M/Ph_'xdZddlZddlmZGddZGddZedkrd Zej d Z ej Z d Z d Zee e e eZeed eejdd d\ZZeejdd d\ZZeeed\ZZer(ddlmZejedejdej Z dZee eZ ejdddZ!ee e!ee e!er+ej"e #ejddddej$dejdej"Z%e&gdD]h\Z'Z(e%)dde'd ze #ejddde(ej$dejde(zid Z(e e(d!"Zej"ejed#ejd$e(zdSdSdS)%a)Parametric Mixture Distributions Created on Sat Jun 04 2011 Author: Josef Perktold Notes: Compound Poisson has mass point at zero https://en.wikipedia.org/wiki/Compound_Poisson_distribution and would need special treatment need a distribution that has discrete mass points and contiuous range, e.g. compound Poisson, Tweedie (for some parameter range), pdf of Tobit model (?) - truncation with clipping Question: Metaclasses and class factories for generating new distributions from existing distributions by transformation, mixing, compounding N)statsc0eZdZdZ d dZd dZdZdZdS) ParametricMixtureDamixtures with a discrete distribution The mixing distribution is a discrete distribution like scipy.stats.poisson. All distribution in the mixture of the same type and parametrized by the outcome of the mixing distribution and have to be a continuous distribution (or have a pdf method). As an example, a mixture of normal distributed random variables with Poisson as the mixing distribution. assumes vectorized shape, loc and scale as in scipy.stats.distributions assume mixing_dist is frozen initialization looks fragile for all possible cases of lower and upper bounds of the distributions. MbP?c||_||_tj|jjs |jj}n|d}tj|jjs |jj}n| d}||_ ||_ tj ||dz}| ||_|||_|||_dS)acreate a mixture distribution Parameters ---------- mixing_dist : discrete frozen distribution mixing distribution base_dist : continuous distribution parametrized distributions in the mixture bd_args_func : callable function that builds the tuple of args for the base_dist. The function obtains as argument the values in the support of the mixing distribution and should return an empty tuple or a tuple of arrays. bd_kwds_func : callable function that builds the dictionary of kwds for the base_dist. The function obtains as argument the values in the support of the mixing distribution and should return an empty dictionary or a dictionary with arrays as values. cutoff : float If the mixing distribution has infinite support, then the distribution is truncated with approximately (subject to integer conversion) the cutoff probability in the missing tail. Random draws that are outside the truncated range are clipped, that is assigned to the highest or lowest value in the truncated support. g-C6?N) mixing_dist base_distnpisneginfdistappfisposinfbisfmambarangepmf mixing_probsbd_argsbd_kwds) selfr r bd_args_func bd_kwds_funccutoffloweruppermixing_supports k/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/sandbox/distributions/otherdist.py__init__zParametricMixtureD.__init__.s8'"{;+-.. *$&EEOOD))E{;+-.. *$&EEOOD))E5%'22'OON;;#|N33 #|N33 rcj|}tj|jjjz ttfdj D}fdj D}d|i}| |j jj i|}|fS)Nc3(K|] }|V dSN).0mdmrvs_idxs r! z)ParametricMixtureD.rvs..bs'<<8 <<<<<z*ParametricMixtureD.rvs..cs&FFFA1dl1oh/FFFr#size) r rvsr cliprrastypeinttuplerrupdater )rr/mrvsrrkwdsr0r*s` @r!r0zParametricMixtureD.rvs]s##D))GD$'4733dg=EEcJJ<<<>( A%4>%aG$,GGG$,GG4,,11"55X~r#ctj|}tj|dkr|d}|jj|g|jRi|j}||jzd}||fSr9) r r;r/r cdfrrrr=r>s r!rDzParametricMixtureD.cdfurBr#N)r)r)__name__ __module__ __qualname____doc__r"r0r<rDr'r#r!rrsj&-4-4-4-4^     r#rcBeZdZdZdZdZdZdZdZdZ dZ d Z d S) ClippedContinuousaclipped continuous distribution with a masspoint at clip_lower Notes ----- first version, to try out possible designs insufficient checks for valid arguments and not clear whether it works for distributions that have compact support clip_lower is fixed and independent of the distribution parameters. The clip_lower point in the pdf has to be interpreted as a mass point, i.e. different treatment in integration and expect function, which means none of the generic methods for this can be used. maybe this will be better designed as a mixture between a degenerate or discrete and a continuous distribution Warning: uses equality to check for clip_lower values in function arguments, since these are floating points, the comparison might fail if clip_lower values are not exactly equal. We could add a check whether the values are in a small neighborhood, but it would be expensive (need to search and check all values). c"||_||_dSr&)r clip_lower)rr rLs r!r"zClippedContinuous.__init__s"$r#cLd|vr|j}n|d}||fS)z@helper method to get clip_lower from kwds or attribute rL)rLpop)rr7rLs r!_get_clip_lowerz!ClippedContinuous._get_clip_lowers4 t # #JJ,//J4r#cl||\}}|jj|i|}||||k<|Sr&)rOr r0)rargsr7rLrvs_s r!r0zClippedContinuous.rvssG//55 D!t~!40400",TJ  r#cFtj|}d|vr|j}n|d}tj|jj|g|Ri|}||jk}tj|r|jj|g|Ri|}|||<d|||k<|SNrLr)r atleast_1drLrNr r<anyrD)rr?rQr7rLpdf_raw clip_mask clip_probs r!r<zClippedContinuous.pdfs M!   t # #JJ,//J- 2 21 Dt D D Dt D DEE$/) 6)   +**:EEEEEEI!*GI #$Jr#cd|vr|j}n|d}|jj|g|Ri|}d|||k<|SrT)rLrNr rD)rr?rQr7rLcdf_raws r!rDzClippedContinuous.cdfsb t # #JJ,//J$$.$Q666666#$Jr#cd|vr|j}n|d}|jj|g|Ri|}d|||k<|S)NrLr)rLrNr sf)rr?rQr7rLsf_raws r!r]zClippedContinuous.sfs` t # #JJ,//J""14t444t44"#qJ r#ctr&)NotImplementedError)rr?rQr7s r!rzClippedContinuous.ppfs!!r#c||\}}|j|g|Ri|}tj|dzg|||kf}ddlm}|j|dz ||j|g|Ri|}|jdt||dz|j ||j|g|Ri||j |g|gddddS) Ngư>r皙?g?zb-bozr-)linefmt markerfmtbasefmt) rOr<r concatenatematplotlib.pyplotpyplotxlimmaxylimplotstem) rr?rQr7rLmassxrpltxpdfs r!rmzClippedContinuous.plots=//55 Dtx 2T222T22 ^jo.!J,@ A A'''''' C)))tx)D)))D))Cdhhjj))#-...XTXb04000400111*vt = = = =r#N) rErFrGrHr"rOr0r<rDr]rrmr'r#r!rJrJs2%%%   $(   """r#rJ__main__r@cdS)Nr'r'r?s r!rws2r#c6|dtj|zdS)Nrb)locscale)r ones_likervs r!rwrws1s2<??/BCCr#i)r/d)binsz'poisson mixture of normal distributionsg:0yE> r:3?)r?ryrzzclipped normal distribution)rrg?rtzclipped normal, loc = %3.2fg?i)ryr/2zclipped normal rvs, loc = %3.2f)*rHnumpyr scipyrrrJrEdoplotspoissonmdistnormbdist bd_args_fn bd_kwds_fnpdprintr<linspacepbprDpcbpcr0mrhrirqhisttitle clip_lower_cnormr?figurermsqrtfig enumerateiry add_subplotr'r#r!rs.````````Jwwwwwwwwx zG EM"  E JEJCCJ  E5*j A AB E"&&)) FF;2;qB'' ( (EArff[R[2b))**GB E"&&((OOO VVV  FC=''''''S!!!! ;<<< JEK  e[ 1 1E D!R  A E%))A,, E%))A,,;   {r{2q"--3gbgajj III /000cjlli 0 0 011 ; ;FAs OOAa! $ $ $ JJ;2;r1b11s'"'!**J M M M CI3c9 : : : :iiCdi++ 2 3c9:::::iD;;r#