def testNoBatchMultivariate(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
sigma = np.eye(2, dtype=np.float32)
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=1)
self.assertEqual(
0, bijector.shaper.batch_ndims.eval()) # "no batches"
self.assertEqual(
1, bijector.shaper.event_ndims.eval()) # "is vector"
x = [1., 1]
self.assertAllClose([2., 0], run(bijector.forward, x))
self.assertAllClose([0., 2], run(bijector.inverse, x))
self.assertAllClose([0.],
run(bijector.inverse_log_det_jacobian, x))
x = [[1., 1], [-1., -1]]
self.assertAllClose([[2., 0], [0, -2]],
run(bijector.forward, x))
self.assertAllClose([[0., 2], [-2., 0]],
run(bijector.inverse, x))
self.assertAllClose([0.],
run(bijector.inverse_log_det_jacobian, x))
# When mu is a scalar and x is multivariate then the location is
# broadcast.
for run in (static_run, dynamic_run):
mu = 1.
sigma = np.eye(2, dtype=np.float32)
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=1)
self.assertEqual(
0, bijector.shaper.batch_ndims.eval()) # "no batches"
self.assertEqual(
1, bijector.shaper.event_ndims.eval()) # "is vector"
x = [1., 1]
self.assertAllClose([2., 2], run(bijector.forward, x))
self.assertAllClose([0., 0], run(bijector.inverse, x))
self.assertAllClose([0.],
run(bijector.inverse_log_det_jacobian, x))
x = [[1., 1]]
self.assertAllClose([[2., 2]], run(bijector.forward, x))
self.assertAllClose([[0., 0]], run(bijector.inverse, x))
self.assertAllClose([0.],
run(bijector.inverse_log_det_jacobian, x))
def testWeirdSampleNoBatchScalar(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = -1.
sigma = 2. # Scalar.
bijector = _ShiftAndScale(loc=mu, scale=sigma)
self.assertEqual(
0, bijector.shaper.batch_ndims.eval()) # "no batches"
self.assertEqual(
0, bijector.shaper.event_ndims.eval()) # "is scalar"
x = [[1., 2, 3], [4, 5, 6]] # Weird sample shape.
self.assertAllClose([[1., 3, 5], [7, 9, 11]],
run(bijector.forward, x))
self.assertAllClose([[1., 1.5, 2.], [2.5, 3, 3.5]],
run(bijector.inverse, x))
self.assertAllClose([-math.log(2.)],
run(bijector.inverse_log_det_jacobian, x))
def testBatchMultivariateFullDynamic(self):
with self.test_session() as sess:
x = tf.placeholder(tf.float32, name="x")
mu = tf.placeholder(tf.float32, name="mu")
sigma = tf.placeholder(tf.float32, name="sigma")
event_ndims = tf.placeholder(tf.int32, name="event_ndims")
x_value = np.array([[[1., 1]]], dtype=np.float32)
mu_value = np.array([[1., -1]], dtype=np.float32)
sigma_value = np.array([np.eye(2, dtype=np.float32)])
event_ndims_value = np.array(1, dtype=np.int32)
feed_dict = {
x: x_value,
mu: mu_value,
sigma: sigma_value,
event_ndims: event_ndims_value
}
bijector = _ShiftAndScale(loc=mu,
scale=sigma,
event_ndims=event_ndims)
self.assertEqual(1, sess.run(bijector.shaper.batch_ndims,
feed_dict))
self.assertEqual(1, sess.run(bijector.shaper.event_ndims,
feed_dict))
self.assertAllClose([[[2., 0]]],
sess.run(bijector.forward(x), feed_dict))
self.assertAllClose([[[0., 2]]],
sess.run(bijector.inverse(x), feed_dict))
self.assertAllClose([0.],
sess.run(bijector.inverse_log_det_jacobian(x),
feed_dict))
def testWeirdSampleNoBatchScalar(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = -1.
sigma = 2. # Scalar.
bijector = _ShiftAndScale(loc=mu, scale=sigma)
self.assertEqual(0, bijector.shaper.batch_ndims.eval()) # "no batches"
self.assertEqual(0, bijector.shaper.event_ndims.eval()) # "is scalar"
x = [[1., 2, 3],
[4, 5, 6]] # Weird sample shape.
self.assertAllClose([[1., 3, 5],
[7, 9, 11]],
run(bijector.forward, x))
self.assertAllClose([[1., 1.5, 2.],
[2.5, 3, 3.5]],
run(bijector.inverse, x))
self.assertAllClose([-math.log(2.)],
run(bijector.inverse_log_det_jacobian, x))
def testNoBatchMultivariate(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
sigma = np.eye(2, dtype=np.float32)
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=1)
self.assertEqual(0, bijector.shaper.batch_ndims.eval()) # "no batches"
self.assertEqual(1, bijector.shaper.event_ndims.eval()) # "is vector"
x = [1., 1]
self.assertAllClose([2., 0], run(bijector.forward, x))
self.assertAllClose([0., 2], run(bijector.inverse, x))
self.assertAllClose([0.], run(bijector.inverse_log_det_jacobian, x))
x = [[1., 1],
[-1., -1]]
self.assertAllClose([[2., 0],
[0, -2]],
run(bijector.forward, x))
self.assertAllClose([[0., 2],
[-2., 0]],
run(bijector.inverse, x))
self.assertAllClose([0.], run(bijector.inverse_log_det_jacobian, x))
# When mu is a scalar and x is multivariate then the location is
# broadcast.
for run in (static_run, dynamic_run):
mu = 1.
sigma = np.eye(2, dtype=np.float32)
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=1)
self.assertEqual(0, bijector.shaper.batch_ndims.eval()) # "no batches"
self.assertEqual(1, bijector.shaper.event_ndims.eval()) # "is vector"
x = [1., 1]
self.assertAllClose([2., 2], run(bijector.forward, x))
self.assertAllClose([0., 0], run(bijector.inverse, x))
self.assertAllClose([0.], run(bijector.inverse_log_det_jacobian, x))
x = [[1., 1]]
self.assertAllClose([[2., 2]], run(bijector.forward, x))
self.assertAllClose([[0., 0]], run(bijector.inverse, x))
self.assertAllClose([0.], run(bijector.inverse_log_det_jacobian, x))
def testBatchMultivariateFullDynamic(self):
with self.test_session() as sess:
x = tf.placeholder(tf.float32, name="x")
mu = tf.placeholder(tf.float32, name="mu")
sigma = tf.placeholder(tf.float32, name="sigma")
event_ndims = tf.placeholder(tf.int32, name="event_ndims")
x_value = np.array([[[1., 1]]], dtype=np.float32)
mu_value = np.array([[1., -1]], dtype=np.float32)
sigma_value = np.array([np.eye(2, dtype=np.float32)])
event_ndims_value = np.array(1, dtype=np.int32)
feed_dict = {x: x_value, mu: mu_value, sigma: sigma_value,
event_ndims: event_ndims_value}
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=event_ndims)
self.assertEqual(1, sess.run(bijector.shaper.batch_ndims, feed_dict))
self.assertEqual(1, sess.run(bijector.shaper.event_ndims, feed_dict))
self.assertAllClose([[[2., 0]]], sess.run(bijector.forward(x), feed_dict))
self.assertAllClose([[[0., 2]]], sess.run(bijector.inverse(x), feed_dict))
self.assertAllClose(
[0.], sess.run(bijector.inverse_log_det_jacobian(x), feed_dict))
def testBatchMultivariate(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value, dtype=np.float32)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [[1., -1]]
sigma = np.array([np.eye(2, dtype=np.float32)])
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=1)
self.assertEqual(
1, bijector.shaper.batch_ndims.eval()) # "one batch dim"
self.assertEqual(
1, bijector.shaper.event_ndims.eval()) # "is vector"
x = [[[1., 1]]]
self.assertAllClose([[[2., 0]]], run(bijector.forward, x))
self.assertAllClose([[[0., 2]]], run(bijector.inverse, x))
self.assertAllClose([0.], run(bijector.inverse_log_det_jacobian, x))
def testTwoBatchScalar(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
sigma = [1., 1] # Univariate, two batches.
bijector = _ShiftAndScale(loc=mu, scale=sigma)
self.assertEqual(
1, bijector.shaper.batch_ndims.eval()) # "one batch dim"
self.assertEqual(
0, bijector.shaper.event_ndims.eval()) # "is scalar"
x = [1., 1] # One sample from each of two batches.
self.assertAllClose([2., 0], run(bijector.forward, x))
self.assertAllClose([0., 2], run(bijector.inverse, x))
self.assertAllClose([0., 0],
run(bijector.inverse_log_det_jacobian, x))
def testBatchMultivariate(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value, dtype=np.float32)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [[1., -1]]
sigma = np.array([np.eye(2, dtype=np.float32)])
bijector = _ShiftAndScale(loc=mu, scale=sigma, event_ndims=1)
self.assertEqual(
1, bijector.shaper.batch_ndims.eval()) # "one batch dim"
self.assertEqual(
1, bijector.shaper.event_ndims.eval()) # "is vector"
x = [[[1., 1]]]
self.assertAllClose([[[2., 0]]], run(bijector.forward, x))
self.assertAllClose([[[0., 2]]], run(bijector.inverse, x))
self.assertAllClose([0.],
run(bijector.inverse_log_det_jacobian, x))
def testTwoBatchScalar(self):
with self.test_session() as sess:
def static_run(fun, x):
return fun(x).eval()
def dynamic_run(fun, x_value):
x_value = np.array(x_value)
x = tf.placeholder(tf.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
sigma = [1., 1] # Univariate, two batches.
bijector = _ShiftAndScale(loc=mu, scale=sigma)
self.assertEqual(
1, bijector.shaper.batch_ndims.eval()) # "one batch dim"
self.assertEqual(
0, bijector.shaper.event_ndims.eval()) # "is scalar"
x = [1., 1] # One sample from each of two batches.
self.assertAllClose([2., 0], run(bijector.forward, x))
self.assertAllClose([0., 2], run(bijector.inverse, x))
self.assertAllClose([0., 0],
run(bijector.inverse_log_det_jacobian, x))
def testProperties(self):
with self.test_session():
mu = -1.
sigma = 2.
bijector = _ShiftAndScale(loc=mu, scale=sigma)
self.assertEqual("ShiftAndScale", bijector.name)
def testProperties(self):
with self.test_session():
mu = -1.
sigma = 2.
bijector = _ShiftAndScale(loc=mu, scale=sigma)
self.assertEqual("ShiftAndScale", bijector.name)