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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Affine Scalar Tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.contrib.distributions.python.ops.bijectors.affine_scalar import AffineScalar
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
from tensorflow.python.platform import test
class AffineScalarBijectorTest(test.TestCase):
"""Tests correctness of the Y = scale @ x + shift transformation."""
def testProperties(self):
with self.cached_session():
mu = -1.
# scale corresponds to 1.
bijector = AffineScalar(shift=mu)
self.assertEqual("affine_scalar", bijector.name)
def testNoBatchScalar(self):
with self.cached_session() as sess:
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value)
x = array_ops.placeholder(dtypes.float32, name="x")
return sess.run(fun(x, **kwargs), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = -1.
# Corresponds to scale = 2
bijector = AffineScalar(shift=mu, scale=2.)
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, event_ndims=0))
def testOneBatchScalarViaIdentityIn64BitUserProvidesShiftOnly(self):
with self.cached_session() as sess:
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value).astype(np.float64)
x = array_ops.placeholder(dtypes.float64, name="x")
return sess.run(fun(x, **kwargs), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = np.float64([1.])
# One batch, scalar.
# Corresponds to scale = 1.
bijector = AffineScalar(shift=mu)
x = np.float64([1.]) # One sample from one batches.
self.assertAllClose([2.], run(bijector.forward, x))
self.assertAllClose([0.], run(bijector.inverse, x))
self.assertAllClose(
0.,
run(bijector.inverse_log_det_jacobian, x, event_ndims=0))
def testOneBatchScalarViaIdentityIn64BitUserProvidesScaleOnly(self):
with self.cached_session() as sess:
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value).astype(np.float64)
x = array_ops.placeholder(dtypes.float64, name="x")
return sess.run(fun(x, **kwargs), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
multiplier = np.float64([2.])
# One batch, scalar.
# Corresponds to scale = 2, shift = 0.
bijector = AffineScalar(scale=multiplier)
x = np.float64([1.]) # One sample from one batches.
self.assertAllClose([2.], run(bijector.forward, x))
self.assertAllClose([0.5], run(bijector.inverse, x))
self.assertAllClose(
[np.log(0.5)],
run(bijector.inverse_log_det_jacobian, x, event_ndims=0))
def testTwoBatchScalarIdentityViaIdentity(self):
with self.cached_session() as sess:
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value).astype(np.float32)
x = array_ops.placeholder(dtypes.float32, name="x")
return sess.run(fun(x, **kwargs), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
# Univariate, two batches.
# Corresponds to scale = 1.
bijector = AffineScalar(shift=mu)
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, event_ndims=0))
def testTwoBatchScalarIdentityViaScale(self):
with self.cached_session() as sess:
def static_run(fun, x, **kwargs):
return fun(x, **kwargs).eval()
def dynamic_run(fun, x_value, **kwargs):
x_value = np.array(x_value).astype(np.float32)
x = array_ops.placeholder(dtypes.float32, name="x")
return sess.run(fun(x, **kwargs), feed_dict={x: x_value})
for run in (static_run, dynamic_run):
mu = [1., -1]
# Univariate, two batches.
# Corresponds to scale = 1.
bijector = AffineScalar(shift=mu, scale=[2., 1])
x = [1., 1] # One sample from each of two batches.
self.assertAllClose([3., 0], run(bijector.forward, x))
self.assertAllClose([0., 2], run(bijector.inverse, x))
self.assertAllClose(
[-np.log(2), 0.],
run(bijector.inverse_log_det_jacobian, x, event_ndims=0))
def testScalarCongruency(self):
with self.cached_session():
bijector = AffineScalar(shift=3.6, scale=0.42)
assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.)
if __name__ == "__main__":
test.main()
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