def test_max_rank():
X = np.random.standard_normal((100, 200))
nt.assert_raises(ValueError, FM.compute_iterative_svd, X, max_rank=2)
U, D, VT = FM.compute_iterative_svd(X)
nt.assert_true(np.linalg.norm(np.dot(U.T, U) - np.identity(100)) < 1.e-6)
nt.assert_true(np.linalg.norm(np.dot(VT, VT.T) - np.identity(100)) < 1.e-6)
U, D, VT = FM.compute_iterative_svd(X, max_rank=200)
nt.assert_true(np.linalg.norm(np.dot(U.T, U) - np.identity(100)) < 1.e-6)
nt.assert_true(np.linalg.norm(np.dot(VT, VT.T) - np.identity(100)) < 1.e-6)
def test_max_rank():
X = np.random.standard_normal((100, 200))
nt.assert_raises(ValueError, FM.compute_iterative_svd, X, max_rank=2)
U, D, VT = FM.compute_iterative_svd(X)
nt.assert_true(np.linalg.norm(np.dot(U.T, U) - np.identity(100)) < 1.e-6)
nt.assert_true(np.linalg.norm(np.dot(VT, VT.T) - np.identity(100)) < 1.e-6)
U, D, VT = FM.compute_iterative_svd(X, max_rank=200)
nt.assert_true(np.linalg.norm(np.dot(U.T, U) - np.identity(100)) < 1.e-6)
nt.assert_true(np.linalg.norm(np.dot(VT, VT.T) - np.identity(100)) < 1.e-6)
def test_stopping_rule():
'''
use a stopping rule in compute_iterative_svd
'''
def soft_threshold_rule(L):
return lambda D: np.fabs(D).min() <= L
L = 30
svt_rule = soft_threshold_rule(L)
U, D, VT = FM.compute_iterative_svd(X, initial_rank=3, stopping_rule=svt_rule, tol=1.e-12, debug=True)
D2 = (D - L) * (D > L)
D1 = np.linalg.svd(X)[1]
D1 = (D1 - L) * (D1 > L)
rank = (D2 > 0).sum()
all_close(D1[:rank], D2[:rank], 'stopping_rule', None)
def test_stopping_rule():
'''
use a stopping rule in compute_iterative_svd
'''
def soft_threshold_rule(L):
return lambda D: np.fabs(D).min() <= L
L = 30
svt_rule = soft_threshold_rule(L)
U, D, VT = FM.compute_iterative_svd(X,
initial_rank=3,
stopping_rule=svt_rule,
tol=1.e-12,
debug=True)
D2 = (D - L) * (D > L)
D1 = np.linalg.svd(X)[1]
D1 = (D1 - L) * (D1 > L)
rank = (D2 > 0).sum()
all_close(D1[:rank], D2[:rank], 'stopping_rule', None)
