def __init__(self, mode):
self.mode = mode
if self.mode == "random":
pass
elif self.mode == "sobol":
self.rand_gen = SobolGenerator(2)
elif self.mode == "halton":
self.rand_gen = Halton(2)
self.rand_gen_1 = Halton(1)
def get_design_sites(dim, n_sample, x_lb, x_ub, sampling_method='lhs'):
x_lb = atleast_2d(x_lb)
x_ub = atleast_2d(x_ub)
x_lb = x_lb.T if size(x_lb, 0) != 1 else x_lb
x_ub = x_ub.T if size(x_ub, 0) != 1 else x_ub
if sampling_method == 'lhs':
# Latin Hyper Cube Sampling: Get evenly distributed sampling in R^dim
samples = lhs(dim, samples=n_sample) * (x_ub - x_lb) + x_lb
elif sampling_method == 'uniform':
samples = np.random.rand(n_sample, dim) * (x_ub - x_lb) + x_lb
elif sampling_method == 'sobol':
seed = mod(int(time.time()) + os.getpid(), int(1e6))
samples = np.zeros((n_sample, dim))
for i in range(n_sample):
samples[i, :], seed = i4_sobol(dim, seed)
samples = samples * (x_ub - x_lb) + x_lb
elif sampling_method == 'halton':
sequencer = Halton(dim)
samples = sequencer.get(n_sample) * (x_ub - x_lb) + x_lb
return samples
def __init__(self, n, shape='col'):
if not halton_available:
raise ImportError("Package 'ghalton' not found, QuasiGaussianHaltonSampling not available.")
self.n = n
self.shape = (n,1) if shape == 'col' else (1,n)
self.halton = Halton(n)
def __init__(self,
param_distributions,
n_iter,
random_state=None,
method='Halton'):
self.param_distributions = param_distributions
self.n_iter = n_iter
self.random_state = random_state
self.method = method
if method == 'Halton':
self.Halton = Halton(len(self.param_distributions.keys()))