def test_inv(self):
# source data
arrays = [
np.array([[2.]]),
np.array([[0., 2.], [3., 0.]]),
np.array([[1., -2.], [-4., 9.]]),
np.array([[10., 11.], [100., 111.]]), # near singular
np.array([[1., 2., -3.], [1., -2., 3.], [-1., 2., 3.]])
]
# test that A * inv(A) = I within machine precision
for A in arrays:
iA = linalg.inv(A)
E = np.eye(A.shape[0])
E1 = np.dot(A, iA)
E2 = np.dot(iA, A)
self.assertApproxEqual(E1, E)
self.assertApproxEqual(E2, E)
def test_inv(self):
# source data
arrays = [
np.array([[ 2.]]),
np.array([[ 0., 2.], [ 3., 0.]]),
np.array([[ 1., -2.], [-4., 9.]]),
np.array([[10., 11.], [100., 111.]]), # near singular
np.array([[1., 2., -3.], [1., -2., 3.], [-1., 2., 3.]])
]
# test that A * inv(A) = I within machine precision
for A in arrays:
iA = linalg.inv(A)
E = np.eye(A.shape[0])
E1 = np.dot(A, iA)
E2 = np.dot(iA, A)
self.assertApproxEqual(E1, E)
self.assertApproxEqual(E2, E)
def test_inv_func(self):
"""Work-around so that this function can be annotated in .pxd"""
# source data
arrays = [
np.array([[2.0]]),
np.array([[0.0, 2.0], [3.0, 0.0]]),
np.array([[1.0, -2.0], [-4.0, 9.0]]),
np.array([[10.0, 11.0], [100.0, 111.0]]), # near singular
np.array([[1.0, 2.0, -3.0], [1.0, -2.0, 3.0], [-1.0, 2.0, 3.0]]),
]
# test that A * inv(A) = I within machine precision
for A in arrays:
iA = linalg.inv(A)
E = np.eye(A.shape[0])
E1 = np.dot(A, iA)
E2 = np.dot(iA, A)
self.assertApproxEqual(E1, E)
self.assertApproxEqual(E2, E)
def test_inv_func(self):
"""Work-around so that this function can be annotated in .pxd"""
# source data
arrays = [
np.array([[2.]]),
np.array([[0., 2.], [3., 0.]]),
np.array([[1., -2.], [-4., 9.]]),
np.array([[10., 11.], [100., 111.]]), # near singular
np.array([[1., 2., -3.], [1., -2., 3.], [-1., 2., 3.]])
]
# test that A * inv(A) = I within machine precision
for A in arrays:
iA = linalg.inv(A)
E = np.eye(A.shape[0])
E1 = np.dot(A, iA)
E2 = np.dot(iA, A)
self.assertApproxEqual(E1, E)
self.assertApproxEqual(E2, E)
