def test_log_sparse_jac(self):
val = np.array([1, 2, 3])
J = sps.csc_matrix(np.array([[3, 2, 1], [5, 6, 1], [2, 3, 5]]))
a = Ad_array(val, J)
b = af.log(a)
jac = np.dot(np.diag(1 / val), J.A)
self.assertTrue(np.all(b.val == np.log(val)) and np.all(b.jac == jac))
def test_log_vector(self):
val = np.array([1, 2, 3])
J = sps.csc_matrix(np.array([[3, 2, 1], [5, 6, 1], [2, 3, 5]]))
a = Ad_array(val, J)
b = af.log(a)
jac = sps.diags(1 / val) * J
self.assertTrue(np.all(b.val == np.log(val)) and np.all(b.jac.A == jac))
def test_log_scalar_times_ad_var(self):
val = np.array([1, 2, 3])
J = sps.diags(np.array([1, 1, 1]))
a = Ad_array(val, J)
c = 2
b = af.log(c * a)
jac = sps.diags(1 / val) * J
self.assertTrue(
np.allclose(b.val, np.log(c * val)) and np.allclose(b.jac.A, jac.A)
)
self.assertTrue(np.all(a.val == [1, 2, 3]) and np.all(a.jac.A == J.A))
def test_log_advar(self):
a = Ad_array(2, 3)
b = af.log(a)
self.assertTrue(b.val == np.log(2) and b.jac == 1 / 2 * 3)
self.assertTrue(a.val == 2 and a.jac == 3)
def test_log_scalar(self):
a = Ad_array(2, 0)
b = af.log(a)
self.assertTrue(b.val == np.log(2) and b.jac == 0)
self.assertTrue(a.val == 2 and a.jac == 0)