def test_scaled_expm_multiply_single_timepoint(self):
np.random.seed(1234)
t = 0.1
n = 5
k = 2
A = np.random.randn(n, n)
B = np.random.randn(n, k)
observed = _expm_multiply_simple(A, B, t=t)
expected = scipy.linalg.expm(t*A).dot(B)
assert_allclose(observed, expected)
def test_scaled_expm_multiply_single_timepoint(self):
np.random.seed(1234)
t = 0.1
n = 5
k = 2
A = np.random.randn(n, n)
B = np.random.randn(n, k)
observed = _expm_multiply_simple(A, B, t=t)
expected = scipy.linalg.expm(t * A).dot(B)
assert_allclose(observed, expected)
def test_scaled_expm_multiply(self):
np.random.seed(1234)
n = 40
k = 3
nsamples = 10
for i in range(nsamples):
for t in (0.2, 1.0, 1.5):
A = scipy.linalg.inv(np.random.randn(n, n))
B = np.random.randn(n, k)
observed = _expm_multiply_simple(A, B, t=t)
expected = np.dot(scipy.linalg.expm(t*A), B)
assert_allclose(observed, expected)
def test_scaled_expm_multiply(self):
np.random.seed(1234)
n = 40
k = 3
nsamples = 10
for i in range(nsamples):
for t in (0.2, 1.0, 1.5):
A = scipy.linalg.inv(np.random.randn(n, n))
B = np.random.randn(n, k)
observed = _expm_multiply_simple(A, B, t=t)
expected = np.dot(scipy.linalg.expm(t*A), B)
assert_allclose(observed, expected)
def test_scaled_expm_multiply_single_timepoint(self):
np.random.seed(1234)
t = 0.1
n = 5
k = 2
A = np.random.randn(n, n)
B = np.random.randn(n, k)
observed = _expm_multiply_simple(A, B, t=t)
expected = sp_expm(t * A).dot(B)
assert_allclose(observed, expected)
observed = estimated(_expm_multiply_simple)(aslinearoperator(A),
B,
t=t)
assert_allclose(observed, expected)
def test_scaled_expm_multiply(self):
np.random.seed(1234)
n = 40
k = 3
nsamples = 10
for i, t in product(range(nsamples), [0.2, 1.0, 1.5]):
with np.errstate(invalid='ignore'):
A = scipy.linalg.inv(np.random.randn(n, n))
B = np.random.randn(n, k)
observed = _expm_multiply_simple(A, B, t=t)
expected = np.dot(sp_expm(t * A), B)
assert_allclose(observed, expected)
observed = estimated(_expm_multiply_simple)(
aslinearoperator(A), B, t=t)
assert_allclose(observed, expected)
