def test_map_default(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
inference = ed.MAP([x])
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
qx = inference.latent_vars[x]
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_map_default(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
inference = ed.MAP([x])
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
qx = inference.latent_vars[x]
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_map_custom(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
qx = PointMass(tf.nn.softplus(tf.Variable(0.5)))
inference = ed.MAP({x: qx})
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)
def test_map_custom(self):
with self.test_session() as sess:
x = Gamma(2.0, 0.5)
qx = PointMass(tf.nn.softplus(tf.Variable(0.5)))
inference = ed.MAP({x: qx})
inference.initialize(auto_transform=True, n_iter=500)
tf.global_variables_initializer().run()
for _ in range(inference.n_iter):
info_dict = inference.update()
# Check approximation on constrained space has same mode as
# target distribution.
stats = sess.run([x.mode(), qx])
self.assertAllClose(stats[0], stats[1], rtol=1e-5, atol=1e-5)