'YHhwDdZddlZddlmZddlZGddZ ddZy)zM Created on Fri Dec 19 11:29:18 2014 Author: Josef Perktold License: BSD-3 N)statsczeZdZdZ d dZedZedZedZedZ edZ d d Z d d Z y)PredictionResultsaE Results class for predictions. Parameters ---------- predicted_mean : ndarray The array containing the prediction means. var_pred_mean : ndarray The array of the variance of the prediction means. var_resid : ndarray The array of residual variances. df : int The degree of freedom used if dist is 't'. dist : {'norm', 't', object} Either a string for the normal or t distribution or another object that exposes a `ppf` method. row_labels : list[str] Row labels used in summary frame. Nc||_||_||_||_||_||dk(rt j |_d|_y|dk(r(t j|_|jf|_y||_d|_y)Nnormt) predictedvar_preddf var_resid row_labelsrrdist dist_argsr )selfpredicted_mean var_pred_meanr r rrs [/var/www/html/planif/env/lib/python3.12/site-packages/statsmodels/regression/_prediction.py__init__zPredictionResults.__init__$so'% "$ <46> DIDN S[DI"ggZDNDIDNcZtj|j|jzSN)npsqrtrr rs rse_obszPredictionResults.se_obs6s wwt))DNN:;;rc|jSr)sers rse_meanzPredictionResults.se_mean:s wwrc|jSr)r rs rrz PredictionResults.predicted_mean>s~~rc|jSr)r rs rrzPredictionResults.var_pred_meanCs}}rc@tj|jSr)rrrrs rrzPredictionResults.seHswwt))**rc|r |jn |j}|jjd|dz z g|j}|j ||zz }|j ||zz}t j||fS)a. Returns the confidence interval of the value, `effect` of the constraint. This is currently only available for t and z tests. Parameters ---------- alpha : float, optional The significance level for the confidence interval. ie., The default `alpha` = .05 returns a 95% confidence interval. Returns ------- ci : ndarray, (k_constraints, 2) The array has the lower and the upper limit of the confidence interval in the columns. g@)rrrppfrrr column_stack)robsalpharqloweruppers rconf_intzPredictionResults.conf_intMsu( T[[T\\ DIIMM!ebj. :4>> :##a"f,##a"f,u~..rc`|j|d}|j|d}i}|j|d<|j|d<|dddf|d<|dddf|d <|dddf|d <|dddf|d <||_t j ||j |j }|S) NT)r(r'Fmeanmean_ser mean_ci_lowerr$ mean_ci_upper obs_ci_lower obs_ci_upper)indexcolumns)r,rrtablepd DataFramerkeys)rr(ci_obsci_mean to_includeress r summary_framezPredictionResults.summary_framehsU5--e-7 !00 6 $ 9&-adm ?#&-adm ?#%+AqD\ >"%+AqD\ >" ll:T__#-??#46 r)NNN)F皙?)r?) __name__ __module__ __qualname____doc__rpropertyrrrrrr,r>rrrrrs*15 $<<++/6rrc|rtt|jdr^|\ddlm}t |t j rt j|}||jjj|}||t|dd}t|rd}tj|}|jdk(r/|jj ddkDr |dddf}n |dddf}tj"|}nR|jj$}|t|jdd}|!t|jjdd}|]tj|}|j&dkDr9|jdk7s|j d|j dk(r t)d |i}|jj*|j|fi|}|j-}|tj.||j0j0zj3d} |j4} |j6d k(r|j8d } || |z} d d g|j:} t=|| | |j>| |S)a Compute prediction results. Parameters ---------- exog : array_like, optional The values for which you want to predict. transform : bool, optional If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you'd need to log the data first. weights : array_like, optional Weights interpreted as in WLS, used for the variance of the predicted residual. row_labels : list A list of row labels to use. If not provided, read `exog` is available. **kwargs Some models can take additional keyword arguments, see the predict method of the model for the details. Returns ------- linear_model.PredictionResults The prediction results instance contains prediction and prediction variance and can on demand calculate confidence intervals and summary tables for the prediction of the mean and of new observations. formulaNr)dmatrixr4r$weightsrzweights has wrong shapez fixed scalescalerr )r rr) hasattrmodelpatsyrG isinstancer7Seriesr8data design_infogetattrcallablerasarrayndimparamsshape atleast_2dexogsize ValueErrorpredict cov_paramsdotTsumrIcov_typecov_kwdsuse_trdf_resid) rrX transformrHr pred_kwdsrGrcovbrr rs rget_predictionrg}s,FWTZZ38H! dBII &<<%Dtzz22D9    w5J #! zz$ 99>{{  #a'D!G}AtG}}}T"zz ?djj)T:G   ,EJ**W% LL1 "gmmA&6$**Q-&G67 7 'TZZ'' TGYGN ?? DBFF4022277:M I }} %MM'* W C= $D ^]I $ D(2 44r)NTNNN) rCnumpyrscipyrpandasr7rrgrrrrks1kk\=A.2Z4r