def _test(shape, n):
rv = InvGamma(shape, alpha=tf.zeros(shape)+0.5, beta=tf.zeros(shape)+0.5)
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
alpha = rv.alpha.eval()
beta = rv.beta.eval()
for idx in range(shape[0]):
assert np.allclose(
rv.log_prob_idx((idx, ), x_tf).eval(),
stats.invgamma.logpdf(x[:, idx], alpha[idx], scale=beta[idx]))
def _test(shape, n):
rv = InvGamma(shape, alpha=tf.zeros(shape)+0.5, beta=tf.zeros(shape)+0.5)
rv_sample = rv.sample(n)
with sess.as_default():
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
alpha = rv.alpha.eval()
beta = rv.beta.eval()
for idx in range(shape[0]):
assert np.allclose(
rv.log_prob_idx((idx, ), x_tf).eval(),
stats.invgamma.logpdf(x[:, idx], alpha[idx], scale=beta[idx]))
def _test(shape, a, scale, n):
x = InvGamma(shape, a, scale)
val_est = tuple(get_dims(x.sample(n)))
val_true = (n, ) + shape
assert val_est == val_true
def _test(shape, a, scale, size):
x = InvGamma(shape, a, scale)
val_est = tuple(get_dims(x.sample(size=size)))
val_true = (size, ) + shape
assert val_est == val_true
def _test(shape, a, scale, n):
x = InvGamma(shape, a, scale)
val_est = tuple(get_dims(x.sample(n)))
val_true = (n, ) + shape
assert val_est == val_true
