def main(num_to_find=5):
from genetic_lattice import decode_lattice, decode_string
config = decode_lattice(decode_string('RBBBRFFFUBUFRDDBUBLLLDDFUUFRB'))
pool = Pool()
global_checker = GlobalChecker(pool, num_to_find, config)
print "1"
for i in range(cpu_count()*30):
global_checker.check_local()
print "3"
#i = 0
while True:
#print "4 %d" % i
#i += 1
#if i>10: break
global_checker.restart_locals()
#pool.join()
from time import sleep
sleep(5)
def calc_genetic_energy(config,sequence):
return energy_function(decode_lattice(config),sequence)
def global_minimum():
if len(sys.argv)==1 or 'first' in sys.argv:
sequence = convert_sequence('HHPHHPPHPPPHPPHPHHHHPPPPHHPPHPHHHHPP')
else:
sequence = convert_sequence('HHHPPHHPHHHHPHHPPHPHHPHPPHPPPHHPPPPP')
sequence_len = len(sequence)
if len(sys.argv)>1 and not sys.argv[1].isdigit(): sys.argv.pop(1) # remove first/second
lenargv = len(sys.argv)
# GA data:
# number of individuals per generation
ind_per_gen = int(sys.argv[1]) if lenargv>1 else 100
# number of generations
num_generations = int(sys.argv[2]) if lenargv>2 else 100
# number of GA/SA swaps
num_tries = int(sys.argv[3]) if lenargv>3 else 10
# SA data:
alpha = 0.995
ntemp = int(sys.argv[4]) if lenargv>4 else 1000
ncycles = int(sys.argv[5]) if lenargv>5 else 100
orig_T_star = float(sys.argv[6]) if lenargv>6 else 7.0
# T_star should be <0.2 after ntemp, so
# T_star*alpha**ntemp<0.2
# (0.2/T_star)**(1.0/ntemp)=alpha
alpha = (0.2/orig_T_star)**(1.0/(ntemp+1))
print alpha
# mutation rates
rate_cross = 0.8
rate_mutate = 0.8
population = birth(sequence_len,ind_per_gen)
lowest_V = lowest_V_sofar = 0
highest_V = 1
equal_generations = 0
lower_generations = []
for numtry in xrange(num_tries):
choose_fit(population,ind_per_gen,sequence) # just so it sorts
for i in xrange(num_generations):
print_at('generation %d' % i,0)
# mate best half?
with TimingContext(1):
#mating_population = choose_fit(population,ind_per_gen/2,sequence,resort=False)
mating_population = population[:ind_per_gen/2]
population = population[ind_per_gen/2:] # population is pre-sorted by choose_fit
with TimingContext(2):
if lowest_V < -1.0 and lowest_V == highest_V: # rebirth soon necessary
equal_generations += 1
else:
equal_generations = 0
if equal_generations > 4: # we lost genetic diversity...
mating_population = birth(sequence_len,ind_per_gen)
population = sample(population,2)
with TimingContext(3):
do_cross_over(mating_population,rate_cross,sequence_len)
with TimingContext(4):
do_mutations(mating_population,rate_mutate,sequence_len)
# keep best ones only
with TimingContext(5):
population.extend(mating_population)
with TimingContext(6):
population = choose_fit(population,ind_per_gen,sequence)
# select lowest energy
with TimingContext(7):
#if True:
lowest_conf = population[0]
lowest_V = calc_genetic_energy(lowest_conf,sequence)
highest_V = calc_genetic_energy(population[-1],sequence)
if lowest_V<lowest_V_sofar:
lowest_V_sofar = lowest_V
print_at("lowest V: %f for config %s" % (lowest_V,str(lowest_conf).replace(' ','')),8)
lower_generations.append((i,lowest_V,lowest_conf))
print_at("current_lowest %f" % lowest_V,9)
print_at("current_highest %f" % highest_V,10)
print_at("Generations worth their CPU: %s" % str(lower_generations).replace(' ',''), 11)
# have lowest conf, try annealing now...
lowest_conf = decode_lattice(lowest_conf)
T_star = orig_T_star
for k in xrange(ntemp):
print_at("SA temperature %f (%d)" % (T_star,k),25)
current_config = lowest_conf
current_V = lowest_V
print_at("lowest_V: %f" % lowest_V,26)
for j in xrange(ncycles):
print_at("Cycle %d" % j,27)
next_conf,next_V = MC_step(current_config, sequence, T_star)
if next_V<lowest_V:
current_V = next_V
current_config = next_conf
print_at("current V: %f" % next_V,28)
# take best config from cycles and use that
if current_V < lowest_V:
lowest_V = current_V
lowest_conf = current_config
print_at("lowest V: %f for config %s" % (lowest_V,str(encode_lattice(lowest_conf)).replace(' ','')),29)
T_star *= alpha
# aaaaand start over
population = [encode_lattice(lowest_conf)]+birth(sequence_len,ind_per_gen)
mating_population = []
print_at('',30)
from genetic_lattice import decode_string, decode_lattice, print_at
from energy_function import energy_function
def sequence_from_i(i,sequence_len):
return tuple((i>>j)&1 for j in range(sequence_len))
def num_H(sequence):
return sum(sequence)
if __name__=='__main__':
configuration = decode_lattice(decode_string('RBBBRFFFUBUFRDDBUBLLLDDFUUFRB'))
sequence_len = len(configuration)
sequence_len2 = len(configuration)/2
max_configs = int(2**sequence_len)
#start = 10000
start = 32767 # first half-H config
#found = []
lowest_seq = None
lowest_V = 0
j = 0
for i in xrange(start,max_configs):
sequence = sequence_from_i(i,sequence_len)
if num_H(sequence)!=sequence_len2: continue
j += 1
#found.append(sequence)
V = energy_function(configuration, sequence)
if j%10000==0:
print_at(str(sequence), 2)
print_at("current V: %f, lowest_V: %f" % (V,lowest_V), 3)
