"gzdZddlZddlmZddlZddlZddlm Z ddl m Z ddl m Z Gdd e ZGd d Zy) z4Canonical correlation analysis author: Yichuan Liu N)svd)Model)summary2)multivariate_statsc2eZdZdZdfd ZddZdZxZS)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/ c Pt|||f||d||j|y)N)missinghasconst)super__init___fit)selfendogexog tolerancer r kwargs __class__s ]/var/www/dash_apps/app1/venv/lib/python3.12/site-packages/statsmodels/multivariate/cancorr.pyrzCanCorr.__init__.s5  Cg/7 C;A C )c `|jj\}}|jj\}}tj||g}tj |j}||j dz }tj |j}||j dz }t|d\}} } | j} | |kD} | jt| kr td| dd| fxx| | zcc<t|d\} }}|j}||kD} | jt| kr td|dd| fxx|| zcc<t|jj| d\}}}tj tt|Dcgc]}tdt ||dc}|_| j|ddd|f|_|j|jddd|f|_ycc}w)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.r)rshapernpminarraymeanrTsumlen ValueErrordotrangemaxcancorr x_cancoef y_cancoef)rrnobsk_yvark_xvarkxyuxsxvxvx_dsmaskuysyvyvy_dsusvis rrz CanCorr._fit3szz'' fyy f FFFF# $ HHTYY  q M HHTZZ  q MAY BI~ 88:D !12 2 ag"T("AY BI~ 88:D !23 3 ag"T("bddhhrlA&1axxuSV} M!QAaD! !5 MN 1QU8,133q"1"u:.!Ns6 H+c |jj\}}|jj\}}tj|j d}t jgdttt|dz dd}d}tt|dz ddD]M}|d||z z}||z }||z } ||z dz || z dzdz z } || zdz dz } || z} |dz| dzzdz dkDr.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<tjjj!|| |}||j|df<P|j"j$ddd}|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)columnsindexrr=r>r?r@rArBN)rrrrpowerr%pd DataFramelistr#r sqrtlocscipystatsfsfrEvaluesrCanCorrTestResults)rr(r)r* eigenvalsrOprodr:pqrr7df1tdf2lmdFpvalindstats_mvs r corr_testzCanCorr.corr_test]slzz'' fyy fHHT\\1-  &M#'c)nq.@"b(I#JLs9~)2r2 A A ! $ $D A A"q1uqyAo5AQaAa%CAvQ"Q&GGa!e\A-!q&16/A2EFGa%!a%-C((4Q'CSC#%+A48LLOEIIa00 1,0EIIa( )%(EIIak "%(EIIak "&'EIIal #;;==##AsC0D%)EIIak " + >kk  2& #q&!&i&,fdVma6GI!%22r):0yE>noneN)rb)__name__ __module__ __qualname____doc__rrra __classcell__)rs@rr r s< (/T73rr c"eZdZdZdZdZdZy)rSz 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 ||_||_yN)rOr`)rrOr`s rrzCanCorrTestResults.__init__s   rc>|jjSrk)summary__str__)rs rrnzCanCorrTestResults.__str__s||~%%''rctj}|jd|j|j|j ddi|j ddi|j|j |S)NzCancorr resultsz,Multivariate Statistics and F Approximations)rSummary add_titleadd_dfrOadd_dictr`)rsumms rrmzCanCorrTestResults.summarysd! () DJJ r2h ErJK DMM" rN)rdrerfrgrrnrmrrrSrSs !(rrS)rgnumpyr numpy.linalgrrNpandasrIstatsmodels.base.modelrstatsmodels.iolibrmultivariate_olsrr rSrvrrr}s: (&0E3eE3Pr