def test_rankone():
x1 = np.random.standard_normal(100)
x2 = np.random.standard_normal(50)
fm = FM.factored_matrix(np.multiply.outer(x1, x2), min_singular=1.e-2, affine_offset=0)
fm.X
fm.copy()
U = np.random.standard_normal((50, 10))
V = np.random.standard_normal((100, 20))
nt.assert_raises(ValueError, FM.factored_matrix, X, -1)
fm.linear_map(U)
fm.affine_map(U)
fm.adjoint_map(V)
def test_class():
for shape in [(100, 50), (50, 100)]:
n, p = shape
X = np.random.standard_normal((n, p))
fm = FM.factored_matrix(X, min_singular=1.e-2, affine_offset=0)
fm.X
fm.copy()
U = np.random.standard_normal((p, 10))
V = np.random.standard_normal((n, 20))
nt.assert_raises(ValueError, FM.factored_matrix, X, -1)
fm.linear_map(U)
fm.affine_map(U)
fm.adjoint_map(V)
def test_class():
for shape in [(100, 50), (50, 100)]:
n, p = shape
X = np.random.standard_normal((n,p))
fm = FM.factored_matrix(X, min_singular=1.e-2, affine_offset=0)
fm.X
fm.copy()
U = np.random.standard_normal((p, 10))
V = np.random.standard_normal((n, 20))
nt.assert_raises(ValueError, FM.factored_matrix, X, -1)
fm.linear_map(U)
fm.affine_map(U)
fm.adjoint_map(V)
def test_rankone():
x1 = np.random.standard_normal(100)
x2 = np.random.standard_normal(50)
fm = FM.factored_matrix(np.multiply.outer(x1, x2),
min_singular=1.e-2,
affine_offset=0)
fm.X
fm.copy()
U = np.random.standard_normal((50, 10))
V = np.random.standard_normal((100, 20))
nt.assert_raises(ValueError, FM.factored_matrix, X, -1)
fm.linear_map(U)
fm.affine_map(U)
fm.adjoint_map(V)
def test_zero():
fm = FM.factored_matrix(np.zeros((100, 50)))
fm.X
def test_zero():
fm = FM.factored_matrix(np.zeros((100, 50)))
fm.X
