]h(SrSSKJr SSKJrJrJrJr SSKr SSK r SSK J r Jr SSKJrJr SSKJr SS KJr "S S 5rg) z0Container for the result of running optimization) OrderedDict)DictOptionalTupleUnionN)Method OptimizeArgs) get_loggerscan_optimize_csv)InferenceMetadata)RunSetc D\rSrSrSrS\SS4SjrS\4SjrS\S\ \ R\ 44S jr S \SS4S jr\S\\S 44S j5r\S\4Sj5r\S\ R4Sj5r\S\\ R4Sj5r\S\R04Sj5r\S\\R04Sj5r\S\\\ R844Sj5rSSS.S\S\S\S\ \ R\ 44SjjrSS\S\\\ \ R\ 444Sjjr S S\\SS4Sjjr!Sr"g)! CmdStanMLEz\ Container for outputs from CmdStan optimization. Created by :meth:`CmdStanModel.optimize`. runsetreturnNcUR[R:Xd$[SR UR55eXlUR 5UlUR RRn[U[5(deURUl URURS5 g)zInitialize object.z?Wrong runset method, expecting optimize runset, found method {}rN)methodrOPTIMIZE ValueErrorformatr_check_retcodes converged_args method_args isinstancer save_iterations_save_iterations_set_mle_attrs csv_files)selfr optimize_argss hC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\cmdstanpy/stanfit/mle.py__init__CmdStanMLE.__init__s}}/""(&"7  //1 ))55  <    '4&C&C F,,Q/0cSRURRURRRR S/S95nSRUSR URR5SR URR55nUR(d"SRU5nSRU5nU$)NzCmdStanMLE: model={}{}r)cmdz#{} csv_file: {} output_file: {}z z{} Warning: invalid estimate, z#{} optimization failed to converge.) rrmodelrrcomposejoinr! stdout_filesr)r"reprs r$__repr__CmdStanMLE.__repr__'s'.. KK  t{{00<<DDQBDO ;AA  KK -- . KK 00 1  ~~5<.B $U   *.{*;DO !r'.cUR$)z Names of estimated quantities, includes joint log probability, and all parameters, transformed parameters, and generated quantities. )rAr"s r$r=CmdStanMLE.column_namesGs !!!r'cUR$)z Returns object which contains CmdStan configuration as well as information about the names and structure of the inference method and model output variables. )r@rGs r$metadataCmdStanMLE.metadataOs~~r'cnUR(d[5RS5 UR$)z Returns all final estimates from the optimizer as a numpy.ndarray which contains all optimizer outputs, i.e., the value for `lp__` as well as all Stan program variables. 2Invalid estimate, optimization failed to converge.)rr warningrBrGs r$optimized_params_npCmdStanMLE.optimized_params_npXs*~~ L D yyr'cUR(d[5RS5 gUR(d[5RS5 UR$)z Returns all saved iterations from the optimizer and final estimate as a numpy.ndarray which contains all optimizer outputs, i.e., the value for `lp__` as well as all Stan program variables. lIntermediate iterations not saved to CSV output file. Rerun the optimize method with "save_iterations=True".NrM)rr rNrrCrGs r$optimized_iterations_np"CmdStanMLE.optimized_iterations_npesN$$ L I ~~ L D r'cURR5(d[5RS5 [R "UR /URS9$)z Returns all final estimates from the optimizer as a pandas.DataFrame which contains all optimizer outputs, i.e., the value for `lp__` as well as all Stan program variables. rMcolumns)rrr rNpd DataFramerBr=rGs r$optimized_params_pdCmdStanMLE.optimized_params_pdysI{{**,, L D ||TYYK1B1BCCr'cUR(d[5RS5 gUR(d[5RS5 [R "UR URS9$)z Returns all saved iterations from the optimizer and final estimate as a pandas.DataFrame which contains all optimizer outputs, i.e., the value for `lp__` as well as all Stan program variables. rRNrMrV)rr rNrrXrYrCr=rGs r$optimized_iterations_pd"CmdStanMLE.optimized_iterations_pds_$$ L I ~~ L D ||DOOT5F5FGGr'cURR5(d[5RS5 [ [ UR UR55$)z{ Returns all estimates from the optimizer, including `lp__` as a Python Dict. Only returns estimate from final iteration. rM)rrr rNrzipr=rBrGs r$optimized_params_dict CmdStanMLE.optimized_params_dictsH {{**,, L D 3t00$))<==r'FTinc_iterationswarnvarrdrecxXRR;a5[SUS3SRURR5-5eU(a1U(a*UR(d[ 5R S5 U(a8URR5(d[ 5R S5 U(aUR(a URnO URnURRURU5nURS:XdURS:Xa)[ 5R S5 UR5$U$![aD [SUS3SRURRR55-5ef=f) a Return a numpy.ndarray which contains the estimates for the for the named Stan program variable where the dimensions of the numpy.ndarray match the shape of the Stan program variable. This functionaltiy is also available via a shortcut using ``.`` - writing ``fit.a`` is a synonym for ``fit.stan_variable("a")`` :param var: variable name :param inc_iterations: When ``True`` and the intermediate estimates are included in the output, i.e., the optimizer was run with ``save_iterations=True``, then intermediate estimates are included. Default value is ``False``. See Also -------- CmdStanMLE.stan_variables CmdStanMCMC.stan_variable CmdStanPathfinder.stan_variable CmdStanVB.stan_variable CmdStanGQ.stan_variable CmdStanLaplace.stan_variable zUnknown variable name: z Available variables are z, rRrM)r zThe default behavior of CmdStanMLE.stan_variable() will change in a future release to always return a numpy.ndarray, even for scalar variables.)r@ stan_varsrr,rr rNrrrCrBextract_reshapeshapeitemKeyErrorkeys)r"rfrdredataouts r$r6CmdStanMLE.stan_variablesu> nn.. .)#/++-1YYt~~7O7O-PQ  N4+@+@ L I   3355 L D  d33??D99D "nn66s;KKCyyB#))t"3 $$@ xxz!J )#/++))DNN4499;<=  s8A0E+)E++AF9cURR5(d[5RS5 0nURR HnUR X1SS9X#'M U$)a$ Return a dictionary mapping Stan program variables names to the corresponding numpy.ndarray containing the inferred values. :param inc_iterations: When ``True`` and the intermediate estimates are included in the output, i.e., the optimizer was run with ``save_iterations=True``, then intermediate estimates are included. Default value is ``False``. See Also -------- CmdStanMLE.stan_variable CmdStanMCMC.stan_variables CmdStanPathfinder.stan_variables CmdStanVB.stan_variables CmdStanGQ.stan_variables CmdStanLaplace.stan_variables rMFrc)rrr rNr@rir6)r"rdresultnames r$stan_variablesCmdStanMLE.stan_variablessh,{{**,, L D NN,,D--%.FL- r'dirc:URRU5 g)a/ Move output CSV files to specified directory. If files were written to the temporary session directory, clean filename. E.g., save 'bernoulli-201912081451-1-5nm6as7u.csv' as 'bernoulli-201912081451-1.csv'. :param dir: directory path See Also -------- stanfit.RunSet.save_csvfiles cmdstanpy.from_csv N)r save_csvfiles)r"rws r$ryCmdStanMLE.save_csvfiless !!#&r')rCrAr@rBrrr)F)N)#__name__ __module__ __qualname____firstlineno____doc__rr%strr/rnpndarrayfloatr9r propertyrr=r rJrOrrSrXrYrZr]rfloat64raboolr6rury__static_attributes__rhr'r$rrs 1v1$1" # **bjj%.?(@*<3<4<"eCHo""+ RZZ  "**)=& DR\\ D DH",,)?HH& >tCO'< > > % E E E  E rzz5 ! EP&+" c5U*++ ,B'#'$''r'r)r collectionsrtypingrrrrnumpyrpandasrXcmdstanpy.cmdstan_argsrr cmdstanpy.utilsr r rJr rrrrhr'r$rs/6#//79'L'L'r'