ó ŸÃÒYc@ssdZddlZddlZddlZdd„Zddd„Zd„Zddd„Z d „Z d „Z dS( sS Utilities for generating random numbers, random sequences, and random selections. iÿÿÿÿNg@cCs*gt|ƒD]}tj|dƒ^q S(sK Return sample sequence of length n from a power law distribution. i(trangetrandomt paretovariate(tntexponentti((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pytpowerlaw_sequencesic Csæ|dkrtdƒ‚n|dkr6tdƒ‚n|dk rRtj|ƒn|d}d|}xytrádtjƒ}tjƒ}t||d| ƒ}dd||}|||d|d||kriPqiqiW|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 : 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 [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?iN(t ValueErrortNoneRtseedtTruetint( talphatxminR ta1tbtutvtxtt((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pytzipf_rv s .       $cCs\dg}tt|ƒƒ}x:tdt|ƒƒD]#}|j|||||ƒq1W|S(sEReturn normalized cumulative distribution from discrete distribution.gi(tfloattsumRtlentappend(t distributiontcdftpsumR((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pytcumulative_distribution`s  !c Csddl}|dk r!|}n*|dk r<t|ƒ}ntjdƒ‚gt|ƒD]}tjƒ^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(tbisectRRtnxt NetworkXErrorRRt bisect_left( RRt cdistributionRRRtinputseqtstseq((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pytdiscrete_sequencejs     %)cCs`|t|ƒkr!tdƒ‚ntƒ}x)t|ƒ|krU|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(tmappingtktsample((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pytrandom_weighted_sampleˆs  cCsWtjƒt|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(RRtvaluestitems(R*trndR+tw((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pyR(•s   ( t__doc__RtsystnetworkxRRRRRR%R-R((((st/private/var/folders/w6/vb91730s7bb1k90y_rnhql1dhvdd44/T/pip-build-w4MwvS/networkx/networkx/utils/random_sequence.pyts    @