def check_local_minimum(seq, moves, orig_config):
#print "(local) checking... %s" % seq
# 1k MC steps
from minimization import apply_move, find_move
from energy_function import energy_function
from genetic_lattice import print_at
init_energy = energy_function(orig_config, seq)
config = orig_config
for move in moves:
nconfig = apply_move(move, config)
if energy_function(nconfig, seq)<init_energy:
#print_at("found lower energy with MC... %s" % (energy_function(nconfig, seq)),4)
return (False, seq)
#print_at("quick MC was a'ight %s" % seq,1)
from minimization import calc_acceptance, apply_some_move
sequence_len = len(seq)
sequence_range = xrange(sequence_len)
cE = energy_function(config, seq)
#print_at("MC step?",2)
for i in xrange(10**3):
for res in sequence_range:
moves = find_move(res, config, sequence_len)
if not moves: continue
nconfig = apply_some_move(moves, config)
nE = energy_function(nconfig, seq)
if calc_acceptance(cE,nE,seq,0.5):
cE = nE
config = nconfig
if cE<init_energy:
#print_at("MC steps beat it...i %d res %d" % (i,res),2)
return (False, seq)
print_at("1k MC steps still failed",3)
# a little GA
from minimization import birth, do_mutations, do_cross_over, calc_genetic_energy, choose_fit
from genetic_lattice import encode_lattice
indivs = 100
population = birth(sequence_len, indivs) + [encode_lattice(orig_config)]
for gen in xrange(50):
do_cross_over(population,1.0,sequence_len)
do_mutations(population,1.0,sequence_len)
population = choose_fit(population, indivs, seq)
lowest_V = calc_genetic_energy(population[0],seq)
if lowest_V<init_energy:
print_at("initial energy %s" % init_energy,4)
print_at("found lower energy with GA... %s" % lowest_V,5)
return (False, seq)
print_at("local minimum? %s" % seq,6)
print "\n\n\n"
return (True, seq)
def check_global_minimum(seq, orig_config):
from genetic_lattice import print_at
print_at("(global) checking... %s" % seq,7)
f = open("/tmp/global.out",'a')
f.write(str(seq))
f.close()
# 1mil MC steps
from minimization import apply_some_move, find_move, calc_acceptance
from energy_function import energy_function
config = orig_config
init_energy = energy_function(config, seq)
from minimization import calc_acceptance
sequence_len = len(seq)
sequence_range = xrange(sequence_len)
cE = energy_function(config, seq)
for i in xrange(10**6):
for res in sequence_range:
moves = find_move(res, config, sequence_len)
if not moves: continue
nconfig = apply_some_move(moves, config)
nE = energy_function(nconfig, seq)
if calc_acceptance(cE,nE,seq,0.5):
cE = nE
config = nconfig
if cE<init_energy:
print_at("MC steps beat it...i %d res %d" % (i,res),2)
return (False, seq)
print "10k MC steps still failed"
# a little GA
from minimization import birth, do_mutations, do_cross_over, calc_genetic_energy, choose_fit
from genetic_lattice import encode_lattice
indivs = 100
population = birth(sequence_len, indivs) + [encode_lattice(orig_config)]
for gen in xrange(50):
do_cross_over(population,1.0,sequence_len)
do_mutations(population,1.0,sequence_len)
population = choose_fit(population, indivs, seq)
lowest_V = calc_genetic_energy(population[0],seq)
if lowest_V<init_energy:
print "initial energy %s" % init_energy
print "found lower energy with GA... %s" % lowest_V
return (False, seq)
print "global minimum!!!"
return (True, seq)
def choose_fit(population,num,sequence,resort=True):
if resort:
population.sort(key=lambda c:calc_genetic_energy(c,sequence))
if len(population)>num:
return population[:num]
return population