for j in range(n_samples):
r[j, 0:n_dim], seed = self._sobol(n_dim, seed)
if self.randomize:
return space.inverse_transform(_random_shift(r, random_state))
return space.inverse_transform(r)
def _sobol(self, dim_num, seed):
Generates a new quasirandom Sobol vector with each call.
After Change
rng = check_random_state(random_state)
space = Space(dimensions)
n_dim = space.n_dims
transformer = space.get_transformer()
space.set_transformer("normalize")
r = np.full((n_samples, n_dim), np.nan)
if self.min_skip == self.max_skip:
seed = self.min_skip
else:
seed = rng.randint(self.min_skip, self.max_skip)
for j in range(n_samples):
r[j, 0:n_dim], seed = self._sobol(n_dim, seed)
if self.randomize:
r = space.inverse_transform(_random_shift(r, random_state))r = space.inverse_transform(r)
space.set_transformer(transformer)return r
def _sobol(self, dim_num, seed):
Generates a new quasirandom Sobol vector with each call.
