def weighted_distance_merit(num_pts,
surrogate,
X,
fX,
cand,
weights,
Xpend=None,
dtol=1e-3):
"""Compute the weighted distance merit function.
:param num_pts: Number of points to generate
:type num_pts: int
:param surrogate: Surrogate model object
:type surrogate: object
:param X: Previously evaluated points, of size n x dim
:type X: numpy.array
:param fX: Values at previously evaluated points, of size n x 1
:type fX: numpy.array
:param cand: Candidate points to select from, of size m x dim
:type cand: numpy.array
:param weights: num_pts weights in [0, 1] for merit function
:type weights: list or numpy.array
:param Xpend: Pending evaluation, of size k x dim
:type Xpend: numpy.array
:param dtol: Minimum distance between evaluated and pending points
:type dtol: float
:return: The num_pts new points chosen from the candidate points
:rtype: numpy.array of size num_pts x dim
"""
# Distance
dim = X.shape[1]
if Xpend is None: # cdist can't handle None arguments
Xpend = np.empty([0, dim])
dists = scpspatial.distance.cdist(cand, np.vstack((X, Xpend)))
dmerit = np.amin(dists, axis=1, keepdims=True)
# Values
fvals = surrogate.predict(cand)
fvals = unit_rescale(fvals)
# Pick candidate points
new_points = np.ones((num_pts, dim))
for i in range(num_pts):
w = weights[i]
merit = w * fvals + (1.0 - w) * (1.0 - unit_rescale(np.copy(dmerit)))
merit[dmerit < dtol] = np.inf
jj = np.argmin(merit)
fvals[jj] = np.inf
new_points[i, :] = cand[jj, :].copy()
# Update distances and weights
ds = scpspatial.distance.cdist(cand, np.atleast_2d(new_points[i, :]))
dmerit = np.minimum(dmerit, ds)
return new_points
def weighted_distance_merit(num_pts, surrogate, X, fX, cand,
weights, Xpend=None, dtol=1e-3):
"""Compute the weighted distance merit function.
:param num_pts: Number of points to generate
:type num_pts: int
:param surrogate: Surrogate model object
:type surrogate: object
:param X: Previously evaluated points, of size n x dim
:type X: numpy.array
:param fX: Values at previously evaluated points, of size n x 1
:type fX: numpy.array
:param cand: Candidate points to select from, of size m x dim
:type cand: numpy.array
:param weights: num_pts weights in [0, 1] for merit function
:type weights: list or numpy.array
:param Xpend: Pending evaluation, of size k x dim
:type Xpend: numpy.array
:param dtol: Minimum distance between evaluated and pending points
:type dtol: float
:return: The num_pts new points chosen from the candidate points
:rtype: numpy.array of size num_pts x dim
"""
# Distance
dim = X.shape[1]
if Xpend is None: # cdist can't handle None arguments
Xpend = np.empty([0, dim])
dists = scpspatial.distance.cdist(cand, np.vstack((X, Xpend)))
dmerit = np.amin(dists, axis=1, keepdims=True)
# Values
fvals = surrogate.predict(cand)
fvals = unit_rescale(fvals)
# Pick candidate points
new_points = np.ones((num_pts, dim))
for i in range(num_pts):
w = weights[i]
merit = w*fvals + (1.0-w)*(1.0 - unit_rescale(np.copy(dmerit)))
merit[dmerit < dtol] = np.inf
jj = np.argmin(merit)
fvals[jj] = np.inf
new_points[i, :] = cand[jj, :].copy()
# Update distances and weights
ds = scpspatial.distance.cdist(
cand, np.atleast_2d(new_points[i, :]))
dmerit = np.minimum(dmerit, ds)
return new_points
def test_unit_rescale():
X = np.random.rand(5, 3)
X1 = unit_rescale(X)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(X1.max(), 1.0)
np.testing.assert_almost_equal(X1.min(), 0.0)
X = X.flatten() # Test for 1D array as well
X1 = unit_rescale(X)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(X1.max(), 1.0)
np.testing.assert_almost_equal(X1.min(), 0.0)
X = 0.5 * np.ones((5, 3))
X1 = unit_rescale(X)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(X1.max(), 1.0)
np.testing.assert_almost_equal(X1.min(), 1.0)
def test_unit_rescale():
X = np.random.rand(5, 3)
X1 = unit_rescale(X)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(X1.max(), 1.0)
np.testing.assert_almost_equal(X1.min(), 0.0)
X = X.flatten() # Test for 1D array as well
X1 = unit_rescale(X)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(X1.max(), 1.0)
np.testing.assert_almost_equal(X1.min(), 0.0)
X = 0.5 * np.ones((5, 3))
X1 = unit_rescale(X)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(X1.max(), 1.0)
np.testing.assert_almost_equal(X1.min(), 1.0)