Dj4ddlZddlZddlmZGddZy)N)statsceZdZdZ d dZddZedZedZedZ edZ ed Z dd Z dd Z dd Zy)PredictionResultsa Prediction results Parameters ---------- predicted_mean : {ndarray, Series, DataFrame} The predicted mean values var_pred_mean : {ndarray, Series, DataFrame} The variance of the predicted mean values dist : {None, "norm", "t", rv_frozen} The distribution to use when constructing prediction intervals. Default is normal. df : int, optional The degree of freedom parameter for the t. Not used if dist is None, "norm" or a callable. row_labels : {Sequence[Hashable], pd.Index} Row labels to use for the summary frame. If None, attempts to read the index of ``predicted_mean`` Nctj||_tj||_||_||_|t |dd|_|j du|_|dk7r | td||dk(rtj|_ d|_ y|dk(r(tj|_ |jf|_ yt|tjj r||_ d|_ ytd)Nindextz$df must be None when dist is not "t"normz/dist must be a None, "norm", "t" or a callable.)npasarray_predicted_mean_var_pred_mean_df _row_labelsgetattr _use_pandas ValueErrorrr dist dist_argsr isinstance distributions rv_frozen)selfpredicted_mean var_pred_meanrdf row_labelss cC:\Crop_Prediction\Backend\crop-ai-system\venv\Lib\site-packages\statsmodels\tsa\base\prediction.py__init__zPredictionResults.__init__s "zz.9 jj7%  &~wED ++47 3;2>CD D <46> DIDN S[DI"hh[DN e11;; <DIDNNO Oc|js|S|jdk(r"tj||j|Stj ||j|S)N)rname)rcolumns)rndimpdSeriesr DataFrame)rvaluer#r$s r _wrap_pandaszPredictionResults._wrap_pandas:sML ::?99U$*:*:F F||E)9)97KKr c|jS)z$The row labels used in pandas-types.)rrs rrzPredictionResults.row_labelsAsr c:|j|jdS)zThe predicted meanr)r*r r,s rrz PredictionResults.predicted_meanFs  !5!57GHHr c|jjdkDr |jS|j|jdS)z"The variance of the predicted meanr)rr%r*r,s rrzPredictionResults.var_pred_meanKs=    # #a '&& &  !4!4oFFr c,|jj}|dk(r tj|j}nH|dk(r8tj|jjj }n t d|j|dS)z,The standard deviation of the predicted meanr"zvar_pre_mean must be 1 or 3 dimmean_se)rr%r sqrtTdiagonalNotImplementedErrorr*)rr%valuess rse_meanzPredictionResults.se_meanRsy""'' 19WWT001F QYWWT0022;;=>F%&GH H  33r cz|j|jz }t|tjrd|_|S)z9The ratio of the predicted mean to its standard deviationtvalues)rr8rr&r'r#)rvals rr:zPredictionResults.tvalues^s2!!DLL0 c299 % CH r c|j|z |jz }|dvrA|jjt j |g|j dz}||fS|dvr+|jj|g|j }||fS|dvr+|jj|g|j }||fStd)a8 z- or t-test for hypothesis that mean is equal to value Parameters ---------- value : array_like value under the null hypothesis alternative : str 'two-sided', 'larger', 'smaller' Returns ------- stat : ndarray test statistic pvalue : ndarray p-value of the hypothesis test, the distribution is given by the attribute of the instance, specified in `__init__`. Default if not specified is the normal distribution. ) two-sidedz2-sided2sr/)largerl)smallerszinvalid alternative) rr8rsfr absrcdfr)rr) alternativestatpvalues rt_testzPredictionResults.t_testfs*##e+t||; 8 8!TYY\\"&&,@@1DFV| O +!TYY\\$88F V| , ,"TYY]]49$..9FV|23 3r c0|j}|jjd|dz z g|j}|j||zz }|j||zz}t j ||f}|jr|j|ddgS|S)a  Confidence interval construction for the predicted mean. This is currently only available for t and z tests. Parameters ---------- alpha : float, optional The significance level for the prediction interval. The default `alpha` = .05 returns a 95% confidence interval. Returns ------- pi : {ndarray, DataFrame} The array has the lower and the upper limit of the prediction interval in the columns. r"g@lowerupper)r$) r8rppfrrr column_stackrr*)ralphaseqrKrLcis rconf_intzPredictionResults.conf_ints$\\ DIIMM!eck/ ;DNN ;##a"f,##a"f, __eU^ ,   $$R'71C$D D r ctj|j|}|dddf|dddf}}|j|j||d}t j |S)a Summary frame of mean, variance and confidence interval. Returns ------- DataFrame DataFrame containing four columns: * mean * mean_se * mean_ci_lower * mean_ci_upper Notes ----- Fixes alpha to 0.05 so that the confidence interval should have 95% coverage. )rONrr")meanr2 mean_ci_lower mean_ci_upper)r r rSrr8r&r()rrOci_meanrKrL to_includes r summary_framezPredictionResults.summary_framesd&**T]]]78q!t}gadmu''||""  ||J''r )NNN)NN)rr=)g?)__name__ __module__ __qualname____doc__rr*propertyrrrr8r:rIrSrZr r rrrs0  P>L  IIGG  4 4B6(r r)numpyr pandasr&scipyrrr r rrcsw(w(r