return log_prior + log_lik
def build_toy_dataset(N):
pi = np.array([0.4, 0.6])
mus = [[1, 1], [-1, -1]]
stds = [[0.1, 0.1], [0.1, 0.1]]
x = np.zeros((N, 2), dtype=np.float32)
for n in range(N):
k = np.argmax(np.random.multinomial(1, pi))
x[n, :] = np.random.multivariate_normal(mus[k], np.diag(stds[k]))
return x
ed.set_seed(42)
x_train = build_toy_dataset(500)
K = 2
D = 2
model = MixtureGaussian(K, D)
qpi = PointMass(params=ed.to_simplex(tf.Variable(tf.random_normal([K - 1]))))
qmu = PointMass(params=tf.Variable(tf.random_normal([K * D])))
qsigma = PointMass(params=tf.exp(tf.Variable(tf.random_normal([K * D]))))
data = {'x': x_train}
inference = ed.MAP({'pi': qpi, 'mu': qmu, 'sigma': qsigma}, data, model)
inference.run(n_iter=500, n_minibatch=10)
return log_prior + log_lik
def build_toy_dataset(N):
pi = np.array([0.4, 0.6])
mus = [[1, 1], [-1, -1]]
stds = [[0.1, 0.1], [0.1, 0.1]]
x = np.zeros((N, 2), dtype=np.float32)
for n in range(N):
k = np.argmax(np.random.multinomial(1, pi))
x[n, :] = np.random.multivariate_normal(mus[k], np.diag(stds[k]))
return x
ed.set_seed(42)
x_train = build_toy_dataset(500)
K = 2
D = 2
model = MixtureGaussian(K, D)
with tf.variable_scope("posterior"):
qpi = PointMass(params=ed.to_simplex(tf.Variable(tf.random_normal([K - 1]))))
qmu = PointMass(params=tf.Variable(tf.random_normal([K * D])))
qsigma = PointMass(params=tf.exp(tf.Variable(tf.random_normal([K * D]))))
data = {"x": x_train}
inference = ed.Laplace({"pi": qpi, "mu": qmu, "sigma": qsigma}, data, model)
inference.run(n_iter=500, n_minibatch=10)