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)
while r_line != '':
r = tuple(int(p) for p in r_line.strip().replace(',', ' ').split())
#r = Point(coord[0],coord[1],coord[2])
configuration.append(r)
#~ print configuration
r_line = r_file.readline()
r_file.close()
start_configuration = configuration
# set up temperature, target folding energy
temperature = 0.65
Vmin = -34.5
# set up sequence and number_of_cycles
#sequence = convert_sequence('HHPHHPPHPPPHPPHPHHHHPPPPHHPPHPHHHHPP')
sequence = convert_sequence('HHHPPHHPHHHHPHHPPHPHHPHPPHPPPHHPPPPP')
Max_number_of_cycles = 100 # CAN INCREASE THIS LATER, not currently used
# counters for MPFT
sequence_traversals = nbr_moves = 0
trajectories = 3
running_total = 0
MFPT = []
all_sequence_traversals = []
V = energy_function(configuration, sequence)
print 'Starting V = ' + str(V)
for i in xrange(trajectories):
count = 0
while V > Vmin:
