def run_indep():
candidate = copy.copy(startcandidate)
global neval_count
neval_count = 0
def func(params):
l = eval_candidate(params)
global neval_count
neval_count = neval_count + 1
return -l
print 'optgrid'
candidate = opt_grid(startcandidate, func, [(-20, 20)] * nvar,
ftol=1, disp=0, compute_errors=False)
l = -func(candidate)
print 'Best after %d evaluations' % neval_count,
print_candidate(candidate, l, {})
evaluate_best(candidate, l)
def eval_candidate(candidate):
l = like(candidate)
if numpy.isnan(l):
return -1e300
return l
from jbopt.independent import opt_grid
def func(par):
params = [par[0]] * nvar
print 'flat func:', par, '-'*20,
l = eval_candidate(params)
print 'likelihood', ' '*30, l
return -l
val = opt_grid([0.5], func, [(-20, 20)], ftol=1, disp=0, compute_errors=False)
l = -func(val)
startcandidate = [float(val) for _ in range(nvar)]
seeds = [startcandidate]
json.dump([{'candidate': startcandidate, 'fitness':l}],
open('test_ga_values.json', 'w'), indent=4)
@inspyred.ec.utilities.memoize
@inspyred.ec.evaluators.evaluator
def fitness(candidate, args):
l = eval_candidate(candidate)
#print_candidate(candidate, l, args)
return l
