def test_grad_arg1():
N = 50
def f_arg1(x):
return normal_logEI_diff_sigma(np.ones(N) * 100,
x)
rng = np.random.RandomState(123)
#sigmavec = np.exp(np.linspace(N) * 10)
sigmavec = np.linspace(.1, 10, N)
verify_grad( f_arg1, [sigmavec], rng=rng, rel_tol=1e-3)
def test_grad_arg0():
N = 50
def f_arg01(x):
return normal_logEI_diff_sigma(x, np.ones(1))
def f_arg0(x):
return normal_logEI_diff_sigma(x, np.ones(N))
rng = np.random.RandomState(123)
diffvec = (rng.rand(N) - .5) * 200
verify_grad(f_arg01, [np.asarray([-50.])], rng=rng)
verify_grad(f_arg01, [np.asarray([50.])], rng=rng)
verify_grad(f_arg0, [diffvec], rng=rng, rel_tol=1e-3)
def test_complex_matrix_inverse(self):
print("shpek")
x_shape = (2, 10, 10)
x = rnd.normal(size=x_shape)
verify_grad(complex_matrix_inverse, (x, ), rng=rnd)
def test_complex_expm(self):
print("kek")
x_shape = (2, 10, 10)
x = rnd.normal(size=x_shape)
verify_grad(complex_expm, (x, ), rng=rnd)
