def testSessionRun(self):
with self.cached_session() as sess:
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 0.1))
x_ph = tf1.placeholder(tf.float32, [])
y = ed.RandomVariable(tfp.distributions.Normal(x_ph, 0.1))
self.assertLess(sess.run(x), 5.0)
self.assertLess(sess.run(x, feed_dict={x_ph: 100.0}), 5.0)
self.assertGreater(sess.run(y, feed_dict={x_ph: 100.0}), 5.0)
self.assertRaises(tf.errors.InvalidArgumentError, sess.run, y)
def testConstructor(self):
x = ed.RandomVariable(tfp.distributions.Poisson(rate=np.ones([2, 5])),
value=np.ones([2, 5]))
self.assertAllEqual(tf.convert_to_tensor(x), x.value)
with self.assertRaises(ValueError):
_ = ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5),
value=tf.zeros([2, 5], dtype=tf.int32))
x = ed.RandomVariable(FakeDistribution())
with self.assertRaises(NotImplementedError):
_ = ed.RandomVariable(FakeDistributionNoSample())
def testOperatorsFloordiv(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x // y
z_value = x.value // y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
def testOperatorsRpow(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y**x
z_value = y**x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
def make_index_rv(*args, **kwargs):
del args
sample_shape = kwargs.pop('sample_shape', ())
rv_value = kwargs.pop('value', None)
return ed.RandomVariable(distribution=index_dist,
sample_shape=sample_shape,
value=rv_value)
def testArrayPriority(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = np.array(5.0, dtype=np.float32)
z = y / x
z_value = y / x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
def testNumpy(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.23)
if tf.executing_eagerly():
self.assertEqual(x.numpy(), tf.constant(1.23).numpy())
else:
with self.assertRaises(NotImplementedError):
_ = x.numpy()
def testOperatorsGetitem(self):
x = ed.RandomVariable(tfp.distributions.Normal(tf.random.normal([3, 4]),
1.))
z = x[0:2, 2:3]
z_value = x.value[0:2, 2:3]
self.assertIsInstance(z, ed.RandomVariable)
self.assertAllEqual(z.distribution.mean(), x.distribution.mean()[0:2, 2:3])
self.assertAllEqual(tf.convert_to_tensor(z), z_value)
def testShape(self, probs, sample_shape, batch_shape, event_shape):
rv = ed.RandomVariable(tfp.distributions.Bernoulli(probs=probs),
sample_shape=sample_shape)
self.assertEqual(rv.shape, sample_shape + batch_shape + event_shape)
self.assertEqual(rv.shape, rv.shape)
self.assertEqual(rv.sample_shape, sample_shape)
self.assertEqual(rv.distribution.batch_shape, batch_shape)
self.assertEqual(rv.distribution.event_shape, event_shape)
def testStr(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.234)
if tf.executing_eagerly():
pattern = "RandomVariable(\"1.234\", shape=(), dtype=float32"
else:
pattern = "RandomVariable(\"{name}\", shape=(), dtype=float32".format(
name=x.distribution.name)
regexp = re.escape(pattern)
self.assertRegexpMatches(str(x), regexp)
def testGradientsFirstOrder(self):
x = ed.RandomVariable(tfp.distributions.Normal(0., 1.))
def f(x):
return 2. * x
with tf.GradientTape() as tape:
tape.watch(x.value)
y = f(x)
z = tape.gradient(y, [x.value])[0]
self.assertEqual(z, 2.)
def testRepr(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.234)
if tf.executing_eagerly():
string = ("<ed.RandomVariable '{name}' shape=() "
"dtype=float32 numpy=1.234>".format(name=x.distribution.name))
else:
string = "<ed.RandomVariable '{name}' shape=() dtype=float32>".format(
name=x.distribution.name)
self.assertEqual(repr(x), string)
def testGradientsFirstOrder(self):
f = lambda x: 2. * x
x = ed.RandomVariable(tfp.distributions.Normal(0., 1.))
y = f(x)
if tf.executing_eagerly():
df = tfe.gradients_function(f)
(z, ) = df(x)
else:
(z, ) = tf.gradients(y, x)
self.assertEqual(self.evaluate(z), 2.)
def testOperatorsGetitem(self):
x = ed.RandomVariable(tfp.distributions.Normal(tf.random.normal([3, 4]),
1.))
z = x[0:2, 2:3]
self.assertIsInstance(z, ed.RandomVariable)
z_mean_eval, x_mean_eval = self.evaluate([z.distribution.mean(),
x.distribution.mean()[0:2, 2:3]])
self.assertAllEqual(z_mean_eval, x_mean_eval)
z_value = x.value[0:2, 2:3]
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
def testGradientsSecondOrder(self):
f = lambda x: 2 * (x**2)
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = f(x)
if tf.executing_eagerly():
df = tfe.gradients_function(f)
d2f = tfe.gradients_function(lambda x: df(x)[0])
(z, ) = d2f(x)
else:
(z, ) = tf.gradients(y, x)
(z, ) = tf.gradients(z, x)
self.assertEqual(self.evaluate(z), 4.0)
def testGradientsSecondOrder(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
def f(x):
return 2 * (x ** 2)
with tf.GradientTape() as tape2:
tape2.watch(x.value)
with tf.GradientTape() as tape:
tape.watch(x.value)
y = f(x)
z = tape.gradient(y, [x.value])[0]
z = tape2.gradient(z, [x.value])[0]
self.assertEqual(z, 4.0)
def testGradientsFirstOrder(self):
x = ed.RandomVariable(tfp.distributions.Normal(0., 1.))
def f(x):
return 2. * x
if tf.executing_eagerly():
with tf.GradientTape() as tape:
tape.watch(x.value)
y = f(x)
z = tape.gradient(y, [x.value])[0]
else:
y = f(x)
(z,) = tf.gradients(y, x)
self.assertEqual(self.evaluate(z), 2.)
def testGradientsSecondOrder(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
def f(x):
return 2 * (x ** 2)
if tf.executing_eagerly():
with tf.GradientTape() as tape2:
tape2.watch(x.value)
with tf.GradientTape() as tape:
tape.watch(x.value)
y = f(x)
z = tape.gradient(y, [x.value])[0]
z = tape2.gradient(z, [x.value])[0]
else:
y = f(x)
(z,) = tf.gradients(y, x)
(z,) = tf.gradients(z, x)
self.assertEqual(self.evaluate(z), 4.0)
def testOperatorsHash(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
self.assertNotEqual(hash(x), hash(y))
self.assertEqual(hash(x), id(x))
def testRandomTensorSample(self):
num_samples = tf.cast(
tfp.distributions.Poisson(rate=5.).sample(), tf.int32)
_ = ed.RandomVariable(tfp.distributions.Normal(loc=0.0, scale=1.0),
sample_shape=num_samples)
class RandomVariableTest(parameterized.TestCase, tf.test.TestCase):
@tfe.run_test_in_graph_and_eager_modes
def testConstructor(self):
x = ed.RandomVariable(tfp.distributions.Poisson(rate=tf.ones([2, 5])),
value=tf.ones([2, 5]))
x_sample, x_value = self.evaluate([tf.convert_to_tensor(x), x.value])
self.assertAllEqual(x_sample, x_value)
with self.assertRaises(ValueError):
_ = ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5),
value=tf.zeros([2, 5], dtype=tf.int32))
x = ed.RandomVariable(FakeDistribution())
with self.assertRaises(NotImplementedError):
_ = x.value
@tfe.run_test_in_graph_and_eager_modes
def testGradientsFirstOrder(self):
f = lambda x: 2. * x
x = ed.RandomVariable(tfp.distributions.Normal(0., 1.))
y = f(x)
if tf.executing_eagerly():
df = tfe.gradients_function(f)
(z, ) = df(x)
else:
(z, ) = tf.gradients(y, x)
self.assertEqual(self.evaluate(z), 2.)
@tfe.run_test_in_graph_and_eager_modes
def testGradientsSecondOrder(self):
f = lambda x: 2 * (x**2)
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = f(x)
if tf.executing_eagerly():
df = tfe.gradients_function(f)
d2f = tfe.gradients_function(lambda x: df(x)[0])
(z, ) = d2f(x)
else:
(z, ) = tf.gradients(y, x)
(z, ) = tf.gradients(z, x)
self.assertEqual(self.evaluate(z), 4.0)
@tfe.run_test_in_graph_and_eager_modes
def testStr(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.234)
if tf.executing_eagerly():
pattern = "RandomVariable(\"1.234\", shape=(), dtype=float32"
else:
pattern = "RandomVariable(\"{name}\", shape=(), dtype=float32".format(
name=x.distribution.name)
regexp = re.escape(pattern)
self.assertRegexpMatches(str(x), regexp)
@tfe.run_test_in_graph_and_eager_modes
def testRepr(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.234)
if tf.executing_eagerly():
string = ("<ed.RandomVariable '{name}' shape=() "
"dtype=float32 numpy=1.234>".format(
name=x.distribution.name))
else:
string = "<ed.RandomVariable '{name}' shape=() dtype=float32>".format(
name=x.distribution.name)
self.assertEqual(repr(x), string)
@tfe.run_test_in_graph_and_eager_modes
def testNumpy(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.23)
if tf.executing_eagerly():
self.assertEqual(x.numpy(), tf.constant(1.23).numpy())
else:
with self.assertRaises(NotImplementedError):
_ = x.numpy()
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsAdd(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x + y
z_value = x.value + y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRadd(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y + x
z_value = y + x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsSub(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x - y
z_value = x.value - y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRsub(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y - x
z_value = y - x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsMul(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x * y
z_value = x.value * y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRmul(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y * x
z_value = y * x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsDiv(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x / y
z_value = x.value / y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRdiv(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y / x
z_value = y / x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsFloordiv(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x // y
z_value = x.value // y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRfloordiv(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y // x
z_value = y // x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsMod(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x % y
z_value = x.value % y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRmod(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y % x
z_value = y % x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsLt(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x < y
z_value = x.value < y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsLe(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x <= y
z_value = x.value <= y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsGt(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x > y
z_value = x.value > y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsGe(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x >= y
z_value = x.value >= y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsGetitem(self):
x = ed.RandomVariable(
tfp.distributions.Normal(tf.random.normal([3, 4]), 1.))
z = x[0:2, 2:3]
self.assertIsInstance(z, ed.RandomVariable)
z_mean_eval, x_mean_eval = self.evaluate(
[z.distribution.mean(),
x.distribution.mean()[0:2, 2:3]])
self.assertAllEqual(z_mean_eval, x_mean_eval)
z_value = x.value[0:2, 2:3]
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsPow(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = x**y
z_value = x.value**y
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsRpow(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
z = y**x
z_value = y**x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsNeg(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
z = -x
z_value = -x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsAbs(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
z = abs(x)
z_value = abs(x.value)
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsHash(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
self.assertNotEqual(hash(x), hash(y))
self.assertEqual(hash(x), id(x))
# TODO(trandustin): Re-enable test.
# @tfe.run_test_in_graph_and_eager_modes
# def testOperatorsEq(self):
# x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
# self.assertEqual(x, x)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsNe(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
self.assertNotEqual(x, y)
@tfe.run_test_in_graph_and_eager_modes
def testOperatorsBoolNonzero(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
with self.assertRaises(TypeError):
_ = not x
@tfe.run_test_in_graph_and_eager_modes
def testArrayPriority(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = np.array(5.0, dtype=np.float32)
z = y / x
z_value = y / x.value
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
@tfe.run_test_in_graph_and_eager_modes
def testConvertToTensor(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 0.1))
with self.assertRaises(ValueError):
_ = tf.convert_to_tensor(x, dtype=tf.int32)
def testSessionEval(self):
with self.cached_session() as sess:
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 0.1))
x_ph = tf1.placeholder(tf.float32, [])
y = ed.RandomVariable(tfp.distributions.Normal(x_ph, 0.1))
self.assertLess(x.eval(), 5.0)
self.assertLess(x.eval(sess), 5.0)
self.assertLess(x.eval(feed_dict={x_ph: 100.0}), 5.0)
self.assertGreater(y.eval(feed_dict={x_ph: 100.0}), 5.0)
self.assertGreater(y.eval(sess, feed_dict={x_ph: 100.0}), 5.0)
self.assertRaises(tf.errors.InvalidArgumentError, y.eval)
self.assertRaises(tf.errors.InvalidArgumentError, y.eval, sess)
def testSessionRun(self):
with self.cached_session() as sess:
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 0.1))
x_ph = tf1.placeholder(tf.float32, [])
y = ed.RandomVariable(tfp.distributions.Normal(x_ph, 0.1))
self.assertLess(sess.run(x), 5.0)
self.assertLess(sess.run(x, feed_dict={x_ph: 100.0}), 5.0)
self.assertGreater(sess.run(y, feed_dict={x_ph: 100.0}), 5.0)
self.assertRaises(tf.errors.InvalidArgumentError, sess.run, y)
@parameterized.parameters(
{
"rv": ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5)),
"sample_shape": [],
"batch_shape": [],
"event_shape": []
},
{
"rv": ed.RandomVariable(
tfp.distributions.Bernoulli(tf.zeros([2, 3]))),
"sample_shape": [],
"batch_shape": [2, 3],
"event_shape": []
},
{
"rv":
ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5),
sample_shape=2),
"sample_shape": [2],
"batch_shape": [],
"event_shape": []
},
{
"rv":
ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5),
sample_shape=[2, 1]),
"sample_shape": [2, 1],
"batch_shape": [],
"event_shape": []
},
{
"rv":
ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5),
sample_shape=tf.constant([2])),
"sample_shape": [2],
"batch_shape": [],
"event_shape": []
},
{
"rv":
ed.RandomVariable(tfp.distributions.Bernoulli(probs=0.5),
sample_shape=tf.constant([2, 4])),
"sample_shape": [2, 4],
"batch_shape": [],
"event_shape": []
},
)
@tfe.run_test_in_graph_and_eager_modes
def testShape(self, rv, sample_shape, batch_shape, event_shape):
self.assertEqual(rv.shape, sample_shape + batch_shape + event_shape)
self.assertEqual(rv.shape, rv.shape)
self.assertEqual(rv.sample_shape, sample_shape)
self.assertEqual(rv.distribution.batch_shape, batch_shape)
self.assertEqual(rv.distribution.event_shape, event_shape)
@tfe.run_test_in_graph_and_eager_modes
def testRandomTensorSample(self):
num_samples = tf.cast(
tfp.distributions.Poisson(rate=5.).sample(), tf.int32)
_ = ed.RandomVariable(tfp.distributions.Normal(loc=0.0, scale=1.0),
sample_shape=num_samples)
def testConvertToTensor(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 0.1))
with self.assertRaises(ValueError):
_ = tf.convert_to_tensor(x, dtype=tf.int32)
def testOperatorsBoolNonzero(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
with self.assertRaises(TypeError):
_ = not x
def testOperatorsRpow(self): x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0)) y = 5.0 z = y ** x z_value = y ** x.value self.assertAllEqual(z, z_value)
def testOperatorsAbs(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
z = abs(x)
z_value = abs(x.value)
z_eval, z_value_eval = self.evaluate([z, z_value])
self.assertAllEqual(z_eval, z_value_eval)
def testNumpy(self): x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.23) self.assertEqual(x, tf.constant(1.23))
def testOperatorsPow(self): x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0)) y = 5.0 z = x ** y z_value = x.value ** y self.assertAllEqual(z, z_value)
def testStr(self): x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0), value=1.234) pattern = "RandomVariable(\"1.234\", shape=(), dtype=float32" regexp = re.escape(pattern) self.assertRegexpMatches(str(x), regexp)
def testOperatorsNe(self):
x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0))
y = 5.0
self.assertNotEqual(x, y)
def testOperatorsNeg(self): x = ed.RandomVariable(tfp.distributions.Normal(0.0, 1.0)) z = -x z_value = -x.value self.assertAllEqual(z, z_value)
