bi~ pdZddlmZddlmcmZddlmZ ddl m Z ddl m Z d dZd dZGd d e Zy) a, Copyright 2013 Steven Diamond Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ) annotationsN)settings)Leaf)scopesctj5|j xr|jcdddS#1swYyxYw)zCReturns true if expression is parameters-affine (and variable-free)N)r dpp_scope variables is_affineexprs `/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/cvxpy/expressions/constants/parameter.pyis_param_affiners<    9>>##8(8999s #AA c$|j S)z/Returns true if expression is not parametrized.) parametersr s r is_param_freers  cteZdZdZdZ d d fd ZdZd dZddZe dZ ddZ d Z d d Z xZS) ParameteraParameters in optimization problems. Parameters are constant expressions whose value may be specified after problem creation. The only way to modify a problem after its creation is through parameters. For example, you might choose to declare the hyper-parameters of a machine learning model to be Parameter objects; more generally, Parameters are useful for computing trade-off curves. rc |tj|_n||_|$tj|j|_n||_d|_d|_d|_tt|.||fi|d|_ y)NT) luget_idids PARAM_PREFIX_name_valuedeltagradientsuperr__init__ _is_constant)selfshapenamevaluerkwargs __class__s r r zParameter.__init__.sv :iikDGDG <NN+DGG95DJDJ    i'u?? rct|j|j|j|j|jgS)zLReturns info needed to reconstruct the expression besides the args. )r#rr%r attributesr"s r get_datazParameter.get_dataAs) DJJ DGGT__MMrc|jS)N)rr*s r r$zParameter.nameFs zzrc.tjryy)NFT)rdpp_scope_activer*s r is_constantzParameter.is_constantIs  " " $rciS)zGives the (sub/super)gradient of the expression w.r.t. each variable. Matrix expressions are vectorized, so the gradient is a matrix. Returns: A map of variable to SciPy CSC sparse matrix or None. r*s r gradzParameter.gradNs  rc|gS)z'Returns itself as a parameter. r1r*s r rzParameter.parametersYs v rc^tj|j|j}|gfS)zReturns the graph implementation of the object. Returns: A tuple of (affine expression, [constraints]). )r create_paramr#r)r"objs r canonicalizezParameter.canonicalize^s& oodjj$''2Ryrc|j}t|dkDrd|j|dSd|jdS)z'String to recreate the object. rz Parameter()) _get_attr_strlenr#)r"attr_strs r __repr__zParameter.__repr__gsA%%' x=1 (, H= = '+jj2 2r)r1NNN)r#zint | tuple[int, ...]r$z str | NonereturnNone)r>strr>bool)r>zlist[Parameter])__name__ __module__ __qualname____doc__ PARAM_COUNTr r+r$r/propertyr2rr7r= __classcell__)r's@r rr#skKQU !*!7A! !&N   3rrrA)rF __future__rcvxpy.lin_ops.lin_utilslin_ops lin_utilsrcvxpyrrcvxpy.expressions.leafrcvxpy.utilitiesrrrrr1rr rQs6#$$'"9 ! K3K3r