def as_constraint(self, *args):
"""
Imagine there are bubbles of a fixed size floating about constraining
the discrete space
each face is given a coordinate
X = 1, Xg is coordinate dist_real <= r
X = 1, Xg is (0, 0) dist_fake <= r + 1000
note -
2-norm is SIGNIFICANTLY Faster than 1norm.
"""
N = len(self.space.faces)
# centroids.shape[N, 2]
centroids = np.asarray(self.space.faces.centroids)
M = 2 * centroids.max()
centroids = Parameter(shape=centroids.shape, value=centroids)
px, py = self._p.X, self._p.Y
C = []
for i, face_set in enumerate(self._actions):
X = face_set.vars # selected faces
cx = cvx.multiply(centroids[:, 0], X)
cy = cvx.multiply(centroids[:, 1], X)
Xg = cvx.vstack([cx, cy]).T
v = cvx.vstack(
[cvx.promote(px[i], (N, )),
cvx.promote(py[i], (N, ))]).T
C = [cvx.norm(v - Xg, 2, axis=1) <= self._r[i] + M * (1 - X)]
return C
def as_constraint(self, *args):
"""
Imagine there are bubbles of a fixed size floating about constraining
the discrete space
each face is given a coordinate
X = 1, Xg is coordinate dist_real <= r
X = 1, Xg is (0, 0) dist_fake <= r + 1000
note -
2-norm is SIGNIFICANTLY Faster than 1norm.
"""
N = len(self.space.faces)
X = self.stacked # selected faces
M = 100 # upper bound
# centroids.shape[N, 2]
centroids = np.asarray(self.space.faces.centroids)
centroids = Parameter(shape=centroids.shape, value=centroids)
cx = cvx.multiply(centroids[:, 0], X)
cy = cvx.multiply(centroids[:, 1], X)
Xg = cvx.vstack([cx, cy]).T
v = cvx.vstack(
[cvx.promote(self._bx, (N, )),
cvx.promote(self._by, (N, ))]).T
C = [cvx.norm(v - Xg, 2, axis=1) <= self._r + M * (1 - X)]
return C
def test_hermitian(self):
"""Test Hermitian variables.
"""
with self.assertRaises(Exception) as cm:
v = Variable((4, 3), hermitian=True)
self.assertEqual(
str(cm.exception),
"Invalid dimensions (4, 3). Must be a square matrix.")
v = Variable((2, 2), hermitian=True)
assert v.is_hermitian()
# v = Variable((2,2), PSD=True)
# assert v.is_symmetric()
# v = Variable((2,2), NSD=True)
# assert v.is_symmetric()
v = Variable((2, 2), diag=True)
assert v.is_hermitian()
v = Variable((2, 2), hermitian=True)
expr = v + v
assert expr.is_hermitian()
expr = -v
assert expr.is_hermitian()
expr = v.T
assert expr.is_hermitian()
expr = cp.real(v)
assert expr.is_hermitian()
expr = cp.imag(v)
assert expr.is_hermitian()
expr = cp.conj(v)
assert expr.is_hermitian()
expr = cp.promote(Variable(), (2, 2))
assert expr.is_hermitian()
def test_symmetric(self) -> None:
"""Test symmetric variables.
"""
with self.assertRaises(Exception) as cm:
v = Variable((4, 3), symmetric=True)
self.assertEqual(
str(cm.exception),
"Invalid dimensions (4, 3). Must be a square matrix.")
v = Variable((2, 2), symmetric=True)
assert v.is_symmetric()
v = Variable((2, 2), PSD=True)
assert v.is_symmetric()
v = Variable((2, 2), NSD=True)
assert v.is_symmetric()
v = Variable((2, 2), diag=True)
assert v.is_symmetric()
assert self.a.is_symmetric()
assert not self.A.is_symmetric()
v = Variable((2, 2), symmetric=True)
expr = v + v
assert expr.is_symmetric()
expr = -v
assert expr.is_symmetric()
expr = v.T
assert expr.is_symmetric()
expr = cp.real(v)
assert expr.is_symmetric()
expr = cp.imag(v)
assert expr.is_symmetric()
expr = cp.conj(v)
assert expr.is_symmetric()
expr = cp.promote(Variable(), (2, 2))
assert expr.is_symmetric()
def get_energy_option_down(self, mask):
""" transform the energy option down into a n x 1 vector
Args:
mask:
Returns: a CVXPY vector
"""
return cvx.promote(self.eod_avg, mask.loc[mask].shape)
