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)
def plot(file=None):
cmaplt.plot(file if file else fplot_current)
cmaplt.pylab.ioff()
cmaplt.pylab.show()
# 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)
def plot(file=None):
cmaplt.plot(file if file else fplot_current)
cmaplt.pylab.ioff()
cmaplt.pylab.show()