def init_latin(bounds, n=None, rng=None):
"""
Initialize using a Latin hypercube design of size `n`. If `n` is `None`
then use 3D points where D is the dimensionality of the input space.
"""
n = 3*len(bounds) if (n is None) else n
X = random.latin(bounds, n, rng)
return X
def init_latin(bounds, n=None, rng=None):
"""
Initialize using a Latin hypercube design of size `n`. If `n` is `None`
then use 3D points where D is the dimensionality of the input space.
"""
n = 3 * len(bounds) if (n is None) else n
X = random.latin(bounds, n, rng)
return X
def __init__(self, bounds, gp, N=None, rng=None):
self.bounds = np.array(bounds, dtype=float, ndmin=2)
self._gp = gp.copy()
self._rng = random.rstate(rng)
# generate some sampled observations.
N = N if (N is not None) else 100 * len(self.bounds)
X = random.latin(bounds, N, self._rng)
y = self._gp.sample(X, latent=False, rng=self._rng)
# add them back to get a new "posterior".
self._gp.add_data(X, y)
