def loop_fn(i):
rates_i = array_ops.gather(rates, i)
# Test both scalar and non-scalar params and shapes.
return (random_ops.random_poisson(lam=rates_i[0, 0], shape=[]),
random_ops.random_poisson(lam=rates_i, shape=[]),
random_ops.random_poisson(lam=rates_i[0, 0], shape=[3]),
random_ops.random_poisson(lam=rates_i, shape=[3]))
def loop_fn(i):
rates_i = array_ops.gather(rates, i)
# Test both scalar and non-scalar params and shapes.
return (random_ops.random_poisson(lam=rates_i[0, 0], shape=[]),
random_ops.random_poisson(lam=rates_i, shape=[]),
random_ops.random_poisson(lam=rates_i[0, 0], shape=[3]),
random_ops.random_poisson(lam=rates_i, shape=[3]))
def testDTypeCombinationsV2(self):
"""Tests random_poisson_v2() for all supported dtype combinations."""
with self.cached_session():
for lam_dt in _SUPPORTED_DTYPES:
for out_dt in _SUPPORTED_DTYPES:
random_ops.random_poisson(
constant_op.constant([1], dtype=lam_dt), [10],
dtype=out_dt).eval()
def testDTypeCombinationsV2(self):
"""Tests random_poisson_v2() for all supported dtype combinations."""
with self.cached_session():
for lam_dt in _SUPPORTED_DTYPES:
for out_dt in _SUPPORTED_DTYPES:
random_ops.random_poisson(
constant_op.constant([1], dtype=lam_dt), [10],
dtype=out_dt).eval()
def testNoCSE(self):
"""CSE = constant subexpression eliminator.
SetIsStateful() should prevent two identical random ops from getting
merged.
"""
for dtype in dtypes.float16, dtypes.float32, dtypes.float64:
with self.cached_session():
rnd1 = random_ops.random_poisson(2.0, [24], dtype=dtype)
rnd2 = random_ops.random_poisson(2.0, [24], dtype=dtype)
diff = rnd2 - rnd1
# Since these are all positive integers, the norm will
# be at least 1 if they are different.
self.assertGreaterEqual(np.linalg.norm(diff.eval()), 1)
def testNoCSE(self):
"""CSE = constant subexpression eliminator.
SetIsStateful() should prevent two identical random ops from getting
merged.
"""
for dtype in dtypes.float16, dtypes.float32, dtypes.float64:
with self.cached_session(use_gpu=True):
rnd1 = random_ops.random_poisson(2.0, [24], dtype=dtype)
rnd2 = random_ops.random_poisson(2.0, [24], dtype=dtype)
diff = rnd2 - rnd1
# Since these are all positive integers, the norm will
# be at least 1 if they are different.
self.assertGreaterEqual(np.linalg.norm(diff.eval()), 1)
def resample_at_rate(inputs, rates, scope=None, seed=None, back_prop=False):
"""Given `inputs` tensors, stochastically resamples each at a given rate.
For example, if the inputs are `[[a1, a2], [b1, b2]]` and the rates
tensor contains `[3, 1]`, then the return value may look like `[[a1,
a2, a1, a1], [b1, b2, b1, b1]]`. However, many other outputs are
possible, since this is stochastic -- averaged over many repeated
calls, each set of inputs should appear in the output `rate` times
the number of invocations.
Args:
inputs: A list of tensors, each of which has a shape of `[batch_size, ...]`
rates: A tensor of shape `[batch_size]` contiaining the resampling rates
for each input.
scope: Scope for the op.
seed: Random seed to use.
back_prop: Whether to allow back-propagation through this op.
Returns:
Selections from the input tensors.
"""
with ops.name_scope(scope, default_name='resample_at_rate',
values=list(inputs) + [rates]):
rates = ops.convert_to_tensor(rates, name='rates')
# random_poisson does not support rates of size 0 (b/36076216)
sample_counts = math_ops.cast(control_flow_ops.cond(
array_ops.shape(rates)[0] > 0,
lambda: random_ops.random_poisson(rates, (), rates.dtype, seed=seed),
lambda: array_ops.zeros(shape=[0], dtype=rates.dtype)), dtypes.int32)
sample_indices = _repeat_range(sample_counts)
if not back_prop:
sample_indices = array_ops.stop_gradient(sample_indices)
return [array_ops.gather(x, sample_indices) for x in inputs]
def resample_at_rate(inputs, rates, scope=None, seed=None, back_prop=False):
"""Given `inputs` tensors, stochastically resamples each at a given rate.
For example, if the inputs are `[[a1, a2], [b1, b2]]` and the rates
tensor contains `[3, 1]`, then the return value may look like `[[a1,
a2, a1, a1], [b1, b2, b1, b1]]`. However, many other outputs are
possible, since this is stochastic -- averaged over many repeated
calls, each set of inputs should appear in the output `rate` times
the number of invocations.
Args:
inputs: A list of tensors, each of which has a shape of `[batch_size, ...]`
rates: A tensor of shape `[batch_size]` contiaining the resampling rates
for each input.
scope: Scope for the op.
seed: Random seed to use.
back_prop: Whether to allow back-propagation through this op.
Returns:
Selections from the input tensors.
"""
with ops.name_scope(scope,
default_name='resample_at_rate',
values=list(inputs) + [rates]):
rates = ops.convert_to_tensor(rates, name='rates')
sample_counts = math_ops.cast(
random_ops.random_poisson(rates, (), rates.dtype, seed=seed),
dtypes.int32)
sample_indices = _repeat_range(sample_counts)
if not back_prop:
sample_indices = array_ops.stop_gradient(sample_indices)
return [array_ops.gather(x, sample_indices) for x in inputs]
def _sample_n(self, n, seed=None):
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
batch_size = (np.prod(self.batch_shape.as_list(), dtype=np.int32)
if self.batch_shape.is_fully_defined()
else math_ops.reduce_prod(self.batch_shape_tensor()))
ids = self._mixture_distribution.sample(
sample_shape=concat_vectors(
[n],
distribution_util.pick_vector(
self.is_scalar_batch(),
np.int32([]),
[batch_size])),
seed=distribution_util.gen_new_seed(
seed, "poisson_lognormal_quadrature_compound"))
# Stride `quadrature_size` for `batch_size` number of times.
offset = math_ops.range(start=0,
limit=batch_size * self._quadrature_size,
delta=self._quadrature_size,
dtype=ids.dtype)
ids += offset
rate = array_ops.gather(
array_ops.reshape(self.distribution.rate, shape=[-1]), ids)
rate = array_ops.reshape(
rate, shape=concat_vectors([n], self.batch_shape_tensor()))
return random_ops.random_poisson(
lam=rate, shape=[], dtype=self.dtype, seed=seed)
def testSizeTooLarge(self):
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
"overflow"):
rate = constant_op.constant(1.0, shape=(4, 4, 4, 4, 4))
self.evaluate(
random_ops.random_poisson(shape=[46902, 51188, 34063, 59195],
lam=rate))
def _sample_n(self, n, seed=None):
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
batch_size = (np.prod(self.batch_shape.as_list(), dtype=np.int32)
if self.batch_shape.is_fully_defined() else
math_ops.reduce_prod(self.batch_shape_tensor()))
ids = self._mixture_distribution.sample(
sample_shape=concat_vectors([n],
distribution_util.pick_vector(
self.is_scalar_batch(),
np.int32([]), [batch_size])),
seed=distribution_util.gen_new_seed(
seed, "poisson_lognormal_quadrature_compound"))
# Stride `quadrature_size` for `batch_size` number of times.
offset = math_ops.range(start=0,
limit=batch_size * self._quadrature_size,
delta=self._quadrature_size,
dtype=ids.dtype)
ids += offset
rate = array_ops.gather(
array_ops.reshape(self.distribution.rate, shape=[-1]), ids)
rate = array_ops.reshape(rate,
shape=concat_vectors(
[n], self.batch_shape_tensor()))
return random_ops.random_poisson(lam=rate,
shape=[],
dtype=self.dtype,
seed=seed)
def poisson(lam=1.0, size=None):
if size is None:
size = ()
elif np_utils.isscalar(size):
size = (size, )
return np_utils.tensor_to_ndarray(
random_ops.random_poisson(shape=size, lam=lam, dtype=np_dtypes.int64))
def func():
with self.session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
rng = random_ops.random_poisson(lam, [num], dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = self.evaluate(rng)
return ret
def func():
with self.session(use_gpu=use_gpu, graph=ops.Graph()) as sess:
rng = random_ops.random_poisson(lam, [num], dtype=dtype, seed=seed)
ret = np.empty([10, num])
for i in xrange(10):
ret[i, :] = sess.run(rng)
return ret
def _sample_n(self, n, seed=None):
# Here we use the fact that if:
# lam ~ Gamma(concentration=total_count, rate=(1-probs)/probs)
# then X ~ Poisson(lam) is Negative Binomially distributed.
rate = random_ops.random_gamma(shape=[n],
alpha=self.total_count,
beta=math_ops.exp(-self.logits),
dtype=self.dtype,
seed=seed)
return random_ops.random_poisson(rate,
shape=[],
dtype=self.dtype,
seed=distribution_util.gen_new_seed(
seed, "negative_binom"))
def _sample_n(self, n, seed=None):
# Here we use the fact that if:
# lam ~ Gamma(concentration=total_count, rate=(1-probs)/probs)
# then X ~ Poisson(lam) is Negative Binomially distributed.
rate = random_ops.random_gamma(
shape=[n],
alpha=self.total_count,
beta=math_ops.exp(-self.logits),
dtype=self.dtype,
seed=seed)
return random_ops.random_poisson(
rate,
shape=[],
dtype=self.dtype,
seed=distribution_util.gen_new_seed(seed, "negative_binom"))
def testShape(self):
# Fully known shape
rnd = random_ops.random_poisson(2.0, [150], seed=12345)
self.assertEqual([150], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([1, 2, 3]),
shape=[150],
seed=12345)
self.assertEqual([150, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([1, 2, 3]),
shape=[20, 30],
seed=12345)
self.assertEqual([20, 30, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32, shape=(2,)),
shape=[12],
seed=12345)
self.assertEqual([12, 2], rnd.get_shape().as_list())
# Partially known shape.
rnd = random_ops.random_poisson(
lam=array_ops.ones([7, 3]),
shape=array_ops.placeholder(dtypes.int32, shape=(1,)),
seed=12345)
self.assertEqual([None, 7, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([9, 6]),
shape=array_ops.placeholder(dtypes.int32, shape=(3,)),
seed=12345)
self.assertEqual([None, None, None, 9, 6], rnd.get_shape().as_list())
# Unknown shape.
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32),
shape=array_ops.placeholder(dtypes.int32),
seed=12345)
self.assertIs(None, rnd.get_shape().ndims)
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32),
shape=[50],
seed=12345)
self.assertIs(None, rnd.get_shape().ndims)
def testShape(self):
# Fully known shape
rnd = random_ops.random_poisson(2.0, [150], seed=12345)
self.assertEqual([150], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([1, 2, 3]),
shape=[150],
seed=12345)
self.assertEqual([150, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([1, 2, 3]),
shape=[20, 30],
seed=12345)
self.assertEqual([20, 30, 1, 2, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32, shape=(2,)),
shape=[12],
seed=12345)
self.assertEqual([12, 2], rnd.get_shape().as_list())
# Partially known shape.
rnd = random_ops.random_poisson(
lam=array_ops.ones([7, 3]),
shape=array_ops.placeholder(dtypes.int32, shape=(1,)),
seed=12345)
self.assertEqual([None, 7, 3], rnd.get_shape().as_list())
rnd = random_ops.random_poisson(
lam=array_ops.ones([9, 6]),
shape=array_ops.placeholder(dtypes.int32, shape=(3,)),
seed=12345)
self.assertEqual([None, None, None, 9, 6], rnd.get_shape().as_list())
# Unknown shape.
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32),
shape=array_ops.placeholder(dtypes.int32),
seed=12345)
self.assertIs(None, rnd.get_shape().ndims)
rnd = random_ops.random_poisson(
lam=array_ops.placeholder(dtypes.float32),
shape=[50],
seed=12345)
self.assertIs(None, rnd.get_shape().ndims)
def _sample_n(self, n, seed=None):
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
batch_size = self.batch_shape.num_elements()
if batch_size is None:
batch_size = math_ops.reduce_prod(self.batch_shape_tensor())
# We need to "sample extra" from the mixture distribution if it doesn't
# already specify a probs vector for each batch coordinate.
# We only support this kind of reduced broadcasting, i.e., there is exactly
# one probs vector for all batch dims or one for each.
ids = self._mixture_distribution.sample(
sample_shape=concat_vectors(
[n],
distribution_util.pick_vector(
self.mixture_distribution.is_scalar_batch(), [batch_size],
np.int32([]))),
seed=distribution_util.gen_new_seed(
seed, "poisson_lognormal_quadrature_compound"))
# We need to flatten batch dims in case mixture_distribution has its own
# batch dims.
ids = array_ops.reshape(ids,
shape=concat_vectors(
[n],
distribution_util.pick_vector(
self.is_scalar_batch(), np.int32([]),
np.int32([-1]))))
# Stride `quadrature_size` for `batch_size` number of times.
offset = math_ops.range(start=0,
limit=batch_size * self._quadrature_size,
delta=self._quadrature_size,
dtype=ids.dtype)
ids += offset
rate = array_ops.gather(
array_ops.reshape(self.distribution.rate, shape=[-1]), ids)
rate = array_ops.reshape(rate,
shape=concat_vectors(
[n], self.batch_shape_tensor()))
return random_ops.random_poisson(lam=rate,
shape=[],
dtype=self.dtype,
seed=seed)
def _sample_n(self, n, seed=None):
# Get ids as a [n, batch_size]-shaped matrix, unless batch_shape=[] then get
# ids as a [n]-shaped vector.
batch_size = self.batch_shape.num_elements()
if batch_size is None:
batch_size = math_ops.reduce_prod(self.batch_shape_tensor())
# We need to "sample extra" from the mixture distribution if it doesn't
# already specify a probs vector for each batch coordinate.
# We only support this kind of reduced broadcasting, i.e., there is exactly
# one probs vector for all batch dims or one for each.
ids = self._mixture_distribution.sample(
sample_shape=concat_vectors(
[n],
distribution_util.pick_vector(
self.mixture_distribution.is_scalar_batch(),
[batch_size],
np.int32([]))),
seed=distribution_util.gen_new_seed(
seed, "poisson_lognormal_quadrature_compound"))
# We need to flatten batch dims in case mixture_distribution has its own
# batch dims.
ids = array_ops.reshape(ids, shape=concat_vectors(
[n],
distribution_util.pick_vector(
self.is_scalar_batch(),
np.int32([]),
np.int32([-1]))))
# Stride `quadrature_size` for `batch_size` number of times.
offset = math_ops.range(start=0,
limit=batch_size * self._quadrature_size,
delta=self._quadrature_size,
dtype=ids.dtype)
ids += offset
rate = array_ops.gather(
array_ops.reshape(self.distribution.rate, shape=[-1]), ids)
rate = array_ops.reshape(
rate, shape=concat_vectors([n], self.batch_shape_tensor()))
return random_ops.random_poisson(
lam=rate, shape=[], dtype=self.dtype, seed=seed)
def MpcRandom_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
dtype = dtype_check_and_set(dtype)
#return random_ops.random_poisson(shape, mean, stddev, dtype, seed, name)
return random_ops.random_poisson(lam, shape, dtype, seed, name)
def loop_fn(_): return random_ops.random_poisson(lam=[1.3], shape=[3])
def testZeroShape(self):
with self.cached_session():
rnd = random_ops.random_poisson([], [], seed=12345)
self.assertEqual([0], rnd.get_shape().as_list())
self.assertAllClose(np.array([], dtype=np.float32), rnd.eval())
def poisson(lam=1.0, size=None):
if size is None:
size = ()
elif np_utils.isscalar(size):
size = (size,)
return random_ops.random_poisson(shape=size, lam=lam, dtype=np_dtypes.int_)
def _sample_n(self, n, seed=None):
return random_ops.random_poisson(
self.rate, [n], dtype=self.dtype, seed=seed)
def loop_fn(_): return random_ops.random_poisson(lam=[1.3], shape=[3])
def testZeroShape(self):
with self.cached_session():
rnd = random_ops.random_poisson([], [], seed=12345)
self.assertEqual([0], rnd.get_shape().as_list())
self.assertAllClose(np.array([], dtype=np.float32), self.evaluate(rnd))
def testInfRate(self): sample = random_ops.random_poisson(shape=[2], lam=np.inf) self.assertAllEqual([np.inf, np.inf], self.evaluate(sample))
