M/PhdZddlZddlmZddlZddlZddlm Z ddl m Z ddl m Z Gdd e ZGd d ZdS) z4Canonical correlation analysis author: Yichuan Liu N)svd)Model)summary2)multivariate_statsc2eZdZdZdfd Zd dZdZxZS) CanCorra Canonical correlation analysis using singular value decomposition For matrices exog=x and endog=y, find projections x_cancoef and y_cancoef such that: x1 = x * x_cancoef, x1' * x1 is identity matrix y1 = y * y_cancoef, y1' * y1 is identity matrix and the correlation between x1 and y1 is maximized. Attributes ---------- endog : ndarray See Parameters. exog : ndarray See Parameters. cancorr : ndarray The canonical correlation values y_cancoef : ndarray The canonical coefficients for endog x_cancoef : ndarray The canonical coefficients for exog References ---------- .. [*] http://numerical.recipes/whp/notes/CanonCorrBySVD.pdf .. [*] http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.pdf .. [*] http://www.mathematica-journal.com/2014/06/canonical-correlation-analysis/ :0yE>noneNc ntj||f||d|||dS)N)missinghasconst)super__init___fit)selfendogexog tolerancer rkwargs __class__s `/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/multivariate/cancorr.pyrzCanCorr.__init__.sW Cg/7 C C;A C C C )ch|jj\}}|jj\}}tj||g}tj|j}||dz }tj|j}||dz }t|d\}} } | j} | |k} | t| krtd| dd| fxx| | zcc<t|d\} }}|j}||k} | t| krtd|dd| fxx|| zcc<t|j | d\}}tjfdttD|_| |ddd|f|_| |jddd|f|_dS)a=Fit the model A ValueError is raised if there are singular values smaller than the tolerance. The treatment of singular arrays might change in future. Parameters ---------- tolerance : float eigenvalue tolerance, values smaller than which is considered 0 rzexog is collinear.Nzendog is collinear.c Xg|]&}tdt|d'S)rr)maxmin).0iss r z CanCorr._fit..Xs/ M M M!QAaD! !5!5 M M Mr)rshapernprarraymeanrTsumlen ValueErrordotrangecancorr x_cancoef y_cancoef)rrnobsk_yvark_xvarkxyuxsxvxvx_dsmaskuysyvyvy_dsuvr s @rrz CanCorr._fit3sz' fy f FFF# $ $ HTY   q M HTZ  q MAYY BI~ 88::D ! !122 2 aaag"T("AYY BI~ 88::D ! !233 3 aaag"T("bdhhrllA&&1ax M M M MuSVV}} M M MNN 1QQQU8,,13qqq"1"u:..rc 4|jj\}}|jj\}}tj|jd}t jgdttt|dz dd}d}tt|dz ddD]*}|d||z z}||z }||z } ||z dz || z dzdz z } || zdz dz } || z} |dz| dzzdz dkr-tj || zdzdz |dz| dzzdz z } nd} | | zd| zz }tj|d| z }d|z |z |z| z }|j||j |d f<||j |d f<| |j |d f<||j |d f<||j |d f<tjj|| |}||j |df<,|jjddd}|j |ddf}t'|||||z dz }t)||S)aIApproximate F test Perform multivariate statistical tests of the hypothesis that there is no canonical correlation between endog and exog. For each canonical correlation, testing its significance based on Wilks' lambda. Returns ------- CanCorrTestResults instance )Canonical Correlation Wilks' lambdaNum DFDen DFF ValuePr > Fr)columnsindexrrBrCrDrErFrGN)rr"rr#powerr,pd DataFramelistr+r(sqrtlocscipystatsfsfrJvaluesrCanCorrTestResults)rr/r0r1 eigenvalsrTprodrpqrr>df1tdf2lmdFpvalindstats_mvs r corr_testzCanCorr.corr_test]sz' fy fHT\1--  &M&M&M#'c)nnq.@"b(I(I#J#JLLLs9~~)2r22  A A ! $ $D A A"q1uqyAo5AQaAa%CAvQ"Q&&Ga!e\A-!q&16/A2EFGGa%!a%-C(4Q''CSC#%+A48LOEIa00 1,0EIa( )%(EIak "%(EIak "&'EIal #;=##AsC00D%)EIak " k 2& #qqq&!&i&,fdVma6GII!%222r)r r N)r )__name__ __module__ __qualname____doc__rrrf __classcell__)rs@rr r sk< (/(/(/(/T73737373737373rr c$eZdZdZdZdZdZdS)rXz Canonical correlation results class Attributes ---------- stats : DataFrame Contain statistical tests results for each canonical correlation stats_mv : DataFrame Contain the multivariate statistical tests results c"||_||_dSN)rTre)rrTres rrzCanCorrTestResults.__init__s   rcN|Srn)summary__str__)rs rrqzCanCorrTestResults.__str__s||~~%%'''rctj}|d||j|ddi|ddi||j|S)NzCancorr resultsz,Multivariate Statistics and F Approximations)rSummary add_titleadd_dfrTadd_dictre)rsumms rrpzCanCorrTestResults.summarys~!! ())) DJ r2h ErJKKK DM""" rN)rgrhrirjrrqrprrrXrXsK  !!!(((rrX)rjnumpyr# numpy.linalgrrSpandasrNstatsmodels.base.modelrstatsmodels.iolibrmultivariate_olsrr rXryrrrs ((((((&&&&&&000000E3E3E3E3E3eE3E3E3Pr