def check_expectation(dim, exp_fn, monte_fn=np.prod,
num_times=NUM_TIMES, num_samps=NUM_SAMPS):
for x in range(num_times):
mn = np.random.normal(0, 10, (dim,))
cov = project_psd(np.random.normal(0, 5, (dim,dim)), 1e-5)
samps = np.random.multivariate_normal(mn, cov, num_samps)
monte = _mean(monte_fn(row) for row in samps)
exp1 = getattr(pure, exp_fn)(mn, cov, *range(dim))
exp2 = getattr(cyth, exp_fn)(mn, cov, *range(dim))
assert exp1 - exp2 < 1e-7
assert abs(monte - exp1) / exp1 < .02, "%r - %r" % (monte, exp1)
def test_exp_dotprod_sq():
n = 1
m = 1
d = 3
k = (n + m) * d
u = np.arange(0, n * d).reshape(n, d).T
v = np.arange(n * d, (n+m) * d).reshape(m, d).T
for i in range(NUM_TIMES):
mn = np.random.normal(0, 10, (k,))
cov = project_psd(np.random.normal(0, 5, (k,k)), 1e-5)
samps = np.random.multivariate_normal(mn, cov, NUM_SAMPS)
monte = _mean(np.dot(x[u[:,0]], x[v[:,0]])**2 for x in samps)
exp1 = pure.exp_dotprod_sq(u, v, mn, cov, 0, 0)
exp2 = cyth.exp_dotprod_sq(u, v, mn, cov, 0, 0)
assert exp1 - exp2 < 1e-7
assert abs(monte - exp1) / exp1 < .02, "%r - %r" % (monte, exp1)
