def _test(alpha, n):
rv = Dirichlet(alpha=alpha)
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
alpha = alpha.eval()
assert np.allclose(rv.log_prob(x_tf).eval(),
dirichlet_logpdf_vec(x, alpha), atol=1e-3)
def _test(alpha, n):
rv = Dirichlet(alpha=alpha)
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
alpha = alpha.eval()
assert np.allclose(rv.log_prob(x_tf).eval(),
dirichlet_logpdf_vec(x, alpha))
def _test(shape, n):
K = shape[-1]
rv = Dirichlet(shape, alpha=tf.constant(1.0 / K, shape=shape))
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
alpha = rv.alpha.eval()
if len(shape) == 1:
assert np.allclose(
rv.log_prob_idx((), x_tf).eval(),
dirichlet_logpdf_vec(x[:, :], alpha[:]))
elif len(shape) == 2:
for i in range(shape[0]):
assert np.allclose(
rv.log_prob_idx((i, ), x_tf).eval(),
dirichlet_logpdf_vec(x[:, i, :], alpha[i, :]))
else:
assert False
def _test(shape, n):
K = shape[-1]
rv = Dirichlet(shape, alpha=tf.constant(1.0/K, shape=shape))
rv_sample = rv.sample(n)
x = rv_sample.eval()
x_tf = tf.constant(x, dtype=tf.float32)
alpha = rv.alpha.eval()
if len(shape) == 1:
assert np.allclose(
rv.log_prob_idx((), x_tf).eval(),
dirichlet_logpdf_vec(x[:, :], alpha[:]))
elif len(shape) == 2:
for i in range(shape[0]):
assert np.allclose(
rv.log_prob_idx((i, ), x_tf).eval(),
dirichlet_logpdf_vec(x[:, i, :], alpha[i, :]))
else:
assert False
qmu_sigma = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qsigma_alpha = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qsigma_beta = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qpi = Dirichlet(alpha=qpi_alpha)
qmu = Normal(mu=qmu_mu, sigma=qmu_sigma)
qsigma = InverseGamma(alpha=qsigma_alpha, beta=qsigma_beta)
data = {'x': x_train}
inference = ed.KLqp({'pi': qpi, 'mu': qmu, 'sigma': qsigma}, data, model)
inference.run(n_iter=2500, n_samples=10, n_minibatch=20)
# Average per-cluster and per-data point likelihood over many posterior samples.
log_liks = []
for s in range(100):
zrep = {
'pi': qpi.sample(()),
'mu': qmu.sample(()),
'sigma': qsigma.sample(())
}
log_liks += [model.predict(data, zrep)]
log_liks = tf.reduce_mean(log_liks, 0)
# Choose the cluster with the highest likelihood for each data point.
clusters = tf.argmax(log_liks, 0).eval()
plt.scatter(x_train[:, 0], x_train[:, 1], c=clusters, cmap=cm.bwr)
plt.axis([-3, 3, -3, 3])
plt.title("Predicted cluster assignments")
plt.show()
qpi_alpha = tf.nn.softplus(tf.Variable(tf.random_normal([K])))
qmu_mu = tf.Variable(tf.random_normal([K * D]))
qmu_sigma = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qsigma_alpha = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qsigma_beta = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qpi = Dirichlet(alpha=qpi_alpha)
qmu = Normal(mu=qmu_mu, sigma=qmu_sigma)
qsigma = InverseGamma(alpha=qsigma_alpha, beta=qsigma_beta)
data = {'x': x_train}
inference = ed.MFVI({'pi': qpi, 'mu': qmu, 'sigma': qsigma}, data, model)
inference.run(n_iter=2500, n_samples=10, n_minibatch=20)
# Average per-cluster and per-data point likelihood over many posterior samples.
log_liks = []
for s in range(100):
zrep = {'pi': qpi.sample(()),
'mu': qmu.sample(()),
'sigma': qsigma.sample(())}
log_liks += [model.predict(data, zrep)]
log_liks = tf.reduce_mean(log_liks, 0)
# Choose the cluster with the highest likelihood for each data point.
clusters = tf.argmax(log_liks, 0).eval()
plt.scatter(x_train[:, 0], x_train[:, 1], c=clusters, cmap=cm.bwr)
plt.axis([-3, 3, -3, 3])
plt.title("Predicted cluster assignments")
plt.show()
def _test(shape, alpha, n):
x = Dirichlet(shape, alpha)
val_est = tuple(get_dims(x.sample(n)))
val_true = (n, ) + shape
assert val_est == val_true
def _test(alpha, n):
x = Dirichlet(alpha=alpha)
val_est = get_dims(x.sample(n))
val_true = n + get_dims(alpha)
assert val_est == val_true
def _test(shape, alpha, size):
x = Dirichlet(shape, alpha)
val_est = tuple(get_dims(x.sample(size=size)))
val_true = (size, ) + shape
assert val_est == val_true
def _test(alpha, n): x = Dirichlet(alpha=alpha) val_est = get_dims(x.sample(n)) val_true = n + get_dims(alpha) assert val_est == val_true
qpi_alpha = tf.nn.softplus(tf.Variable(tf.random_normal([K])))
qmu_mu = tf.Variable(tf.random_normal([K * D]))
qmu_sigma = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qsigma_alpha = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qsigma_beta = tf.nn.softplus(tf.Variable(tf.random_normal([K * D])))
qpi = Dirichlet(alpha=qpi_alpha)
qmu = Normal(mu=qmu_mu, sigma=qmu_sigma)
qsigma = InverseGamma(alpha=qsigma_alpha, beta=qsigma_beta)
data = {'x': x_train}
inference = ed.KLqp({'pi': qpi, 'mu': qmu, 'sigma': qsigma}, data, model)
inference.run(n_iter=2500, n_samples=10, n_minibatch=20)
# Average per-cluster and per-data point likelihood over many posterior samples.
log_liks = []
for s in range(100):
zrep = {'pi': qpi.sample(()),
'mu': qmu.sample(()),
'sigma': qsigma.sample(())}
log_liks += [model.predict(data, zrep)]
log_liks = tf.reduce_mean(log_liks, 0)
# Choose the cluster with the highest likelihood for each data point.
clusters = tf.argmax(log_liks, 0).eval()
plt.scatter(x_train[:, 0], x_train[:, 1], c=clusters, cmap=cm.bwr)
plt.axis([-3, 3, -3, 3])
plt.title("Predicted cluster assignments")
plt.show()
