def test_normal_logEI():
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(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 explore_grad():
N = 15
ubound = 6e2
diff = np.ones(N) * 100
rng = np.random.RandomState(123)
#diff = np.linspace(0, ubound, N).astype('float64')
#var = np.random.rand(N) * .1 + 1 #1e-8 +
#var = np.ones(N) * .01
var = np.exp(rng.randn(N) * 10) ** 2
var = np.sort(var)
s_d, s_v = theano.tensor.dvectors('dv')
s_y = normal_logEI_diff_sigma(s_d, theano.tensor.sqrt(s_v))
s_gd, s_gv = theano.tensor.grad(s_y.sum(), [s_d, s_v])
fn = theano.function([s_d, s_v], [s_y, s_gd, s_gv])
eps = ubound / 1e8 # 1e1 # 1e-4
y, gd, gv = fn(diff, var)
y_eps, _, _ = fn(diff + eps, var)
y_eps2, _, _ = fn(diff, var + eps)
for di, yi, yi_eps, yi2, gdi, gvi in zip(diff, y, y_eps, y_eps2, gd, gv):
print 'di %.6f\tyi:%.6f\tgi:%.6f\tref:%.6f\tgv:%s\tref:%s' % (
di, yi, gdi, (yi_eps - yi) / eps, gvi, (yi2 - yi) / eps
)
def f_arg1(x):
return normal_logEI_diff_sigma(np.ones(N) * 100,
x)
def f_arg0(x):
return normal_logEI_diff_sigma(x, np.ones(N))
