def main():
pop = init.initPop(300)
itera = 100 #迭代次数
each = 30 #每轮迭代的遗传计算次数
tplt = "{:8}\t{:8}\t{:8}"
gen = Genetic()
bests = [] #用以存储每代最优值
print(tplt.format("Gen", "Avg", "Best"))
for i in range(1, itera):
pop = gen.evaluate(pop)
bests.append(gen.printGen(pop, i))
for j in range(1, each):
rand = random.random()
if rand < 0.7: #变异率设为0.3
ind = gen.select(pop)
pop.remove(ind)
ind.mark = 2
ind.gene = gen.mutate(ind.gene, 4)
pop.append(ind)
else:
pop = gen.mate(pop)
res = numpy.array(bests)
x = numpy.arange(1, 99)
y = res[x]
pyplot.title('GP result')
pyplot.xlabel('Gen')
pyplot.ylabel('Best')
pyplot.plot(x, y)
pyplot.show()
def test_mutate():
g = Genetic()
for i in range(100):
a = Individual()
o = g.mutate(a)
assert not o.dna.__contains__(None)
def test_mutate2(self):
point, mutations = Genetic.mutate((1, 0), 0, 0.5, 0.5)
self.assertEqual((1, 0), point)
self.assertEqual(0, mutations)
def test_mutate(self):
point, mutations = Genetic.mutate((1, 0), 1, 0.5, 0.5)
self.assertNotEqual((1, 0), point)
self.assertEqual(2, mutations)
def main():
filename = newFile()
simu = []
for x in range(MAX_PARALLEL):
simu.append(Multi())
simu[-1].multi_start()
joints_seg = simu[0].parent_pipe.recv()
#simulation.setup()
#################### genetic
#fitness odleglosc od zrodka (x albo y) plus 10x za kazda noge na ziemi minus[kat podst - aktualny] - x3(xyx)
#10*sim_data[1][1] - (0 - abs(sim_data[2][1])) - (0 - abs(sim_data[2][2])) + [10*x for x in sim_data[0]]
genetica = Genetic(POPILATION_SIZE, CHROMOSOME_SIZE, GAIT_STEPS, MAX_MIN,
joints_seg, BEST_SAMPLES, LUCKY_FEW, TOURNAMENT,
MUTATION_CHANCE)
#resetBasePositionAndOrientation
population = []
population = genetica.gen_population()
#print(population)
#population[0] = convertToSim(ppp[0])
#population[1] = convertToSim(ppp[1])
pop_data = []
pop_fitness = [0, 0]
for generation in range(GENERATIONS):
print("Generation:",
str(generation) + " previous 2 best fitness: ",
str(pop_fitness[0]), str(pop_fitness[1]))
weighted_population = []
slicedd = slicePop(population)
#print(len(slicedd))
for slic in slicedd:
#print('.',end=" ",flush=True)
#sys.stdout.flush()
#sys.stdout.write('.')
#sys.stdout.flush()
for idx, individual in enumerate(slic):
#print('lel')
#print(individual)
#print('pop')
#print(len(population))
#print('simu')
#print(len(simu))
#print(idx)
simu[idx].indi = individual
simu[idx].parent_pipe.send(individual)
#print('lel2')
t = 0
while True:
#print('in loop1')
try:
for sim in simu:
#print('in loop1 lel')
if sim.simok == False:
rec = sim.parent_pipe.recv()
if rec == "simok":
sim.simok = True
t += 1
if t == MAX_PARALLEL:
t = 0
break
except:
continue
#print('lel3')
while True:
#print('in loop2')
try:
for sim in simu:
rec = sim.parent_pipe.recv()
contact, base_pos, base_angle = rec[-1]
if len(contact) == 6 and len(base_pos) == 3 and len(
base_angle) == 3:
sim.data = rec
sim.parent_pipe.send('dataok')
t += 1
if t == MAX_PARALLEL:
t = 0
break
except:
continue
for sim in simu:
sim.simok = False
sim.parent_pipe.send('reset')
#print('lel4')
while True:
#print('in loop3')
try:
for sim in simu:
rec = sim.parent_pipe.recv()
if rec == "resok":
t += 1
if t == MAX_PARALLEL:
t = 0
break
except:
continue
for sim in simu:
#fitness_val = genetica.fitness(sim.data[0],sim.data[1],sim.data[2])
dd = 0
for x in sim.data:
#print(dd)
#print(len(sim.data))
if x == sim.data[-1]:
dd += genetica.fitness(x[0], x[1], x[2])
else:
dd += genetica.stability(x[0], x[1], x[2])
fitness_val = dd
#print("lelelee")
#print(sim.indi)
individual = convertToGen(sim.indi)
#print("lvggjvjhg")
#print(len(individual))
pair = (individual, fitness_val)
weighted_population.append(pair)
population = []
#for x in range(GAIT_STEPS)
pop_sorted, pop_fitness = genetica.sort_pop(weighted_population)
#print(pop_sorted[0])
print(pop_fitness)
#saveToFile(str(generation)+" best fitness: " + str(pop_fitness[0]), [pop_sorted + pop_fitness])
saveToFile(
filename, "Generation: " + str(generation) + " best fitness: " +
str(pop_fitness[0]), [pop_fitness[:15], pop_sorted[:15]])
#print("saved to file")
#2 parents survive
a = convertToSim(pop_sorted[0])
b = convertToSim(pop_sorted[1])
population = [a, b]
#print("D-1")
#print(a)
#print("D0")
#print(b)
#print("D1")
#print(population)
#nn = [genetica.mutate(a,1,1), genetica.mutate(a,1,1), genetica.mutate(b,1,1)]
y = 0
while y < 2:
z = genetica.mutate(a, 1, 1)
if z not in population:
population.append(z)
y += 1
#print("D2")
#print(z)
y = 0
while y < 2:
z = genetica.mutate(b, 1, 1)
if z not in population:
population.append(z)
y += 1
#print("D3")
#print(z)
#population = [a, b, genetica.mutate(a,1,1), genetica.mutate(a,1,1), genetica.mutate(b,1,1)]
old_sel = genetica.select_population(pop_sorted)
#print("old_sel")
#print(old_sel[0])
#for ind in old_sel:
# population.append(convertToSim(ind))
#old_sel = genetica.tournamen(old_s el)
#print(len(old_sel[0]))
#print(old_sel)
children = genetica.get_children(old_sel, len(population))
#for x in range(TOURNAMENT):
# population.append(genetica.gen_chromosome())
for indiv in children:
#print("D4x")
#print(indiv)
while True:
#print('child loop2')
#print("D4445")
#print(indiv)
#print('size')
#print(len(indiv))
ind = convertToSim(indiv)
#print("D5")
#print(indiv)
mu = genetica.mutate(ind)
if mu not in population:
population.append(mu)
break
#print("lele2")
#print("population size")
#print(len(population))
#print(population)
print("end")
for sim in simu:
sim.multi_stop()
def main():
simu = []
for x in range(MAX_PARALLEL):
simu.append(Multi())
simu[-1].multi_start()
t=0
while not (t == MAX_PARALLEL):
for sim in simu:
if sim.parent_pipe.poll():
joints_seg = sim.parent_pipe.recv()
if len(joints_seg)==6:
t+=1
#simulation.setup()
#################### genetic
#fitness odleglosc od zrodka (x albo y) plus 10x za kazda noge na ziemi minus[kat podst - aktualny] - x3(xyx)
#10*sim_data[1][1] - (0 - abs(sim_data[2][1])) - (0 - abs(sim_data[2][2])) + [10*x for x in sim_data[0]]
genetica = Genetic(POPILATION_SIZE, CHROMOSOME_SIZE, GAIT_STEPS, MAX_MIN, joints_seg, BEST_SAMPLES, LUCKY_FEW, TOURNAMENT, MUTATION_CHANCE, CROSSOVER_CHANCE)
#resetBasePositionAndOrientation
#population = []
gen = 0
##########################################prev
population,gen, prevfile = readFromFile('/run/media/desu/n1/projekty/spider/algorithm/gen/simple/uproszczenie_kopiowanie/generations-02_05_2019_03_49.txt')
temp = []
for ind in population:
temp.append(convertToSim(ind))
population = temp
del temp
filename, history =newFile(prevfile)
##
###############################################new
'''
filename, history =newFile()
population = genetica.gen_population()
##
'''
#print("population F{}".format(population))
logger.debug('new pop: %s', str(population))
#print(population)
#population[0] = convertToSim(ppp[0])
#population[1] = convertToSim(ppp[1])
pop_data = []
pop_fitness = [0,0]
pop_sorted = [0,0]
for generation in range(1,GENERATIONS+1):
#FITNESS_MAX_HISTORY.append(pop_fitness[0])
#show_plot(fig, li, generation)
#print("Generation:", str(generation) + " previous 2 best fitness: ",str(pop_fitness[0]), str(pop_fitness[1]) )
#print("Generation: {}, previous 2 best fitness: {}, {}, chromosomes: {}, {}".format(generation+gen,pop_fitness[0],pop_fitness[1], pop_sorted[0], pop_sorted[1]) )
print("Generation:", str(generation+gen) + " previous 2 best fitness: ",str(pop_fitness[0]), str(pop_fitness[1]) )
weighted_population = []
slicedd = slicePop(population)
#print(len(slicedd))
logger.debug('check1 pop: %d', len(population))
popCurr = 0
for slic in slicedd:
print("current populations", popCurr, "of",POPILATION_SIZE, end="\r")
for idx, individual in enumerate(slic):
simu[idx].indi = individual
#print(individual)
y = copytoLegs(individual)
logger.debug('sent: %s', str(y))
y = stepRepeat(y)
logger.debug('repeated %s', y)
logger.debug('%d', len(y))
simu[idx].parent_pipe.send(y)
t=0
while not (t == MAX_PARALLEL):#if all processes sent data
for sim in simu:
if sim.simok == False:
if sim.parent_pipe.poll():
rec = sim.parent_pipe.recv()
logger.debug('rec1 %s', str(rec))
if rec == "simok":
sim.simok = True
t+=1
t=0
while not (t == MAX_PARALLEL):
for sim in simu:
if sim.parent_pipe.poll():
rec = sim.parent_pipe.recv()#[-1]#!!!!!!!!!!!!!!!!!tylko ostatnie biore? i potem zapisuje? co z polkrokami
logger.debug('rec2 %s', str(rec))
logger.debug('rec2 len %d', len(rec))
if len(rec)==GAIT_STEPS*STEP_REPEAT:#if len(contact) == 6 and len(base_pos) == 3 and len(base_angle) == 3:
#contact, base_pos, base_angle, diff = rec
sim.data = rec
sim.parent_pipe.send('dataok')
logger.debug('M dataok sent')
t+=1
t=0
for sim in simu:
sim.simok = False
sim.parent_pipe.send('reset')
logger.debug('M reset sent')
while not (t == MAX_PARALLEL):
for sim in simu:
if sim.parent_pipe.poll():
rec = sim.parent_pipe.recv()
logger.debug('rec3 %s', str(rec))
if rec == "resok":
t+=1
t=0
for sim in simu:
dd = 0
for x in sim.data:
#print(len(sim.data))
if x == sim.data[-1]:
dd+=genetica.fitness(x)
else:
dd+=genetica.stability(x)
fitness_val = dd
individual = convertToGen(sim.indi)
pair = (individual, fitness_val)
weighted_population.append(pair)
popCurr+=MAX_PARALLEL
population = []
logger.debug('check2')
#for x in range(GAIT_STEPS)
#pop_sorted, pop_fitness = genetica.sort_pop(weighted_population)
pop_sorted, pop_fitness = genetica.sort_pop(weighted_population)
#pop_sort = [pop_sorted, pop_fitness]
#print(pop_sorted[0])
logger.debug('%s',str(pop_fitness))
#saveToFile(str(generation)+" best fitness: " + str(pop_fitness[0]), [pop_sorted + pop_fitness])
#saveToFile(filename, "Generation: " + str(generation)+ " best fitness: " + str(pop_fitness[0]), [pop_fitness[:15], pop_sorted[:15]])
saveToFile(filename, "Generation: " + str(generation+gen)+ " best fitness: " + str(pop_fitness[0]), [pop_fitness, pop_sorted])
saveToFile(history, str(generation+gen)+ "," + str(pop_fitness[0]), [])
#print("saved to file")
logger.debug('check3')
#2 parents survive
#print(pop_sorted[1])
for x in range(CHROM_TO_COPY):
a = convertToSim(pop_sorted[x])
if (a not in population):
population.append(a)
for y in range(0, 5):
while (1):
b = genetica.mutate(a,1,GAIT_STEPS)
if (b not in population):
population.append(b)
break
for y in range(0, 5):
while (1):
b = genetica.mutate(a,1,GAIT_STEPS, RANDOM_MUTATE_POSITIONS)
if (b not in population):
population.append(b)
break
new_gen_population = genetica.gen_population()
new_gen_population = random.sample(new_gen_population, NEWGENES)
population += new_gen_population
logger.debug('check4')
logger.debug('INITIAL population: %s', str(population))
old_sel = pop_sorted
pop = [x[0] for x in weighted_population] #population
b = [x[1] for x in weighted_population] #fitness
logger.debug('pop: %s', str(pop))
logger.debug('fitn: %s', str(b))
children, parents = genetica.get_children(pop,b, len(population)+LUCKY_FEW)
logger.debug('check5')
logger.debug('children: %s', str(children))
logger.debug('parents: %s', str(parents))
lucky = genetica.select_lucky_few(list(pop), parents)
logger.debug('check6')
for x in lucky:
y = convertToSim(x)
logger.debug('lucky: %s', str(y))
population.append(y)
logger.debug('population after: %d', len(population))
logger.debug('check7')
for indiv in children:
ind = convertToSim(indiv)
while (1):
mu = genetica.mutate(ind, steps_num = random.randint(0,GAIT_STEPS))
if mu not in population:
population.append(mu)
break
logger.debug('check8')
print("end")
for sim in simu:
sim.multi_stop()
