# Source code for mars.tensor.random.pareto

#!/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 TensorPareto(TensorDistribution, TensorRandomOperandMixin):
_input_fields_ = ["a"]
_op_type_ = OperandDef.RAND_PARETO

_fields_ = "a", "size"
a = AnyField("a")
_func_name = "pareto"

def __call__(self, a, chunk_size=None):
return self.new_tensor([a], None, raw_chunk_size=chunk_size)

[docs]def pareto(random_state, a, size=None, chunk_size=None, gpu=None, dtype=None):
r"""
Draw samples from a Pareto II or Lomax distribution with
specified shape.

The Lomax or Pareto II distribution is a shifted Pareto
distribution. The classical Pareto distribution can be
obtained from the Lomax distribution by adding 1 and
multiplying by the scale parameter m (see Notes).  The
smallest value of the Lomax distribution is zero while for the
classical Pareto distribution it is mu, where the standard
Pareto distribution has location mu = 1.  Lomax can also
be considered as a simplified version of the Generalized
Pareto distribution (available in SciPy), with the scale set
to one and the location set to zero.

The Pareto distribution must be greater than zero, and is
unbounded above.  It is also known as the "80-20 rule".  In
this distribution, 80 percent of the weights are in the lowest
20 percent of the range, while the other 20 percent fill the
remaining 80 percent of the range.

Parameters
----------
a : float or array_like of floats
Shape of the distribution. Should be greater than zero.
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 a is a scalar.  Otherwise,
mt.array(a).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 Pareto distribution.

--------
scipy.stats.lomax : probability density function, distribution or
cumulative density function, etc.
scipy.stats.genpareto : probability density function, distribution or
cumulative density function, etc.

Notes
-----
The probability density for the Pareto distribution is

.. math:: p(x) = \frac{am^a}{x^{a+1}}

where :math:a is the shape and :math:m the scale.

The Pareto distribution, named after the Italian economist
Vilfredo Pareto, is a power law probability distribution
useful in many real world problems.  Outside the field of
economics it is generally referred to as the Bradford
distribution. Pareto developed the distribution to describe
the distribution of wealth in an economy.  It has also found
use in insurance, web page access statistics, oil field sizes,
projects in Sourceforge [1]_.  It is one of the so-called
"fat-tailed" distributions.

References
----------
.. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of
Sourceforge projects.
.. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.
.. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme
Values, Birkhauser Verlag, Basel, pp 23-30.
.. [4] Wikipedia, "Pareto distribution",
http://en.wikipedia.org/wiki/Pareto_distribution

Examples
--------
Draw samples from the distribution:

>>> import mars.tensor as mt

>>> a, m = 3., 2.  # shape and mode
>>> s = (mt.random.pareto(a, 1000) + 1) * m

Display the histogram of the samples, along with the probability
density function:

>>> import matplotlib.pyplot as plt
>>> count, bins, _ = plt.hist(s.execute(), 100, normed=True)
>>> fit = a*m**a / bins**(a+1)
>>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')
>>> plt.show()
"""
if dtype is None:
dtype = np.random.RandomState().pareto(handle_array(a), size=(0,)).dtype
size = random_state._handle_size(size)
seed = gen_random_seeds(1, random_state.to_numpy())[0]
op = TensorPareto(size=size, seed=seed, gpu=gpu, dtype=dtype)
return op(a, chunk_size=chunk_size)