def test_skopt():
space = Space([(0.0, 1.0)])
for i in range(TESTS):
prime_idx = randint(0, MAX_BASE)
hammer = Hammersley(prime_idx)
skhammer = SkoptHammersley(0, 0, [PRIME_VECTOR[prime_idx]])
h1 = hammer.get_array(SIZE)
h2 = np.squeeze(skhammer.generate(space.dimensions, SIZE))
assert np.allclose(h1, h2)
def test_hammersly():
h = Hammersly()
x = h.generate([
(0., 1.),
] * 2, 4)
y = np.array([[0, 0], [1 / 2, 0.25], [1 / 4, 0.5], [3 / 4, 0.75]])
assert_almost_equal(x, y)
samples = h.generate([
(0., 1.),
] * 2, 200)
assert len(samples) == 200
assert len(samples[0]) == 2
def test_hammersly():
h = Hammersly()
x = h.generate([
(0., 1.),
] * 2, 3)
y = np.array([[0.75, 0.125, 0.625], [0.25, 0.5, 0.75]]).T
assert_almost_equal(x, y)
x = h.generate([
(0., 1.),
] * 2, 4)
y = np.array([[0.75, 0.125, 0.625, 0.375], [0.2, 0.4, 0.6, 0.8]]).T
assert_almost_equal(x, y)
samples = h.generate([
(0., 1.),
] * 2, 200)
assert len(samples) == 200
assert len(samples[0]) == 2
def cook_initial_point_generator(generator, **kwargs):
"""Cook a default initial point generator.
For the special generator called "random" the return value is None.
Parameters
----------
generator : "lhs", "sobol", "halton", "hammersly", "grid", "random" \
or InitialPointGenerator instance"
Should inherit from `skopt.sampler.InitialPointGenerator`.
kwargs : dict
Extra parameters provided to the generator at init time.
"""
if generator is None:
generator = "random"
elif isinstance(generator, str):
generator = generator.lower()
if generator not in [
"sobol", "halton", "hammersly", "lhs", "random", "grid",
"maxpro", "maxpro-gd"
]:
raise ValueError("Valid strings for the generator parameter "
" are: 'sobol', 'lhs', 'halton', 'hammersly',"
"'random', 'maxpro','maxpro-gd', or 'grid' not "
"%s." % generator)
elif not isinstance(generator, InitialPointGenerator):
raise ValueError("generator has to be an InitialPointGenerator."
"Got %s" % (str(type(generator))))
if isinstance(generator, str):
if generator == "sobol":
generator = Sobol()
elif generator == "halton":
generator = Halton()
elif generator == "hammersly":
generator = Hammersly()
elif generator == "lhs":
generator = Lhs()
elif generator == "grid":
generator = Grid()
elif generator == "random":
return None
elif generator == "maxpro":
generator = MaxPro(use_gradient=False)
elif generator == "maxpro-gd":
generator = MaxPro(use_gradient=True)
generator.set_params(**kwargs)
return generator
#############################################################################
# Halton sampling
# ---------------
halton = Halton()
x = halton.generate(space.dimensions, n_samples)
plot_searchspace(x, 'Halton')
pdist_data.append(pdist(x).flatten())
x_label.append("halton")
#############################################################################
# Hammersly sampling
# ------------------
hammersly = Hammersly()
x = hammersly.generate(space.dimensions, n_samples)
plot_searchspace(x, 'Hammersly')
pdist_data.append(pdist(x).flatten())
x_label.append("hammersly")
#############################################################################
# Grid sampling
# -------------
grid = Grid(border="include", use_full_layout=False)
x = grid.generate(space.dimensions, n_samples)
plot_searchspace(x, 'Grid')
pdist_data.append(pdist(x).flatten())
x_label.append("grid")