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]))
log_prior = dirichlet.logpdf(pi, self.alpha)
log_prior += tf.reduce_sum(norm.logpdf(mus, 0, np.sqrt(self.c)), 1)
log_prior += tf.reduce_sum(invgamma.logpdf(sigmas, self.a, self.b), 1)
# Loop over each mini-batch zs[b,:]
log_lik = []
n_minibatch = get_dims(zs[0])[0]
for s in range(n_minibatch):
log_lik_z = N*tf.reduce_sum(tf.log(pi), 1)
for k in range(self.K):
log_lik_z += tf.reduce_sum(multivariate_normal.logpdf(xs,
mus[s, (k*self.D):((k+1)*self.D)],
sigmas[s, (k*self.D):((k+1)*self.D)]))
log_lik += [log_lik_z]
return log_prior + tf.pack(log_lik)
ed.set_seed(42)
x = np.loadtxt('data/mixture_data.txt', dtype='float32', delimiter=',')
data = ed.Data(tf.constant(x, dtype=tf.float32))
model = MixtureGaussian(K=2, D=2)
variational = Variational()
variational.add(Dirichlet(model.K))
variational.add(Normal(model.K*model.D))
variational.add(InvGamma(model.K*model.D))
inference = ed.MFVI(model, variational, data)
inference.run(n_iter=500, n_minibatch=5, n_data=5)
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
