def testBatchMultivariateIdentity(self):
with self.cached_session() as sess:
placeholder = array_ops.placeholder(dtypes.float32, name="x")
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value)
return sess.run(fun(placeholder, **kwargs),
feed_dict={placeholder: x_value})
for run in (static_run, dynamic_run):
mu = [[1., -1]]
# Corresponds to 1 2x2 matrix, with twos on the diagonal.
scale = 2.
bijector = Affine(shift=mu, scale_identity_multiplier=scale)
x = [[[1., 1]]]
self.assertAllClose([[[3., 1]]], run(bijector.forward, x))
self.assertAllClose([[[0., 1]]], run(bijector.inverse, x))
self.assertAllClose(
-np.log(4),
run(bijector.inverse_log_det_jacobian, x, event_ndims=1))
def testDiagWithTriL(self):
with self.cached_session() as sess:
placeholder = array_ops.placeholder(dtypes.float32, name="x")
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value)
return sess.run(
fun(placeholder, **kwargs), feed_dict={placeholder: x_value})
for run in (static_run, dynamic_run):
mu = -1.
# scale = [[2., 0], [2, 3]]
bijector = Affine(
shift=mu, scale_diag=[1., 2.], scale_tril=[[1., 0], [2., 1]])
x = [[1., 2]] # One multivariate sample.
self.assertAllClose([[1., 7]], run(bijector.forward, x))
self.assertAllClose([[1., 1 / 3.]], run(bijector.inverse, x))
self.assertAllClose(
-np.log(6.),
run(bijector.inverse_log_det_jacobian, x, event_ndims=1))
def testBatchMultivariateFullDynamic(self):
with self.cached_session() as sess:
x = array_ops.placeholder(dtypes.float32, name="x")
mu = array_ops.placeholder(dtypes.float32, name="mu")
scale_diag = array_ops.placeholder(dtypes.float32, name="scale_diag")
x_value = np.array([[[1., 1]]], dtype=np.float32)
mu_value = np.array([[1., -1]], dtype=np.float32)
scale_diag_value = np.array([[2., 2]], dtype=np.float32)
feed_dict = {
x: x_value,
mu: mu_value,
scale_diag: scale_diag_value,
}
bijector = Affine(shift=mu, scale_diag=scale_diag)
self.assertAllClose([[[3., 1]]], sess.run(bijector.forward(x), feed_dict))
self.assertAllClose([[[0., 1]]], sess.run(bijector.inverse(x), feed_dict))
self.assertAllClose(
[-np.log(4)],
sess.run(bijector.inverse_log_det_jacobian(
x, event_ndims=1), feed_dict))
def testIdentityWithDiagUpdate(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 = array_ops.placeholder(dtypes.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = -1.
# Corresponds to scale = 2
bijector = Affine(shift=mu,
scale_identity_multiplier=1.,
scale_diag=[1.],
event_ndims=0)
self.assertEqual(0, bijector.event_ndims.eval()) # "is vector"
x = [1., 2, 3] # Three scalar samples (no batches).
self.assertAllClose([1., 3, 5], run(bijector.forward, x))
self.assertAllClose([1., 1.5, 2.], run(bijector.inverse, x))
self.assertAllClose(-np.log(2.),
run(bijector.inverse_log_det_jacobian, x))
def testIdentityWithTriL(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 = array_ops.placeholder(dtypes.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = -1.
# scale = [[2., 0], [2, 2]]
bijector = Affine(
shift=mu,
scale_identity_multiplier=1.,
scale_tril=[[1., 0], [2., 1]])
self.assertEqual(1, bijector.event_ndims.eval()) # "is vector"
x = [[1., 2]] # One multivariate sample.
self.assertAllClose([[1., 5]], run(bijector.forward, x))
self.assertAllClose([[1., 0.5]], run(bijector.inverse, x))
self.assertAllClose(-np.log(4.),
run(bijector.inverse_log_det_jacobian, x))
def testTwoBatchScalarIdentityViaDiagMultiplier(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 = array_ops.placeholder(dtypes.float32, name="x")
return sess.run(fun(x), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
# Univariate, two batches.
# Corresponds to scale = 1.
bijector = Affine(shift=mu,
scale_identity_multiplier=1.,
event_ndims=0)
self.assertEqual(0, bijector.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.,
run(bijector.inverse_log_det_jacobian, x))
def testScalePropertyAssertsCorrectly(self):
with self.test_session():
with self.assertRaises(NotImplementedError):
scale = Affine( # pylint:disable=unused-variable
scale_tril=[[1., 0], [2, 1]],
scale_perturb_factor=[2., 1.]).scale
def testScalarCongruency(self):
with self.test_session():
bijector = Affine(shift=3.6,
scale_identity_multiplier=0.42,
event_ndims=0)
assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.)
def testEventNdimsLargerThanOneRaises(self):
with self.test_session():
mu = [1., -1]
# Scale corresponds to 2x2 identity matrix.
bijector = Affine(shift=mu, event_ndims=2, validate_args=True)
bijector.forward([1., 1.]).eval()
def testProperties(self):
with self.test_session():
mu = -1.
# scale corresponds to 1.
bijector = Affine(shift=mu, event_ndims=0)
self.assertEqual("affine", bijector.name)
def testScaleDiagAndEventNdimsZeroRaises(self):
# Check Diag matrix with zero scaling.
with self.assertRaisesRegexp(ValueError, "only scale argument"):
Affine(shift=None, scale_diag=[0.0], event_ndims=0, validate_args=True)
def testScalarEventIdentityScale(self):
with self.test_session() as sess:
doubler = Affine(scale_identity_multiplier=2., event_ndims=0)
doubler2 = doubler.inverse_log_det_jacobian(2.)
doubler2_ildj_ = sess.run([doubler2])
self.assertAllClose([-np.log(2.)], doubler2_ildj_)
def testProperties(self):
with self.cached_session():
mu = -1.
# scale corresponds to 1.
bijector = Affine(shift=mu)
self.assertEqual("affine", bijector.name)