o ߗi@sddlZddlZddlZddlmZddlmZddlmZm Z dgZ ddZ gdZ gd Z gd Zgd Ze egZe egZdd d ZejjddZGdddeZdS)N) constraints) Distribution) broadcast_all lazy_propertyVonMisescCs,t|}|}|r|||}|s |SN)listpop)ycoefresultr [/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/distributions/von_mises.py _eval_polys r)g?g$ @g03@g,?N?g2t?gIx?gtHZr?) e3E?g-5?gՒ+Hub?gJNYgTPÂ?g'gZ?gUL+ߐg;^p?)?gY?g(z?g*O?gZ9?g.h?gӰ٩=5?) rg.kg?VmgtZOZ?g<Q g'8`?gP⥝gqJ:N?g;PJ4qcCs|dks |dks J|d}||}t|t|}|dkr#||}|}d|}|d|t|t|}t|dk||}|S)zX Returns ``log(I_order(x))`` for ``x > 0``, where `order` is either 0 or 1. rg@r)r _COEF_SMALLabslog _COEF_LARGEtorchwhere)xorderr smalllarger r r r_log_modified_bessel_fnCs "rc Cstj|jtj|jd}|sltjd|j|j|jd}|\}}}t t j |} d|| || } ||| } | d| |dk| | d| dkB} | rht| |d| |}|| B}|r|t j |dt j t j S)Ndtypedevice)rrr)rzerosshapeboolr allrandrunbindcosmathpiranyrsignacos) loc concentration proposal_rrdoneuu1u2u3zfcacceptr r r_rejection_sampleZs , r;cseZdZdZejejdZejZdZ dfdd Z ddZ e d d Z e d d Ze d dZeefddZdfdd ZeddZeddZe ddZZS)raX A circular von Mises distribution. This implementation uses polar coordinates. The ``loc`` and ``value`` args can be any real number (to facilitate unconstrained optimization), but are interpreted as angles modulo 2 pi. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> m = VonMises(torch.tensor([1.0]), torch.tensor([1.0])) >>> m.sample() # von Mises distributed with loc=1 and concentration=1 tensor([1.9777]) :param torch.Tensor loc: an angle in radians. :param torch.Tensor concentration: concentration parameter )r/r0FNcs6t||\|_|_|jj}t}t|||dSr)rr/r0r$rSizesuper__init__)selfr/r0 validate_args batch_shape event_shape __class__r rr>szVonMises.__init__cCsL|jr|||jt||j}|tdtjt |jdd}|S)Nr"rr) _validate_args_validate_sampler0rr)r/r*rr+r)r?valuelog_probr r rrIs  zVonMises.log_probcC|jtjSr)r/tordoubler?r r r_locz VonMises._loccCrJr)r0rKrrLrMr r r_concentrationrOzVonMises._concentrationcCsh|j}ddd|d}|d|d|}d|dd|}d||}t|dk||S)Nrr"gh㈵>)rPsqrtrr)r?kappataurho _proposal_r_proposal_r_taylorr r rrVs  zVonMises._proposal_rcCs@||}tj||jj|jjd}t|j|j|j | |jjS)a The sampling algorithm for the von Mises distribution is based on the following paper: D.J. Best and N.I. Fisher, "Efficient simulation of the von Mises distribution." Applied Statistics (1979): 152-157. Sampling is always done in double precision internally to avoid a hang in _rejection_sample() for small values of the concentration, which starts to happen for single precision around 1e-4 (see issue #88443). r) _extended_shaperemptyrNrr/r r;rPrVrK)r? sample_shaper$rr r rsamples  zVonMises.samplecsXzt|WSty+|jd}|j|}|j|}t||||dYSw)NrF)r@)r=expandNotImplementedError__dict__getr/r0type)r?rA _instancer@r/r0rCr rr\s    zVonMises.expandcC|jS)z8 The provided mean is the circular one. r/rMr r rmeansz VonMises.meancCrbrrcrMr r rmodesz VonMises.modecCs$dt|jddt|jddS)z< The provided variance is the circular one. rrEr)rr0exprMr r rvariances  zVonMises.variancer)__name__ __module__ __qualname____doc__rrealpositivearg_constraintssupport has_rsampler>rIrrNrPrVrno_gradr<r[r\propertyrdrerg __classcell__r r rCrrjs,      )r)r*r torch.jittorch.distributionsr torch.distributions.distributionrtorch.distributions.utilsrr__all__r_I0_COEF_SMALL_I0_COEF_LARGE_I1_COEF_SMALL_I1_COEF_LARGErrrjitscript_if_tracingr;rr r r rs$