def pcmaes(fitfunc,p):
has_bounds = isinstance(p,lcmaes.CMAParametersPB) or isinstance(p,lcmaes.CMAParametersPBS)
has_scaling = isinstance(p,lcmaes.CMAParametersNBS) or isinstance(p,lcmaes.CMAParametersPBS)
if not has_bounds:
if not has_scaling:
return lcmaes.pcmaes(fitfunc,p)
else:
return lcmaes.pcmaes_ls(fitfunc,p)
else:
if not has_scaling:
return lcmaes.pcmaes_pwqb(fitfunc,p)
else:
return lcmaes.pcmaes_pwqb_ls(fitfunc,p)
def pcmaes(fitfunc, p):
has_bounds = isinstance(p, lcmaes.CMAParametersPB) or isinstance(
p, lcmaes.CMAParametersPBS)
has_scaling = isinstance(p, lcmaes.CMAParametersNBS) or isinstance(
p, lcmaes.CMAParametersPBS)
if not has_bounds:
if not has_scaling:
return lcmaes.pcmaes(fitfunc, p)
else:
return lcmaes.pcmaes_ls(fitfunc, p)
else:
if not has_scaling:
return lcmaes.pcmaes_pwqb(fitfunc, p)
else:
return lcmaes.pcmaes_pwqb_ls(fitfunc, p)
# input parameters for a 10-D problem
x = [10]*10
olambda = 10 # lambda is a reserved keyword in python, using olambda instead.
seed = 0 # 0 for seed auto-generated within the lib.
sigma = 0.1
p = lcmaes.make_simple_parameters(x,sigma,olambda,seed)
p.set_str_algo("acmaes")
# objective function.
def nfitfunc(x,n):
val = 0.0
for i in range(0,n):
val += x[i]*x[i]
return val
# generate a function object
objfunc = lcmaes.fitfunc_pbf.from_callable(nfitfunc);
# pass the function and parameter to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes(objfunc,p)
# collect and inspect results
bcand = cmasols.best_candidate()
bx = lcmaes.get_candidate_x(bcand)
print "best x=",bx
print "distribution mean=",lcmaes.get_solution_xmean(cmasols)
cov = lcmaes.get_solution_cov(cmasols) # numpy array
print "cov=",cov
print "elapsed time=",cmasols.elapsed_time(),"ms"
import cma_multiplt as lcmaplt
# input parameters for a 10-D problem
x = [10.0] * 10
sigma = 0.1
outfile = 'simple.dat'
p = lcmaes.make_simple_parameters(x, sigma)
p.set_str_algo("acmaes")
p.set_fplot(outfile)
# objective function.
def nfitfunc(x, n):
assert len(x) == n # should not be necessary
return sum([xi**2 for xi in x])
# pass the function and parameters to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes(lcmaes.fitfunc_pbf.from_callable(nfitfunc), p)
# visualize results
lcmaplt.plot(outfile)
lcmaplt.pylab.ioff()
lcmaplt.pylab.show()
if __name__ == "__main__":
msg = ' --- press return to continue --- '
try:
raw_input(msg)
except NameError:
input(msg)
# input parameters for a 10-D problem
x = [10.0] * 10
sigma = 0.1
outfile = 'simple.dat'
p = lcmaes.make_simple_parameters(x, sigma)
p.set_str_algo("acmaes")
p.set_fplot(outfile)
# objective function.
def nfitfunc(x, n):
assert len(x) == n # should not be necessary
return sum([xi**2 for xi in x])
# pass the function and parameters to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes(lcmaes.fitfunc_pbf.from_callable(nfitfunc), p)
# visualize results
lcmaplt.plot(outfile)
lcmaplt.pylab.ioff()
lcmaplt.pylab.show()
if __name__ == "__main__":
msg = ' --- press return to continue --- '
try:
raw_input(msg)
except NameError:
input(msg)
x = [10] * 10
lambda_ = 10 # lambda is a reserved keyword in python, using lambda_ instead.
seed = 0 # 0 for seed auto-generated within the lib.
sigma = 0.1
p = lcmaes.make_simple_parameters(x, sigma, lambda_, seed)
p.set_str_algo("acmaes")
# objective function.
def nfitfunc(x, n):
val = 0.0
for i in range(0, n):
val += x[i] * x[i]
return val
# generate a function object
objfunc = lcmaes.fitfunc_pbf.from_callable(nfitfunc)
# pass the function and parameter to cmaes, run optimization and collect solution object.
cmasols = lcmaes.pcmaes(objfunc, p)
# collect and inspect results
bcand = cmasols.best_candidate()
bx = lcmaes.get_candidate_x(bcand)
print("best x=", bx)
print("distribution mean=", lcmaes.get_solution_xmean(cmasols))
cov = lcmaes.get_solution_cov(cmasols) # numpy array
print("cov=", cov)
print("elapsed time=", cmasols.elapsed_time(), "ms")
