def pso(population,x_train, y_train, x_test, y_test,x_validate,y_validate):
# max_velocity = 0
c1 = 1
w, h = 20, 1200;
velocity = [[0 for x in range(h)] for y in range(w)]
present_positions = [[0 for x in range(h)] for y in range(w)]
best_positions = [[0 for x in range(h)] for y in range(w)]
best_lfitness = [0]*20
atual_fitness = [0]*20
gbest = ft.find_best(population, x_train, y_train, x_validate, y_validate)
num_generations = 50
for k in range(num_generations):
lbest = ft.find_best(population,x_train, y_train, x_validate, y_validate)
if lbest[1][0] > gbest[1][0]:
gbest = lbest
for particleIdx in range(len(population)):
velocity[particleIdx] = a(velocity[particleIdx], lbest, c1,present_positions[particleIdx], gbest)
present_positions[particleIdx] = b(present_positions[particleIdx],velocity[particleIdx])
"""
for dimentionIdx in range(1200):
print(velocity[particleIdx][dimentionIdx])
velocity[particleIdx][dimentionIdx] = a(velocity[particleIdx][dimentionIdx], gbest,c1,present_positions[particleIdx][dimentionIdx])
present_positions[particleIdx][dimentionIdx] = b(present_positions[particleIdx][dimentionIdx], velocity[particleIdx][dimentionIdx])
"""
best_accu = ft.fitness_best(gbest[0], gbest[1], x_test, y_test)
# print(best_accu)
return best_accu
def ga(population, x_train, y_train, x_test, y_test):
new_population = []
parents_mated = []
num_generations = 5
for i in range(num_generations):
fitness_all = ft.calculate_pop_ft(population, x_train, y_train, x_test,
y_test)
for j in range(int((len(population) / 2))):
parents = mating_pool(population, fitness_all, 2)
offspring = crossover(parents, 3)
offspring = mutation(offspring)
new_population.append(offspring)
parents_mated.append(parents[0])
parents_mated.append(parents[1])
new_population = np.concatenate(
(new_population, parents_mated[:int((len(population) / 2))]))
population = new_population
#------- Clean aux Lists------
parents_mated = []
new_population = []
best = ft.find_best(population, x_train, y_train, x_test, y_test)
return best
def es(population, x_train, y_train, x_validate, y_validate, x_test, y_test):
mut = 0.3
#cross = 0.2
best_fit = ft.find_best(population, x_train, y_train, x_validate,
y_validate)
num_generations = 50
decendants = []
for i in range(num_generations):
fitness_all, models_all = ft.calculate_pop_ft(population, x_train,
y_train, x_validate,
y_validate)
number_decendants = 0
decendants = []
while number_decendants < (len(population) + 5):
#print(population)
check = 1
while check:
father_idx = np.random.random_integers(0, len(population) - 1)
father = population[father_idx]
father_ft = fitness_all[father_idx]
radom_gauss = random.gauss(0, 1)
father_prob = probability_sum(father_ft, fitness_all)
if radom_gauss < father_prob:
check = 0
check2 = 1
while check2:
mother_idx = np.random.random_integers(0, len(population) - 1)
mother = population[mother_idx]
mother_ft = fitness_all[mother_idx]
radom_gauss = random.gauss(0, 1)
mother_prob = probability_sum(mother_ft, fitness_all)
if radom_gauss < mother_prob:
check2 = 0
crossed = crossover(father, mother)
mutated = mutation(crossed, mut)
#print(crossed)
decendants.append(mutated)
number_decendants += 1
ft_decendants, model_decendants = ft.calculate_pop_ft(
decendants, x_train, y_train, x_validate, y_validate)
new_population, ft_new_pop, model_new = ft.fetch_n_better(
decendants, ft_decendants, model_decendants, len(population))
ft_best = (new_population[0], (ft_new_pop[0], model_new[0]))
population = new_population
if ft_best[1][0] > best_fit[1][0]:
best_fit = ft_best
#
ee_accu = ft.fitness_best(best_fit[0], best_fit[1][1], x_test, y_test)
return ee_accu
def ga(population,x_train, y_train,x_validate, y_validate, x_test, y_test):
mut = 0.3
cross = 0.7
best = ft.find_best(population, x_train, y_train, x_validate, y_validate)
#treshold = 0.5
new_population = []
#parents_mated = []
num_generations = 50
for k in range(num_generations):
fitness_all, models_all = ft.calculate_pop_ft(population,x_train, y_train,x_validate, y_validate)
for j in range(int((len(population)/2))):
parents =roulette_wheel_selection(fitness_all,2,len(population))
parents = (population[parents[0]],population[parents[1]])
offspring = crossover(parents[0],parents[1],cross)
offspring2 = crossover(parents[0],parents[1],cross)
offspring = mutation(offspring,mut)
offspring2 = mutation(offspring2,mut)
new_population.append(offspring)
new_population.append(offspring2)
#parents_mated.append(parents[0])
#parents_mated.append(parents[1])
#new_population = np.concatenate((new_population,parents_mated[:int((len(population)/2))]))
population = new_population
gen_best = ft.find_best(population, x_train, y_train, x_validate, y_validate)
if gen_best[1][0] > best[1][0]:
best = gen_best
#------- Clean aux Lists------
#parents_mated = []
new_population = []
#best = ft.find_best(population,x_train, y_train, x_test, y_test)
best = ft.fitness_best(best[0],best[1][1],x_test,y_test)
return best
def bfo(population, x_train, y_train, x_test, y_test, x_validate, y_validate):
#print("BFO")
Celln = 20
Ned = 10
Nre = 10
Nc = 20
Ns = 10
Ped = 0.25
StepSize = 0.1
best_fit = ft.find_best(population, x_train, y_train, x_validate,
y_validate)
for l in range(Ned):
for k in range(Nre):
for j in range(Nc):
population, cellHealth_all = chemotaxixAndSwim(
population, 1200, 20, Ns, StepSize, x_train, y_train,
x_validate, y_validate)
local_best = ft.find_best(population, x_train, y_train,
x_validate, y_validate)
if local_best[1][0] > best_fit[1][0]:
best_fit = local_best
#DUVIDA DEVERIA USAR O HEALTH AO INVES DO FITNESS AQUI?
all_fitness, all_models = ft.calculate_pop_ft(
population, x_train, y_train, x_validate, y_validate)
new_pop, ft_new, model_new = ft.fetch_n_better(
population, all_fitness, all_models, 10)
#DIVIDA COMO GARANTIR QUE NO FINAL A POPULAÇÃO VAI TER 20 BACTERIAS???
for cell in new_pop:
if (rd.uniform(0, 1) >= Ped):
cell = np.random.uniform(low=0, high=1.0, size=1200)
new_pop.append(cell)
population = new_pop
bfo_accu = ft.fitness_best(best_fit[0], best_fit[1][1], x_test, y_test)
return bfo_accu
def es(population, x_train, y_train, x_validate, y_validate, x_test, y_test):
mut = 0.3
cross = 0.7
#fitness_all = ft.calculate_pop_ft(population, x_train, y_train, x_test, y_test)
best_fit = ft.find_best(population, x_train, y_train, x_validate,
y_validate)
num_generations = 50
decendants = []
for i in range(num_generations):
number_decendants = 0
decendants = []
while number_decendants < (len(population) + 5):
#print(population)
father = np.random.random_integers(0, len(population) - 1)
mother = np.random.random_integers(0, len(population) - 1)
mother2 = np.random.random_integers(0, len(population) - 1)
father = population[father]
mother = population[mother]
mother2 = population[mother2]
monft = ft.fitness(mother, x_train, y_train, x_validate,
y_validate)
monft2 = ft.fitness(mother2, x_train, y_train, x_validate,
y_validate)
if monft[0] < monft2[0]:
mother = mother2
crossed = crossover(father, mother, cross)
mutated = mutation(crossed, mut)
#print(crossed)
decendants.append(mutated)
number_decendants += 1
ft_decendants, model_decendants = ft.calculate_pop_ft(
decendants, x_train, y_train, x_validate, y_validate)
new_population, ft_new_pop, model_new = ft.fetch_n_better(
decendants, ft_decendants, model_decendants, len(population))
ft_best = (new_population[0], (ft_new_pop[0], model_new[0]))
population = new_population
if ft_best[1][0] > best_fit[1][0]:
best_fit = ft_best
ee_accu = ft.fitness_best(best_fit[0], best_fit[1][1], x_test, y_test)
return ee_accu