def test_vector_scalar(x, y):
df2d = np.atleast_2d(df(x))
dv = np.atleast_2d(np.zeros_like(y)).transpose()
dgxy2d = g(forward.seed(x) * y).dvalue.toarray()
check_general(x, lambda x: stack(f(x), y), csr_matrix(np.vstack(
(df2d, dv))))
check_general(x, lambda x: stack(y, f(x)), csr_matrix(np.vstack(
(dv, df2d))))
check_general(x, lambda x: stack(f(x), g(x * y)),
csr_matrix(np.vstack((df2d, dgxy2d))))
check_general(x, lambda x: stack(g(x * y), f(x)),
csr_matrix(np.vstack((dgxy2d, df2d))))
def test_simple(x, func, result):
def f(x):
return eval(func, globals(), dict(x=x))
return check_general(x, f, result)
def test_vector(x, func, mat):
f = lambdify(func, dict(ns='sg'))
check_general(x, f, mat)
def check_dot3(x, M):
def g(x): return 5 * x**3 + 3 * x
def f(x): return g(dot(M, x))
dg = g(forward.seed(dot(M, x))).dvalue
check_general(x, f, dot(dg, M))
def check_dot2(x, M):
def g(x): return 5 * x**3 + 3 * x
dg = g(forward.seed(x)).dvalue
def f(x): return dot(M, g(x))
check_general(x, f, dot(M, dg))
def check_dot1(x, M):
def f(x): return dot(M, x)
check_general(x, f, M)
def test_simple(x):
check_general(x, lambda x: stack(f(x), g(x)), csr_matrix([[df(x)],
[dg(x)]]))
check_general(x, lambda x: stack(f(x), 0), csr_matrix([[df(x)], [0]]))
check_general(x, lambda x: stack(0, f(x)), csr_matrix([[0], [df(x)]]))