M/PhdZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z GddZGdd eZGd d eZdS) zM Created on Wed Feb 17 15:35:23 2021 Author: Josef Perktold License: BSD-3 N)stats)cache_readonly)_Grid cdf2prob_grid prob2cdf_grid_eval_bernstein_dd_eval_bernstein_2d_eval_bernstein_1dc\eZdZdZdZedZedZdZ dZ dZ dZ d S) BernsteinDistributionaDistribution based on Bernstein Polynomials on unit hypercube. Parameters ---------- cdf_grid : array_like cdf values on a equal spaced grid of the unit hypercube [0, 1]^d. The dimension of the arrays define how many random variables are included in the multivariate distribution. Attributes ---------- cdf_grid : grid of cdf values prob_grid : grid of cell or bin probabilities k_dim : (int) number of components, dimension of random variable k_grid : (tuple) shape of cdf_grid k_grid_product : (int) total number of bins in grid _grid : Grid instance with helper methods and attributes ctj|x|_}|j|_|j|_tjd|jD|_t|j|_ dS)Ncg|]}|dz S)).0is c/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/distributions/bernstein.py z2BernsteinDistribution.__init__..*s&@&@&@qqs&@&@&@) npasarraycdf_gridndimk_dimshapek_gridprodk_grid_productr_grid)selfrs r__init__zBernsteinDistribution.__init__&sb#%:h#7#77 ] n  g&@&@DK&@&@&@AA4;'' rctj|}tj|dkstj|dkrtd|jdkr |dddf}|jd}tj|dkr|g|z}d|D}tj||d\}}td|DsJ|t|z}t|}||S) aoCreate distribution instance from data using histogram binning. Classmethod to construct a distribution instance. Parameters ---------- data : array_like Data with observation in rows and random variables in columns. Data can be 1-dimensional in the univariate case. k_bins : int or list Number or edges of bins to be used in numpy histogramdd. If k_bins is a scalar int, then the number of bins of each component will be equal to it. Returns ------- Instance of a Bernstein distribution rrzdata needs to be in [0, 1]NcFg|]}tjd|z d|dzS)r)rlinspace)rnis rrz3BernsteinDistribution.from_data..Ks.AAAB BGQQ//AAArF)binsdensityc&g|]}|ddkS)rrr)reis rrz3BernsteinDistribution.from_data..Os +++2BqEQJ+++r) rrany ValueErrorrrsize histogramddalllenr)clsdatak_binsrr(cers r from_datazBernsteinDistribution.from_data-s(z$ 6$(   ;rvdQh// ;9:: : 9>>4=D 1  76??a  X%FAA&AAA~du===1+++++,,,,, SYY ##s8}}rc.t|jdS)N)prepend)rr)r s r prob_gridzBernsteinDistribution.prob_gridUsT]D9999rctj|}|jdkr|jdkr |dddf}t ||j}|S)acdf values evaluated at x. Parameters ---------- x : array_like Points of multivariate random variable at which cdf is evaluated. This can be a single point with length equal to the dimension of the random variable, or two dimensional with points (observations) in rows and random variables in columns. In the univariate case, a 1-dimensional x will be interpreted as different points for evaluation. Returns ------- pdf values Notes ----- Warning: 2-dim x with many points can be memory intensive because currently the bernstein polynomials will be evaluated in a fully vectorized computation. rN)rrrrrrr xcdf_s rcdfzBernsteinDistribution.cdfYsM. JqMM 6Q;;4:??!!!T' A!!T]33 rctj|}|jdkr|jdkr |dddf}|jt ||jz}|S)apdf values evaluated at x. Parameters ---------- x : array_like Points of multivariate random variable at which pdf is evaluated. This can be a single point with length equal to the dimension of the random variable, or two dimensional with points (observations) in rows and random variables in columns. In the univariate case, a 1-dimensional x will be interpreted as different points for evaluation. Returns ------- cdf values Notes ----- Warning: 2-dim x with many points can be memory intensive because currently the bernstein polynomials will be evaluated in a fully vectorized computation. rN)rrrrrrr:r r=pdf_s rpdfzBernsteinDistribution.pdfvsV. JqMM 6Q;;4:??!!!T' A"%74>%J%JJ rc|jdkr|Sdg|jz}tj|dkr|g}|D]}tddd||<|jt |}t |}|S)aFGet marginal BernsteinDistribution. Parameters ---------- idx : int or list of int Index or indices of the component for which the marginal distribution is returned. Returns ------- BernsteinDistribution instance for the marginal distribution. rr$rN)rrrslicertupler )r idxsliicdf_m bpd_marginals r get_marginalz"BernsteinDistribution.get_marginals :??KTDJ  8C==B  %C - -B4t,,BrFF eBii(,U33 rc tj||j}|j}g}t t|D]}||dkrtj||jj }g}t |D]w}|j |} |j j |||} | tj| | zdz| d| z zdz||x| tj|tj|} | S)zGenerate random numbers from distribution. Parameters ---------- nobs : int Number of random observations to generate. rr)r.)rrandom multinomialr:flattenrranger1 unravel_indexrrr x_marginalappendrbetarvs column_stack concatenate) r nobsrvs_mnlk_comprvs_mrrGrvsijnxgirvsms rrVzBernsteinDistribution.rvss?)''dn.D.D.F.FGGs7||$$ 4 4AqzQ&q$.*>??vAAA AA*/23q6:CKK q3w{A3K!O4;AJ!/!@!@AAAA R_T22333~e$$ rN) __name__ __module__ __qualname____doc__r! classmethodr7rr:r?rCrLrVrrrr r s&(((%%[%N::^::<6rr ceZdZdZdZdS)BernsteinDistributionBVc0t||j}|SN)r rr<s rr?zBernsteinDistributionBV.cdfs!!T]33 rc@|jt||jz}|Srj)rr r:rAs rrCzBernsteinDistributionBV.pdfs!"%74>%J%JJ rNrbrcrdr?rCrrrrhrhs2rrhceZdZddZddZdS)BernsteinDistributionUVbinomc4t||j|}|SN)method)r r)r r=rrr>s rr?zBernsteinDistributionUV.cdfs!!T]6BBB rcD|jt||j|z}|Srq)rr r:)r r=rrrBs rrCzBernsteinDistributionUV.pdfs3"%74>?E&G&G&GG rN)rorlrrrrnrns< rrn)renumpyrscipyrstatsmodels.tools.decoratorsrstatsmodels.distributions.toolsrrrrr r r rhrnrrrrxs2777777DDDDDDDDDDDDDDDD vvvvvvvvr     3        3     r