ó
áp7]c           @  sJ	  d  Z  d d l m Z d d l Z d d l m Z d d l j Z	 d d l
 j Z d d l j j Z d d l j j j Z e j d d d g d d d	 g d d	 d
 g g ƒ Z e j d d d
 g ƒ Z e j e e ƒ Z e j d d ƒ Z d
 d d g d d
 d g d d d g d d d g g Z e j e ƒ j d d d … d d … f Z  d d d d g Z! d d d d g Z" g  e D] Z# e j$ e# ƒ ^ qrZ% e e! e% d d ƒe& e% ƒ e& d ƒ e& e e j e d ƒ k  j' d ƒ j( d ƒ ƒ e& e d4 e  k  j' d ƒ j( d ƒ ƒ e& e j) d „  d d ƒƒ e& e j) d „  d d ƒƒ e j* ƒ  Z+ e e+ j, e+ j- d d  ƒe e+ j( e j. d! ƒ d d  ƒe j/ e ƒ Z0 e j, e0 d" d ƒZ1 e e+ j, e1 d d# ƒe e j. d! ƒ e0 j( d ƒ d d# ƒe j2 ƒ  Z3 e e+ j, e3 j, d d# ƒe j4 e ƒ Z5 e j, e0 d" d ƒZ6 e e j- e6 d d# ƒe e j. d! ƒ e5 j( d ƒ d d# ƒe j7 e j d d g ƒ ƒ Z8 e& e8 j( ƒ e& e8 j, ƒ e j9 e j d d g ƒ d g ƒ Z: e& e: j( ƒ e& e: j, ƒ e j9 e j d g ƒ d d g ƒ Z: e& e: j( ƒ e& e: j, ƒ e j; e d d … d f e j< e d d … d d … f d$ e= ƒƒ Z> e> j? ƒ  Z@ e& e@ jA jB e j d d d g ƒ ƒ ƒ e j9 e j d g ƒ d d g ƒ Z: e& e: j( ƒ e j9 e j d g ƒ d d g ƒ Z: e& e@ jA jB e j d d d g ƒ ƒ ƒ e& e: j( ƒ e jC e e d ƒ ZD eD j d d ƒ ZE e eD j, e j, eE d" d ƒd d ƒeD jF ƒ  ZG eD j* ƒ  ZH e eD j- eH jI d d  ƒe eH j- eH jI d d  ƒe e jJ d! ƒ eG jI d d  ƒeD j4 eE ƒ ZK e j, eK d" d ƒZL eD j/ eE ƒ ZM e j, eM d" d ƒZN e jO eM d" d ƒZP e eH j( eM j( d ƒ d d# ƒe eH j- eP d d ƒe eH j, eN d d ƒe eG j, eL d d ƒd d d g Z# eD j$ e# ƒ ZQ e& eQ ƒ e& eE e j e# ƒ k  j' d ƒ j( d ƒ ƒ e& d% d& ƒ e& d% d' ƒ e& d( eQ d' ƒ d d d g Z# eD j$ e# ƒ ZR e& eR ƒ e& eE e j e# ƒ k  j' d ƒ j( d ƒ ƒ e& d% d) ƒ e& d% d* ƒ e& d( eR d* ƒ e eQ d' d d+ ƒe eR d* d d+ ƒe j d d
 d
 g ƒ ZS e j eS e d
 d ƒ ZT e jU d, d	 g d+ eD eH g d! ƒ ZV e jU d, d	 g d- e eT g d! ƒ Z e	 jW ƒ  ZX eX jY d# d# d ƒ e	 jZ e d d … d f e d d … d f d. d/ d0 ƒe	 j[ d1 ƒ eX jY d# d# d# ƒ e	 jZ e d d … d f e d d … d# f d. d/ d0 ƒe	 j[ d2 ƒ eX jY d# d# d! ƒ e	 jZ e d d … d f e d d … d# f d. d/ d0 ƒe	 j[ d3 ƒ d S(5   s   examples for multivariate normal and t distributions


Created on Fri Jun 03 16:00:26 2011

@author: josef


for comparison I used R mvtnorm version 0.9-96

iÿÿÿÿ(   t   print_functionN(   t   assert_array_almost_equalg      ð?g      à?g      è?g      ø?g333333ã?g       @g        t   sizei@B g      @gT=ô8gŸÔ?gPÚÕ?g„,i qá?gÃËG½ñÓ?gÅø˜½úûñ>gó!ê×â¬ð>g¶ÞkU?g½!±(H´?t   decimali   t    i    .i   c         C  s   |  t  d k  j d ƒ S(   Ni    iÿÿÿÿ(   t   xlit   all(   t   x(    (    sY   lib/python2.7/site-packages/statsmodels/sandbox/distributions/examples/ex_mvelliptical.pyt   <lambda>6   R   i † c         C  s   |  d t k  j d ƒ S(   N.i   (   .N(   t   Nonet   xliarrR   (   R   (    (    sY   lib/python2.7/site-packages/statsmodels/sandbox/distributions/examples/ex_mvelliptical.pyR   7   R   i   i   t   rowvari   t   prependt   Rg±(60_Ó?gQLÞ 3_Ó?t   diffg²¥¢°êÈ?g$ÄK&]éÈ?i   gš™™™™™Ù?iÐ  t   .t   alphag      Ð?s
   1 versus 0s
   2 versus 0s
   2 versus 1(   .N(\   t   __doc__t
   __future__R    t   numpyt   npt   numpy.testingR   t   matplotlib.pyplott   pyplott   pltt   statsmodels.apit   apit   smt%   statsmodels.distributions.mixture_rvst   distributionst   mixture_rvst   mixt+   statsmodels.sandbox.distributions.mv_normalt   sandboxt	   mv_normalt   mvdt   arrayt   cov3t   mut   MVNormalt   mvn3t   rvsR   R   t   asarrayt   TR	   R
   t   r_cdft   r_cdf_errorst   at   cdft   n_cdft   printR   t   meant	   expect_mct
   normalizedt   mvn3nt   covt   corrt   zerost	   normalizet   xnt   xn_covt   normalized2t   mvn3n2t   standardizet   xst   xs_covt   marginalt   mv2mt   conditionalt   mv2ct   OLSt   add_constantt   Truet   modt   fitt   rest   modelt   predictt   MVTt   mvt3t   xtt   standardizedt   mvt3st   mvt3nt   sigmat   eyet   xtst   xts_covt   xtnt   xtn_covt   corrcoeft   xtn_corrt	   mvt3_cdf0t	   mvt3_cdf1t   mu2t   mvn32t   mv_mixture_rvst   mdt   figuret   figt   add_subplott   plott   title(    (    (    sY   lib/python2.7/site-packages/statsmodels/sandbox/distributions/examples/ex_mvelliptical.pyt   <module>   sÒ   +"

/&%%$$G($$(%
+
+$$999