generations_fitness.append(numpy.log10(mean_offspring))
generations_population.append(population.copy())
stop_criteria.append(numpy.log10(mean_offspring))
current_generation += 1
best_individual = tools.selBest(population, 1)[0]
# print(best_individual)
M = data[:, best_individual]
duration = time.time() - start_time
return M, duration, [
generations_fitness, generations_population, current_generation
]
