# Source code for mars.tensor.random.exponential

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
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# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import numpy as np

from ... import opcodes as OperandDef
from ...serialization.serializables import AnyField
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, handle_array, TensorDistribution

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)