o 0Gf@svdZddlZddlmZddlmZddlmZm Z m Z m Z m Z m Z GdddZGdd d eZGd d 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@sPeZdZdZddZeddZeddZdd Z d d Z d d Z ddZ dS)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 cCsHt||_}|j|_|j|_tdd|jD|_t |j|_ dS)NcSsg|]}|dqS)).0ir r Clib/python3.10/site-packages/statsmodels/distributions/bernstein.py *sz2BernsteinDistribution.__init__..) npasarraycdf_gridndimk_dimshapek_gridZprodk_grid_productr_grid)selfrr r r__init__&s zBernsteinDistribution.__init__cCst|}t|dkst|dkrtd|jdkr$|dddf}|jd}t|dkr5|g|}dd|D}tj||dd\}}td d|DsQJ|t |}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 rr zdata needs to be in [0, 1]NcSs"g|] }td|d|dqS)r )rZlinspace)r Znir r rrKs"z3BernsteinDistribution.from_data..F)binsZdensitycSsg|]}|ddkqS)r rr )r Zeir r rrOs) rrany ValueErrorrrsizeZ histogramddalllenr)clsdataZk_binsrrcerr r r from_data-s     zBernsteinDistribution.from_datacCst|jddS)N)Zprepend)rr)rr r r prob_gridUszBernsteinDistribution.prob_gridcCs>t|}|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. r N)rrrrrrrxcdf_r r rcdfYs  zBernsteinDistribution.cdfcCsDt|}|jdkr|jdkr|dddf}|jt||j}|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. r N)rrrrrrr)rr+pdf_r r rpdfvs zBernsteinDistribution.pdfcCsb|jdkr|Sdg|j}t|dkr|g}|D] }tddd||<q|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. r rr N)rrrslicertupler )ridxsliiZcdf_mZ bpd_marginalr r r get_marginals  z"BernsteinDistribution.get_marginalc Cstj||j}|j}g}tt|D]H}||dkr]t||jj }g}t|D])}|j |} |j j |||} | tjj| | d| d| d||dq+| t|qt|} | S)zGenerate random numbers from distribution. Parameters ---------- nobs : int Number of random observations to generate. rr )r!)rZrandomZ multinomialr)Zflattenrranger#Z unravel_indexrrrZ x_marginalappendrZbetarvsZ column_stackZ concatenate) rZnobsZrvs_mnlZk_compZrvs_mrr3ZrvsijnZxgiZrvsmr r rr9s"   "  zBernsteinDistribution.rvsN) __name__ __module__ __qualname____doc__r classmethodr(rr)r-r0r6r9r r r rr s '  r c@seZdZddZddZdS)BernsteinDistributionBVcCst||j}|SN)rrr*r r rr-s zBernsteinDistributionBV.cdfcCs|jt||j}|SrB)rrr)r.r r rr0szBernsteinDistributionBV.pdfNr<r=r>r-r0r r r rrAs rAc@s eZdZdddZdddZdS)BernsteinDistributionUVbinomcCst||j|d}|SN)method)r r)rr+rGr,r r rr-szBernsteinDistributionUV.cdfcCs|jt||j|d}|SrF)rr r))rr+rGr/r r rr0s zBernsteinDistributionUV.pdfN)rErCr r r rrDs rD)r?ZnumpyrZscipyrZstatsmodels.tools.decoratorsrZstatsmodels.distributions.toolsrrrrrr r rArDr r r rs   :