a \ą:b!ă@s,dZddlZddd„Zd dd„ZddgZdS) z„Principal Component Analysis Created on Tue Sep 29 20:11:23 2009 Author: josef-pktd TODO : add class for better reuse of results éNTc Csňt |ˇ}|r| dˇ}nt |jdˇ}||8}tj|ddŤ}tj |ˇ\}}t |ˇ} | ddd…} |dd…| f}|| }|dkr¶||jdkr¶|dd…d|…f}|d|…}|rČ|t  |ˇ}t  ||ˇ} t  | |j ˇ|} | | ||fS)a×principal 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 ré)ZrowvarNé˙˙˙˙) ÚnpÚarrayÚmeanZzerosÚshapeZcovÚlinalgZeigZargsortZsqrtÚdotÚT) ÚdataÚkeepdimZ normalizeÚdemeanÚxÚmZxcovÚevalsZevecsÚindicesÚfactorsÚxreduced©rúBlib/python3.9/site-packages/statsmodels/sandbox/tools/tools_pca.pyÚpcas&#     rc Csě|j\}}t |ˇ}|r$| dˇ}nd}||8}tjj|jddŤ\}}} t |j|jˇj} |r’t | dd…d|…f|dd…d|…fjˇ|} n|} |}d|f|d|jdd} | | dd…d|…f| d|…|dd…d|…ffS)aŚprincipal 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)Z full_matricesNz print reassigning keepdim to maxé)rrrrrZsvdr r ) r r r ZnobsZnvarsrrÚUÚsÚvrrrrrrÚpcasvdWs!   4r)rrT)rT)Ú__doc__ZnumpyrrrÚ__all__rrrrÚs  I =