ó áp7]c @s1dZddlmZddlZddlmZmZdefd„ƒYZe dkr-ddl j Z ej dd eƒZejed ed d ed d ddgjeƒjejddƒfƒZe jedeƒZeededdddddddgƒZejƒZeejƒndS(s>Restricted least squares from pandas License: Simplified BSD iÿÿÿÿ(tprint_functionN(tGLStRegressionResultstRLScBs¤eZdZdd d„Zd Zed„ƒZd Zed„ƒZ d Z ed„ƒZ d Z ed„ƒZ d Zed„ƒZd Zed„ƒZd „ZRS( s Restricted general least squares model that handles linear constraints Parameters ---------- endog: array-like n length array containing the dependent variable exog: array-like n-by-p array of independent variables constr: array-like k-by-p array of linear constraints param (0.): array-like or scalar p-by-1 array (or scalar) of constraint parameters sigma (None): scalar or array-like The weighting matrix of the covariance. No scaling by default (OLS). If sigma is a scalar, then it is converted into an n-by-n diagonal matrix with sigma as each diagonal element. If sigma is an n-length array, then it is assumed to be a diagonal matrix with the given sigma on the diagonal (WLS). Notes ----- endog = exog * beta + epsilon weights' * constr * beta = param See Greene and Seaks, "The Restricted Least Squares Estimator: A Pedagogical Note", The Review of Economics and Statistics, 1991. gc Cs›|j\}}tj|ƒ}|jdkrDd|jd}} n|j\}} || krntdƒ‚n||_||_||_tj|ƒr½|dkr½tj |fƒ|}n||_ |dkrÛd}ntj|ƒrtj |ƒ|}ntj |ƒ}|jdkrNtj |ƒ|_tj tj|ƒƒ|_n0||_tjjtjj|jƒƒj|_tt|ƒj||ƒdS(Niis#Constraints and design do not aligngð?(tshapetnptasarraytndimt Exceptiontncoeffst nconstraintt constrainttisscalartonestparamtNonetsqueezetdiagtsigmatsqrtt cholsigmainvtlinalgtcholeskytpinvtTtsuperRt__init__( tselftendogtexogtconstrRRtNtQtKtP((s6lib/python2.7/site-packages/statsmodels/sandbox/rls.pyR)s0        'cCs|jdkrü|j}|j}tj||||fƒ}tj|jj|jƒ|d|…d|…fs † P.