def test_svd(self):
for M in self.Mlist:
MU, MD, MV = hp.svd(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.svd(m)
self.assertTrue(np.allclose(np.dot(PU, np.dot(PD, PV)), m))
for i in range(size):
for j in range(size):
if i != j:
self.assertEqual(PD[i, j], 0)
def test_adjust_cond(self):
for M in self.Mlist:
MU, MD, MV = hp.svd(M)
M2 = hp.adjust_cond(MU, MD, MV, 20)
self.assertAlmostEqual(np.linalg.cond(M2), 20)
