def check(dist):
q_s = Gamma(a_s, b_s)
q_v = Gamma(a_v, b_v)
sess = tf.InteractiveSession()
init = tf.global_variables_initializer()
init.run()
no_sample = 100
s_sample = q_s.sample(no_sample).eval()
v_sample = q_v.sample(no_sample).eval()
n = np.zeros([C_u, C_i])
result = np.zeros([C_u, C_i])
n_expected = np.zeros([C_u, C_i])
for i in range(0, no_sample):
n = np.add(n, np.matmul(s_sample[i], np.transpose(v_sample[i])))
n_expected = n / no_sample
#mean of poisson is rate param. So this is fine.
#sample response
#distribution specific
result = dist_mean(dist, n_expected)
return mae(result)
def _test(alpha, beta, n): x = Gamma(alpha=alpha, beta=beta) val_est = get_dims(x.sample(n)) val_true = n + get_dims(alpha) assert val_est == val_true
def _test(alpha, beta, n):
x = Gamma(alpha=alpha, beta=beta)
val_est = get_dims(x.sample(n))
val_true = n + get_dims(alpha)
assert val_est == val_true
# predictive check
n_rep = 100 # number of replicated datasets we generate
holdout_gen = np.zeros((n_rep, x_train.shape[0], x_train.shape[1]))
for i in range(n_rep):
x_generated = x_post.sample().eval()
# look only at the heldout entries
holdout_gen[i] = np.multiply(x_generated, holdout_mask)
n_eval = 10 # we draw samples from the inferred Z and W
obs_ll = []
rep_ll = []
pbar = Progbar(n_eval)
for j in range(1, n_eval + 1):
z1_sample = z1_post.sample().eval()
W0_sample = qW0.sample().eval()
holdoutmean_sample = np.multiply(z1_sample.dot(W0_sample), holdout_mask)
obs_ll.append(
np.mean(np.ma.masked_invalid(
stats.poisson.logpmf(np.array(x_vad, dtype=int),
holdoutmean_sample)),
axis=1))
rep_ll.append(
np.mean(np.ma.masked_invalid(
stats.poisson.logpmf(holdout_gen, holdoutmean_sample)),
axis=2))
pbar.update(j)
