def test_normal_logEI_elemwise():
rng = np.random.RandomState(123)
N = 2000
thresh = np.linspace(-50, 500, N)
#N = 100
#thresh = np.linspace(37, 38, N)
mean = thresh * 0
var = 1e-1 + rng.rand(N)
sigma = np.sqrt(var)
s_t, s_m, s_v = theano.tensor.dvectors('tmv')
fn = theano.function([s_t, s_m, s_v],
normal_logEI_diff_sigma_elemwise(
s_t - s_m,
theano.tensor.sqrt(s_v)))
my = fn(thresh, mean, var)
ref = logEI_gaussian(mean, var, thresh)
for xi, myv, spv in zip(thresh, my, ref):
print xi, 'my', myv, 'sp', spv, 'diff', (myv - spv)
assert np.any(thresh / sigma > 34)
assert np.all(np.isfinite(my))
assert np.allclose(my[thresh/sigma < 34], ref[thresh/sigma < 34])
assert np.allclose(my, ref, rtol=.1)
def f_arg1(x):
return normal_logEI_diff_sigma_elemwise(
np.ones(N) * 100,
x)
def f_arg0(x):
return normal_logEI_diff_sigma_elemwise(x, np.ones(N))
