ó ,µ[c@sËdZddlZddlZddlZddlmZedƒddd„ƒZedƒddd„ƒZ d „Z ed ƒdddd „ƒZ edƒdd „ƒZ edƒdd „ƒZ dS(sS Utilities for generating random numbers, random sequences, and random selections. iÿÿÿÿN(tpy_random_stateig@cCs*gt|ƒD]}|j|dƒ^q S(sK Return sample sequence of length n from a power law distribution. i(tranget paretovariate(tntexponenttseedti((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pytpowerlaw_sequencesic CsÊ|dkrtdƒ‚n|dkr6tdƒ‚n|d}d|}xytrÅd|jƒ}|jƒ}t||d| ƒ}dd||}|||d|d||krMPqMqMW|S(s|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 : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`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.zipf_rv(alpha=2, xmin=3, seed=42) # doctest: +SKIP References ---------- .. [1] Luc Devroye, Non-Uniform Random Variate Generation, Springer-Verlag, New York, 1986. isxmin < 1sa <= 1.0gð?i(t ValueErrortTruetrandomtint( talphatxminRta1tbtutvtxtt((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pytzipf_rv"s0      $cCs\dg}tt|ƒƒ}x:tdt|ƒƒD]#}|j|||||ƒq1W|S(sEReturn normalized cumulative distribution from discrete distribution.gi(tfloattsumRtlentappend(t distributiontcdftpsumR((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pytcumulative_distributionbs  !ic Csddl}|dk r!|}n*|dk r<t|ƒ}ntjdƒ‚gt|ƒD]}|jƒ^qX}g|D]}|j||ƒd^qw} | S(s# 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 iÿÿÿÿNs8discrete_sequence: distribution or cdistribution missingi(tbisecttNoneRtnxt NetworkXErrorRR t bisect_left( RRt cdistributionRRRRtinputseqtstseq((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pytdiscrete_sequencels     %)cCsc|t|ƒkr!tdƒ‚ntƒ}x,t|ƒ|krX|jt||ƒƒq-Wt|ƒS(sReturn k items without replacement from a weighted sample. The input is a dictionary of items with weights as values. ssample larger than population(RRtsettaddtweighted_choicetlist(tmappingtkRtsample((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pytrandom_weighted_sample‹s  cCsW|jƒt|jƒƒ}x4|jƒD]&\}}||8}|dkr)|Sq)WdS(stReturn a single element from a weighted sample. The input is a dictionary of items with weights as values. iN(R Rtvaluestitems(R+RtrndR,tw((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pyR)™s   (t__doc__R tsystnetworkxRtnetworkx.utilsRRRRRR&R.R)(((s=lib/python2.7/site-packages/networkx/utils/random_sequence.pyts     ?