def testProbScalarBaseDistributionNonScalarTransformDynamic(self):
# Scalar batch_shape.
df = np.asarray(2., dtype=np.float32)
# Non-scalar batch_shape.
loc = np.asarray([[0., 0, 0],
[1, 2, 3],
[1, 0, 1]],
dtype=np.float32)
scale_diag = np.asarray([[1., 2, 3],
[2, 3, 4],
[4, 5, 6]],
dtype=np.float32)
scale_tril = np.concatenate([[np.diag(scale_diag[i])]
for i in range(len(scale_diag))])
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=np.tile(df, reps=len(scale_diag)),
loc=loc,
scale_tril=scale_tril)
with self.cached_session():
df_pl = array_ops.placeholder(dtypes.float32, name="df")
loc_pl = array_ops.placeholder(dtypes.float32, name="loc")
scale_diag_pl = array_ops.placeholder(dtypes.float32, name="scale_diag")
feed_dict = {df_pl: df, loc_pl: loc, scale_diag_pl: scale_diag}
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
def testProbStatic(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2, 3], dtype=np.float32)
# Non-scalar batch_shape.
loc = np.asarray([[0., 0, 0],
[1, 2, 3],
[1, 0, 1]],
dtype=np.float32)
scale_diag = np.asarray([[1., 2, 3],
[2, 3, 4],
[4, 5, 6]],
dtype=np.float32)
scale_tril = np.concatenate([[np.diag(scale_diag[i])]
for i in range(len(scale_diag))])
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df, loc=loc, scale_tril=scale_tril)
with self.cached_session():
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(),
rtol=0., atol=1e-5)
def testProbNonScalarBaseDistributionScalarTransformDynamic(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2., 3.], dtype=np.float32)
# Scalar batch_shape.
loc = np.asarray([1, 2, 3], dtype=np.float32)
scale_diag = np.asarray([2, 3, 4], dtype=np.float32)
scale_tril = np.diag(scale_diag)
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df,
loc=np.tile(loc[array_ops.newaxis, :], reps=[len(df), 1]),
scale_tril=np.tile(scale_tril[array_ops.newaxis, :, :],
reps=[len(df), 1, 1]))
with self.cached_session():
df_pl = array_ops.placeholder(dtypes.float32, name="df")
loc_pl = array_ops.placeholder(dtypes.float32, name="loc")
scale_diag_pl = array_ops.placeholder(dtypes.float32, name="scale_diag")
feed_dict = {df_pl: df, loc_pl: loc, scale_diag_pl: scale_diag}
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
def testProbNonScalarBaseDistributionScalarTransformDynamic(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2., 3.], dtype=np.float32)
# Scalar batch_shape.
loc = np.asarray([1, 2, 3], dtype=np.float32)
scale_diag = np.asarray([2, 3, 4], dtype=np.float32)
scale_tril = np.diag(scale_diag)
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df,
loc=np.tile(loc[None, :], [len(df), 1]),
scale_tril=np.tile(scale_tril[None, :, :], [len(df), 1, 1]))
with self.test_session():
df_pl = array_ops.placeholder(dtypes.float32, name="df")
loc_pl = array_ops.placeholder(dtypes.float32, name="loc")
scale_diag_pl = array_ops.placeholder(dtypes.float32, name="scale_diag")
feed_dict = {df_pl: df, loc_pl: loc, scale_diag_pl: scale_diag}
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
def testProbStatic(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2, 3], dtype=np.float32)
# Non-scalar batch_shape.
loc = np.asarray([[0., 0, 0],
[1, 2, 3],
[1, 0, 1]],
dtype=np.float32)
scale_diag = np.asarray([[1., 2, 3],
[2, 3, 4],
[4, 5, 6]],
dtype=np.float32)
scale_tril = np.concatenate([[np.diag(scale_diag[i])]
for i in range(len(scale_diag))])
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df, loc=loc, scale_tril=scale_tril)
with self.test_session():
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(),
rtol=0., atol=1e-5)
def testProbScalarBaseDistributionNonScalarTransformDynamic(self):
# Scalar batch_shape.
df = np.asarray(2., dtype=np.float32)
# Non-scalar batch_shape.
loc = np.asarray([[0., 0, 0],
[1, 2, 3],
[1, 0, 1]],
dtype=np.float32)
scale_diag = np.asarray([[1., 2, 3],
[2, 3, 4],
[4, 5, 6]],
dtype=np.float32)
scale_tril = np.concatenate([[np.diag(scale_diag[i])]
for i in range(len(scale_diag))])
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=np.tile(df, len(scale_diag)),
loc=loc,
scale_tril=scale_tril)
with self.test_session():
df_pl = array_ops.placeholder(dtypes.float32, name="df")
loc_pl = array_ops.placeholder(dtypes.float32, name="loc")
scale_diag_pl = array_ops.placeholder(dtypes.float32, name="scale_diag")
feed_dict = {df_pl: df, loc_pl: loc, scale_diag_pl: scale_diag}
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
def testProbStaticScalar(self):
with self.test_session():
# Scalar batch_shape.
df = np.asarray(3., dtype=np.float32)
# Scalar batch_shape.
loc = np.asarray([1], dtype=np.float32)
scale_diag = np.asarray([2.], dtype=np.float32)
scale_tril = np.diag(scale_diag)
expected_mst = _FakeVectorStudentT(df=df,
loc=loc,
scale_tril=scale_tril)
actual_mst = _VectorStudentT(df=df,
loc=loc,
scale_diag=scale_diag,
validate_args=True)
x = 2. * self._rng.rand(4, 1).astype(np.float32) - 1.
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(),
rtol=0.,
atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(),
rtol=0.,
atol=1e-5)
def testProbNonScalarBaseDistributionScalarTransform(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2., 3.], dtype=np.float32)
# Scalar batch_shape.
loc = np.asarray([1, 2, 3], dtype=np.float32)
scale_diag = np.asarray([2, 3, 4], dtype=np.float32)
scale_tril = np.diag(scale_diag)
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df,
loc=np.tile(loc[array_ops.newaxis, :], reps=[len(df), 1]),
scale_tril=np.tile(scale_tril[array_ops.newaxis, :, :],
reps=[len(df), 1, 1]))
with self.test_session():
actual_mst = _VectorStudentT(df=df,
loc=loc,
scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(),
rtol=0.,
atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(),
rtol=0.,
atol=1e-5)
def testProbDynamic(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2, 3], dtype=np.float32)
# Non-scalar batch_shape.
shift = np.asarray([[0., 0, 0],
[1, 2, 3],
[1, 0, 1]],
dtype=np.float32)
scale_diag = np.asarray([[1., 2, 3],
[2, 3, 4],
[4, 5, 6]],
dtype=np.float32)
scale_tril = np.concatenate([[np.diag(scale_diag[i])]
for i in range(len(scale_diag))])
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df, shift=shift, scale_tril=scale_tril)
with self.test_session():
df_pl = array_ops.placeholder(dtypes.float32, name="df")
shift_pl = array_ops.placeholder(dtypes.float32, name="shift")
scale_diag_pl = array_ops.placeholder(dtypes.float32, name="scale_diag")
feed_dict = {df_pl: df, shift_pl: shift, scale_diag_pl: scale_diag}
actual_mst = _VectorStudentT(df=df, shift=shift, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(feed_dict=feed_dict),
rtol=0., atol=1e-5)
def testProbStaticScalar(self):
with self.cached_session():
# Scalar batch_shape.
df = np.asarray(3., dtype=np.float32)
# Scalar batch_shape.
loc = np.asarray([1], dtype=np.float32)
scale_diag = np.asarray([2.], dtype=np.float32)
scale_tril = np.diag(scale_diag)
expected_mst = _FakeVectorStudentT(
df=df, loc=loc, scale_tril=scale_tril)
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
x = 2. * self._rng.rand(4, 1).astype(np.float32) - 1.
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(),
rtol=0., atol=1e-5)
def testProbNonScalarBaseDistributionScalarTransform(self):
# Non-scalar batch_shape.
df = np.asarray([1., 2., 3.], dtype=np.float32)
# Scalar batch_shape.
loc = np.asarray([1, 2, 3], dtype=np.float32)
scale_diag = np.asarray([2, 3, 4], dtype=np.float32)
scale_tril = np.diag(scale_diag)
x = 2. * self._rng.rand(4, 3, 3).astype(np.float32) - 1.
expected_mst = _FakeVectorStudentT(
df=df,
loc=np.tile(loc[None, :], [len(df), 1]),
scale_tril=np.tile(scale_tril[None, :, :], [len(df), 1, 1]))
with self.test_session():
actual_mst = _VectorStudentT(df=df, loc=loc, scale_diag=scale_diag,
validate_args=True)
self.assertAllClose(expected_mst.log_prob(x),
actual_mst.log_prob(x).eval(),
rtol=0., atol=1e-5)
self.assertAllClose(expected_mst.prob(x),
actual_mst.prob(x).eval(),
rtol=0., atol=1e-5)
