def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
"""Draws `shape` samples from each of the given Poisson distribution(s).
`lam` is the rate parameter describing the distribution(s).
Example:
samples = tf.random_poisson([0.5, 1.5], [10])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random_poisson([12.2, 3.3], [7, 5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
Args:
lam: A Tensor or Python value or N-D array of type `dtype`.
`lam` provides the rate parameter(s) describing the poisson
distribution(s) to sample.
shape: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per "rate"-parameterized distribution.
dtype: The type of `lam` and the output: `float16`, `float32`, or
`float64`.
seed: A Python integer. Used to create a random seed for the distributions.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
Returns:
samples: a `Tensor` of shape `tf.concat(shape, tf.shape(lam))` with
values of type `dtype`.
"""
with ops.name_scope(name, "random_poisson", [lam, shape]):
lam = ops.convert_to_tensor(lam, name="lam", dtype=dtype)
shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops._random_poisson(shape,
lam,
seed=seed1,
seed2=seed2)
def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
"""Draws `shape` samples from each of the given Poisson distribution(s).
`lam` is the rate parameter describing the distribution(s).
Example:
samples = tf.random_poisson([0.5, 1.5], [10])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random_poisson([12.2, 3.3], [7, 5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
Args:
lam: A Tensor or Python value or N-D array of type `dtype`.
`lam` provides the rate parameter(s) describing the poisson
distribution(s) to sample.
shape: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per "rate"-parameterized distribution.
dtype: The type of `lam` and the output: `float16`, `float32`, or
`float64`.
seed: A Python integer. Used to create a random seed for the distributions.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
Returns:
samples: a `Tensor` of shape `tf.concat(shape, tf.shape(lam))` with
values of type `dtype`.
"""
with ops.name_scope(name, "random_poisson", [lam, shape]):
lam = ops.convert_to_tensor(lam, name="lam", dtype=dtype)
shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops._random_poisson(shape, lam, seed=seed1, seed2=seed2)