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import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import AnyField
from ..utils import gen_random_seeds
from .core import TensorDistribution, TensorRandomOperandMixin, handle_array
class TensorExponential(TensorDistribution, TensorRandomOperandMixin):
_input_fields_ = ["scale"]
_op_type_ = OperandDef.RAND_EXPONENTIAL
_fields_ = "scale", "size"
scale = AnyField("scale")
_func_name = "exponential"
def __call__(self, scale, chunk_size=None):
return self.new_tensor([scale], self.size, raw_chunk_size=chunk_size)
[docs]def exponential(
random_state, scale=1.0, size=None, chunk_size=None, gpu=None, dtype=None
):
r"""
Draw samples from an exponential distribution.
Its probability density function is
.. math:: f(x; \frac{1}{\beta}) = \frac{1}{\beta} \exp(-\frac{x}{\beta}),
for ``x > 0`` and 0 elsewhere. :math:`\beta` is the scale parameter,
which is the inverse of the rate parameter :math:`\lambda = 1/\beta`.
The rate parameter is an alternative, widely used parameterization
of the exponential distribution [3]_.
The exponential distribution is a continuous analogue of the
geometric distribution. It describes many common situations, such as
the size of raindrops measured over many rainstorms [1]_, or the time
between page requests to Wikipedia [2]_.
Parameters
----------
scale : float or array_like of floats
The scale parameter, :math:`\beta = 1/\lambda`.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. If size is ``None`` (default),
a single value is returned if ``scale`` is a scalar. Otherwise,
``np.array(scale).size`` samples are drawn.
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
dtype : data-type, optional
Data-type of the returned tensor.
Returns
-------
out : Tensor or scalar
Drawn samples from the parameterized exponential distribution.
References
----------
.. [1] Peyton Z. Peebles Jr., "Probability, Random Variables and
Random Signal Principles", 4th ed, 2001, p. 57.
.. [2] Wikipedia, "Poisson process",
http://en.wikipedia.org/wiki/Poisson_process
.. [3] Wikipedia, "Exponential distribution",
http://en.wikipedia.org/wiki/Exponential_distribution
"""
if dtype is None:
dtype = (
np.random.RandomState().exponential(handle_array(scale), size=(0,)).dtype
)
size = random_state._handle_size(size)
seed = gen_random_seeds(1, random_state.to_numpy())[0]
op = TensorExponential(seed=seed, size=size, gpu=gpu, dtype=dtype)
return op(scale, chunk_size=chunk_size)