M/Ph *dZddlZddZd dZddgZdS) zPrincipal Component Analysis Created on Tue Sep 29 20:11:23 2009 Author: josef-pktd TODO : add class for better reuse of results NTcxtj|}|r|d}ntj|jd}||z}tj|d}tj|\}}tj|} | ddd} |dd| f}|| }|dkr)||jdkr|ddd|f}|d|}|r|tj |z }tj ||} tj | |j |z} | | ||fS)aprincipal components with eigenvector decomposition similar to princomp in matlab Parameters ---------- data : ndarray, 2d data with observations by rows and variables in columns keepdim : int number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included normalize : bool if true, then eigenvectors are normalized by sqrt of eigenvalues demean : bool if true, then the column mean is subtracted from the data Returns ------- xreduced : ndarray, 2d, (nobs, nvars) projection of the data x on the kept eigenvectors factors : ndarray, 2d, (nobs, nfactors) factor matrix, given by np.dot(x, evecs) evals : ndarray, 2d, (nobs, nfactors) eigenvalues evecs : ndarray, 2d, (nobs, nfactors) eigenvectors, normalized if normalize is true Notes ----- See Also -------- pcasvd : principal component analysis using svd r)rowvarN) nparraymeanzerosshapecovlinalgeigargsortsqrtdotT) datakeepdim normalizedemeanxmxcovevalsevecsindicesfactorsxreduceds c/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/sandbox/tools/tools_pca.pypcar sEF A ! FF1II HQWQZ FA 6!A   D9==&&LE5jGdddmG !!!G) E 'NE{{w++aaaj!hwh%bgenn$fQGvguw''!+H WeU **c|j\}}tj|}|r|d}nd}||z}tj|jd\}}} tj|j|jj} |r6tj| ddd|f|ddd|fj|z} n|} |}d|f|dz|jddz z } | | ddd|f| d||ddd|ffS)aprincipal components with svd Parameters ---------- data : ndarray, 2d data with observations by rows and variables in columns keepdim : int number of eigenvectors to keep if keepdim is zero, then all eigenvectors are included demean : bool if true, then the column mean is subtracted from the data Returns ------- xreduced : ndarray, 2d, (nobs, nvars) projection of the data x on the kept eigenvectors factors : ndarray, 2d, (nobs, nfactors) factor matrix, given by np.dot(x, evecs) evals : ndarray, 2d, (nobs, nfactors) eigenvalues evecs : ndarray, 2d, (nobs, nfactors) eigenvectors, normalized if normalize is true See Also -------- pca : principal component analysis using eigenvector decomposition Notes ----- This does not have yet the normalize option of pca. rr) full_matricesNz print reassigning keepdim to max)r rrr r svdrr) rrrnobsnvarsrrUsvrrrs rpcasvdr+Vs'B*KD% A  FF1II FAimmACqm11GAq!fQS!# G46'!!!HWH*-q8G8}??!C*G33 qD!'!*Q, E WQQQxxZ(%/1QQQxxZ= HHr!r r+)rrT)rT)__doc__numpyrr r+__all__r!rr0s_E+E+E+E+R:I:I:I:Iz ( r!