"""
Utilities for generating random numbers, random sequences, and
random selections.
"""
import networkx as nx
from networkx.utils import py_random_state
# The same helpers for choosing random sequences from distributions
# uses Python's random module
# https://docs.python.org/3/library/random.html
[docs]@py_random_state(2)
def powerlaw_sequence(n, exponent=2.0, seed=None):
"""
Return sample sequence of length n from a power law distribution.
"""
return [seed.paretovariate(exponent - 1) for i in range(n)]
[docs]@py_random_state(2)
def zipf_rv(alpha, xmin=1, seed=None):
r"""Returns a random value chosen from the Zipf distribution.
The return value is an integer drawn from the probability distribution
.. math::
p(x)=\frac{x^{-\alpha}}{\zeta(\alpha, x_{\min})},
where $\zeta(\alpha, x_{\min})$ is the Hurwitz zeta function.
Parameters
----------
alpha : float
Exponent value of the distribution
xmin : int
Minimum value
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
Returns
-------
x : int
Random value from Zipf distribution
Raises
------
ValueError:
If xmin < 1 or
If alpha <= 1
Notes
-----
The rejection algorithm generates random values for a the power-law
distribution in uniformly bounded expected time dependent on
parameters. See [1]_ for details on its operation.
Examples
--------
>>> nx.utils.zipf_rv(alpha=2, xmin=3, seed=42)
8
References
----------
.. [1] Luc Devroye, Non-Uniform Random Variate Generation,
Springer-Verlag, New York, 1986.
"""
if xmin < 1:
raise ValueError("xmin < 1")
if alpha <= 1:
raise ValueError("a <= 1.0")
a1 = alpha - 1.0
b = 2 ** a1
while True:
u = 1.0 - seed.random() # u in (0,1]
v = seed.random() # v in [0,1)
x = int(xmin * u ** -(1.0 / a1))
t = (1.0 + (1.0 / x)) ** a1
if v * x * (t - 1.0) / (b - 1.0) <= t / b:
break
return x
[docs]def cumulative_distribution(distribution):
"""Returns normalized cumulative distribution from discrete distribution."""
cdf = [0.0]
psum = float(sum(distribution))
for i in range(0, len(distribution)):
cdf.append(cdf[i] + distribution[i] / psum)
return cdf
[docs]@py_random_state(3)
def discrete_sequence(n, distribution=None, cdistribution=None, seed=None):
"""
Return sample sequence of length n from a given discrete distribution
or discrete cumulative distribution.
One of the following must be specified.
distribution = histogram of values, will be normalized
cdistribution = normalized discrete cumulative distribution
"""
import bisect
if cdistribution is not None:
cdf = cdistribution
elif distribution is not None:
cdf = cumulative_distribution(distribution)
else:
raise nx.NetworkXError(
"discrete_sequence: distribution or cdistribution missing"
)
# get a uniform random number
inputseq = [seed.random() for i in range(n)]
# choose from CDF
seq = [bisect.bisect_left(cdf, s) - 1 for s in inputseq]
return seq
[docs]@py_random_state(2)
def random_weighted_sample(mapping, k, seed=None):
"""Returns k items without replacement from a weighted sample.
The input is a dictionary of items with weights as values.
"""
if k > len(mapping):
raise ValueError("sample larger than population")
sample = set()
while len(sample) < k:
sample.add(weighted_choice(mapping, seed))
return list(sample)
[docs]@py_random_state(1)
def weighted_choice(mapping, seed=None):
"""Returns a single element from a weighted sample.
The input is a dictionary of items with weights as values.
"""
# use roulette method
rnd = seed.random() * sum(mapping.values())
for k, w in mapping.items():
rnd -= w
if rnd < 0:
return k