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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.utils.random_sequence

"""
Utilities for generating random numbers, random sequences, and
random selections.
"""
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
import random
import sys
import networkx as nx
__author__ = '\n'.join(['Aric Hagberg (hagberg@lanl.gov)',
'Dan Schult(dschult@colgate.edu)',
'Ben Edwards(bedwards@cs.unm.edu)'])

import warnings as _warnings
[docs]def create_degree_sequence(n, sfunction=None, max_tries=50, **kwds): _warnings.warn("create_degree_sequence() is deprecated", DeprecationWarning) """ Attempt to create a valid degree sequence of length n using specified function sfunction(n,**kwds). Parameters ---------- n : int Length of degree sequence = number of nodes sfunction: function Function which returns a list of n real or integer values. Called as "sfunction(n,**kwds)". max_tries: int Max number of attempts at creating valid degree sequence. Notes ----- Repeatedly create a degree sequence by calling sfunction(n,**kwds) until achieving a valid degree sequence. If unsuccessful after max_tries attempts, raise an exception. For examples of sfunctions that return sequences of random numbers, see networkx.Utils. Examples -------- >>> from networkx.utils import uniform_sequence, create_degree_sequence >>> seq=create_degree_sequence(10,uniform_sequence) """ tries=0 max_deg=n while tries < max_tries: trialseq=sfunction(n,**kwds) # round to integer values in the range [0,max_deg] seq=[min(max_deg, max( int(round(s)),0 )) for s in trialseq] # if graphical return, else throw away and try again if nx.is_valid_degree_sequence(seq): return seq tries+=1 raise nx.NetworkXError(\ "Exceeded max (%d) attempts at a valid sequence."%max_tries) # The same helpers for choosing random sequences from distributions # uses Python's random module # http://www.python.org/doc/current/lib/module-random.html
[docs]def pareto_sequence(n,exponent=1.0): """ Return sample sequence of length n from a Pareto distribution. """ return [random.paretovariate(exponent) for i in range(n)]
[docs]def powerlaw_sequence(n,exponent=2.0): """ Return sample sequence of length n from a power law distribution. """ return [random.paretovariate(exponent-1) for i in range(n)]
[docs]def zipf_rv(alpha, xmin=1, seed=None): r"""Return 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 : int Seed value for random number generator 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  for details on its operation. Examples -------- >>> nx.zipf_rv(alpha=2, xmin=3, seed=42) # doctest: +SKIP References ---------- .. 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") if not seed is None: random.seed(seed) a1 = alpha - 1.0 b = 2**a1 while True: u = 1.0 - random.random() # u in (0,1] v = random.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 zipf_sequence(n, alpha=2.0, xmin=1): """Return a sample sequence of length n from a Zipf distribution with exponent parameter alpha and minimum value xmin. See Also -------- zipf_rv """ return [ zipf_rv(alpha,xmin) for _ in range(n)]
[docs]def uniform_sequence(n): """ Return sample sequence of length n from a uniform distribution. """ return [ random.uniform(0,n) for i in range(n)]
[docs]def cumulative_distribution(distribution): """Return normalized cumulative distribution from discrete distribution.""" cdf=[] cdf.append(0.0) psum=float(sum(distribution)) for i in range(0,len(distribution)): cdf.append(cdf[i]+distribution[i]/psum) return cdf
[docs]def discrete_sequence(n, distribution=None, cdistribution=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=[random.random() for i in range(n)] # choose from CDF seq=[bisect.bisect_left(cdf,s)-1 for s in inputseq] return seq
[docs]def random_weighted_sample(mapping, k): """Return 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)) return list(sample)
[docs]def weighted_choice(mapping): """Return a single element from a weighted sample. The input is a dictionary of items with weights as values. """ # use roulette method rnd = random.random() * sum(mapping.values()) for k, w in mapping.items(): rnd -= w if rnd < 0: return k