def generate_points(self, lb=None, ub=None, int_var=None):
"""Generate a two factorial design in the unit hypercube.
You can specify lb, ub, int_var to have the design mapped to a
specific domain. These inputs are ignored if one of lb
or ub is None. The design is generated in [0, 1]^d in this case.
:param lb: Lower bounds
:type lb: numpy.array
:param ub: Upper bounds
:type ub: numpy.array
:param int_var: Indices of integer variables. If None, [], or
np.array([]) we assume all variables are continuous.
:type int_var: numpy.array
:return: Two factorial design in unit hypercube of size num_pts x dim
:rtype: numpy.array
"""
if int_var is None or len(int_var) == 0:
int_var = np.array([])
X = np.array(list(itertools.product([0, 1], repeat=self.dim)))
if all([x is not None for x in [lb, ub]]): # Map and round
X = round_vars(from_unit_box(X, lb, ub), int_var, lb, ub)
return X
def _expdes_dist(gen, iterations, lb, ub, int_var):
"""Helper method for picking the best experimental design.
We generate iterations designs and picks the one the maximizes the
minimum distance between points. This isn't a perfect criterion, but
it will help avoid rank-defficient designs such as y=x.
:param lb: Lower bounds
:type lb: numpy.array
:param ub: Upper bounds
:type ub: numpy.array
:param int_var: Indices of integer variables.
:type int_var: numpy.array
:return: Experimental design of size num_pts x dim
:rtype: numpy.ndarray
"""
X = None
best_score = 0
for _ in range(iterations):
cand = gen() # Generate a new design
if all([x is not None for x in [lb, ub]]): # Map and round
cand = round_vars(from_unit_box(cand, lb, ub), int_var, lb, ub)
dists = cdist(cand, cand)
np.fill_diagonal(dists, np.inf) # Since these are zero
score = dists.min().min()
if score > best_score and rank(cand) == cand.shape[1]:
best_score = score
X = cand.copy()
if X is None:
raise ValueError("No valid design found, increase num_pts?")
return X
def generate_points(self, lb=None, ub=None, int_var=None):
"""Generate a two factorial design in the unit hypercube.
You can specify lb, ub, int_var to have the design mapped to a
specific domain. These inputs are ignored if one of lb
or ub is None. The design is generated in [0, 1]^d in this case.
:param lb: Lower bounds
:type lb: numpy.array
:param ub: Upper bounds
:type ub: numpy.array
:param int_var: Indices of integer variables. If None, [], or
np.array([]) we assume all variables are continuous.
:type int_var: numpy.array
:return: Two factorial design in unit hypercube of size num_pts x dim
:rtype: numpy.array
"""
if int_var is None or len(int_var) == 0:
int_var = np.array([])
X = np.array(list(itertools.product([0, 1], repeat=self.dim)))
if all([x is not None for x in [lb, ub]]): # Map and round
X = round_vars(from_unit_box(X, lb, ub), int_var, lb, ub)
return X
def test_unit_box_map():
X = np.random.rand(5, 3)
lb = -1 * np.ones((3, ))
ub = 2 * np.ones((3, ))
X1 = to_unit_box(X, lb, ub)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(
X1, to_unit_box(X, np.atleast_2d(lb), np.atleast_2d(ub)))
assert (X.max() <= 1.0 and X.min() >= 0)
# Try to map back to what we started with
X2 = from_unit_box(X1, lb, ub)
np.testing.assert_equal(X.shape, X2.shape)
np.testing.assert_almost_equal(
X2, from_unit_box(X1, np.atleast_2d(lb), np.atleast_2d(ub)))
np.testing.assert_almost_equal(X2, X)
def test_unit_box_map():
X = np.random.rand(5, 3)
lb = -1 * np.ones((3,))
ub = 2 * np.ones((3,))
X1 = to_unit_box(X, lb, ub)
np.testing.assert_equal(X.shape, X1.shape)
np.testing.assert_almost_equal(
X1, to_unit_box(X, np.atleast_2d(lb), np.atleast_2d(ub)))
assert(X.max() <= 1.0 and X.min() >= 0)
# Try to map back to what we started with
X2 = from_unit_box(X1, lb, ub)
np.testing.assert_equal(X.shape, X2.shape)
np.testing.assert_almost_equal(
X2, from_unit_box(X1, np.atleast_2d(lb), np.atleast_2d(ub)))
np.testing.assert_almost_equal(X2, X)
def get_x(self):
"""Get the list of data points
:return: List of data points
:rtype: numpy.array
"""
return from_unit_box(self.model.get_x(), self.data)
def _expdes_dist(gen, iterations, lb, ub, int_var):
"""Helper method for picking the best experimental design.
We generate iterations designs and picks the one the maximizes the
minimum distance between points. This isn't a perfect criterion, but
it will help avoid rank-defficient designs such as y=x.
:param lb: Lower bounds
:type lb: numpy.array
:param ub: Upper bounds
:type ub: numpy.array
:param int_var: Indices of integer variables.
:type int_var: numpy.array
:return: Experimental design of size num_pts x dim
:rtype: numpy.ndarray
"""
X = None
best_score = 0
for _ in range(iterations):
cand = gen() # Generate a new design
if all([x is not None for x in [lb, ub]]): # Map and round
cand = round_vars(from_unit_box(cand, lb, ub), int_var, lb, ub)
dists = cdist(cand, cand)
np.fill_diagonal(dists, np.inf) # Since these are zero
score = dists.min().min()
if score > best_score and rank(cand) == cand.shape[1]:
best_score = score
X = cand.copy()
if X is None:
raise ValueError("No valid design found, increase num_pts?")
return X