def test_schur(self):
for M in self.symmMlist:
MU, MD, MV = hp.schur(M)
# ensure that MD is a diagonal matrix
np.testing.assert_almost_equal(MD, np.diag(np.diag(MD)))
reconstructedM = hp.reconstruct(MU, MD, MV)
np.testing.assert_almost_equal(M, reconstructedM)
def evaluate(self, size):
""" Evaluate with a give size matrix.
Args:
size: Matrix size.
"""
m = helpers.rand(size, size)
PU, PD, PV = helpers.schur(m)
self.assertTrue(np.allclose(np.dot(PU, np.dot(PD, PV)), m))
def test_mat_funcs(self):
for P in self.Plist:
PU, PD, PV = hp.schur(P)
# ensure that PD is a diagonal matrix
self.assertTrue(np.array_equal(PD, np.diag(np.diag(PD))), repr(PD))
# raise Exception(PU*PD*PV, np.dot(PU, np.dot(PD, PV)))
self.assertTrue(np.allclose(PU * PD * PV, np.dot(PU, np.dot(PD, PV))))
for P in self.Plist:
self.assertEqual(hp.mindiag(P), min(
P[i, i] for i in range(min(P.shape))))
self.assertEqual(hp.maxdiag(P), max(
P[i, i] for i in range(min(P.shape))))
