o `^h @sddlmZddlmZddlZddlZddlmZm Z m Z m Z ddl m Z ddlmZmZddlmZmZdd lmZGd d d ZGd d d ZGdddZGdddZGdddZdS))suppress) signatureN)BoundsLinearConstraintNonlinearConstraintOptimizeResult)PreparedConstraint) PRINT_OPTIONSBARRIER)CallbackSuccessget_arrays_tol)exact_1d_arrayc@s8eZdZdZddZddZeddZedd Zd S) ObjectiveFunctionz) Real-valued objective function. cGsP|r|dus t|s Jt|tsJt|tsJ||_||_||_d|_dS)a Initialize the objective function. Parameters ---------- fun : {callable, None} Function to evaluate, or None. ``fun(x, *args) -> float`` where ``x`` is an array with shape (n,) and `args` is a tuple. verbose : bool Whether to print the function evaluations. debug : bool Whether to make debugging tests during the execution. *args : tuple Additional arguments to be passed to the function. Nr)callable isinstancebool_fun_verbose_args_n_eval)selffunverbosedebugargsrW/home/air/shanriGPT/back/venv/lib/python3.10/site-packages/scipy/_lib/cobyqa/problem.py__init__s zObjectiveFunction.__init__cCstj|td}|jdurd}|Stt|j|g|jR}|jd7_|jrOtjdit t |j d|d|Wd|S1sJwY|S)a Evaluate the objective function. Parameters ---------- x : array_like, shape (n,) Point at which the objective function is evaluated. Returns ------- float Function value at `x`. dtypeNr () = r) nparrayfloatrsqueezerrr printoptionsr printname)rxfrrr__call__6s  zObjectiveFunction.__call__cC|jS Number of function evaluations. Returns ------- int Number of function evaluations. )rrrrrn_evalO zObjectiveFunction.n_evalcCs:d}|jdurz|jj}W|Styd}Y|Sw|S) Name of the objective function. Returns ------- str Name of the objective function. Nr)r__name__AttributeError)rr*rrrr*[s   zObjectiveFunction.nameN) r6 __module__ __qualname____doc__rr-propertyr2r*rrrrrs  rc@sHeZdZdZddZeddZeddZdd Zd d Z d d Z dS)BoundConstraintsz. Bound constraints ``xl <= x <= xu``. cCst|jt|_t|jt|_tj |jt |j<tj|j t |j <t |j|j ko@t |jtjko@t |j tj k|_ t |jtj kt |j tjk|_t|t|jj|_dS)z Initialize the bound constraints. Parameters ---------- bounds : scipy.optimize.Bounds Bound constraints. N)r$r%lbr&_xlub_xuinfxlisnanxuall is_feasible count_nonzeromronessizepcs)rboundsrrrrss  zBoundConstraints.__init__cCr.)z| Lower bound. Returns ------- `numpy.ndarray`, shape (n,) Lower bound. )r>r1rrrrBr3zBoundConstraints.xlcCr.)z| Upper bound. Returns ------- `numpy.ndarray`, shape (n,) Upper bound. )r@r1rrrrDr3zBoundConstraints.xucCstj|td}||S)0 Evaluate the maximum constraint violation. Parameters ---------- x : array_like, shape (n,) Point at which the maximum constraint violation is evaluated. Returns ------- float Maximum constraint violation at `x`. r)r$asarrayr& violationrr+rrrmaxcvs zBoundConstraints.maxcvcCs|jr tdgS|j|S)Nr)rFr$r%rKrOrPrrrrOs  zBoundConstraints.violationcCs|jr t||j|jS|S)a Project a point onto the feasible set. Parameters ---------- x : array_like, shape (n,) Point to be projected. Returns ------- `numpy.ndarray`, shape (n,) Projection of `x` onto the feasible set. )rFr$cliprBrDrPrrrprojectszBoundConstraints.projectN) r6r8r9r:rr;rBrDrQrOrSrrrrr<ns    r<c@speZdZdZddZeddZeddZedd Zed d Z ed d Z eddZ ddZ ddZ dS)LinearConstraintszK Linear constraints ``a_ub @ x <= b_ub`` and ``a_eq @ x == b_eq``. cs|rt|ts J|D] }t|tsJq t|tsJtdf|_td|_tdf|_td|_ |D]a}t |j |j t |j |j k}t|rpt|j|j|f|_t|jd|j ||j |f|_ t|st|j|j||j| f|_t|j|j ||j | f|_q:d|jt|j<d|jt|j<t|jt|jB}t|j}|j|ddf|_|j||_|j|ddf|_|j||_ fdd|D|_dS)a2 Initialize the linear constraints. Parameters ---------- constraints : list of LinearConstraint Linear constraints. n : int Number of variables. debug : bool Whether to make debugging tests during the execution. r?r!Ncs$g|]}|jjrt|tqSr)ArJrr$rI).0cnrr s z.LinearConstraints.__init__..)rlistrrr$empty_a_ub_b_ub_a_eq_b_eqabsr?r=r anyvstacka_eqrV concatenateb_eqrEa_ubb_ubrCisinfrK)r constraintsrZr constraint is_equalityundef_ubundef_eqrrYrrsf               zLinearConstraints.__init__cCr.)z Left-hand side matrix of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear inequality constraints. )r^r1rrrrhr3zLinearConstraints.a_ubcCr.)z Right-hand side vector of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Right-hand side vector of the linear inequality constraints. )r_r1rrrri#r3zLinearConstraints.b_ubcCr.)z Left-hand side matrix of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear equality constraints. )r`r1rrrre/r3zLinearConstraints.a_eqcCr.)z Right-hand side vector of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Right-hand side vector of the linear equality constraints. )rar1rrrrg;r3zLinearConstraints.b_eqcC|jjSz Number of linear inequality constraints. Returns ------- int Number of linear inequality constraints. )rirJr1rrrm_ubG zLinearConstraints.m_ubcCrpz Number of linear equality constraints. Returns ------- int Number of linear equality constraints. )rgrJr1rrrm_eqSrszLinearConstraints.m_eqcCstj||ddS)rMr!initialr$maxrOrPrrrrQ_szLinearConstraints.maxcvcs.t|jrtfdd|jDStgS)Ncg|]}|qSrrOrWpcr+rrr[qz/LinearConstraints.violation..)lenrKr$rfr%rPrr~rrOos  zLinearConstraints.violationN)r6r8r9r:rr;rhrirergrrrurQrOrrrrrTs"D       rTc@sXeZdZdZddZddZeddZedd Zed d Z dd dZ dddZ d S)NonlinearConstraintszI Nonlinear constraints ``c_ub(x) <= 0`` and ``c_eq(x) == b_eq``. cCst|r#t|ts J|D] }t|tsJq t|tsJt|ts#J||_g|_||_d|_d|_d|_ |_ dS)aA Initialize the nonlinear constraints. Parameters ---------- constraints : list Nonlinear constraints. verbose : bool Whether to print the function evaluations. debug : bool Whether to make debugging tests during the execution. N) rr\rr _constraintsrKr_map_ub_map_eq_m_ub_m_eq)rrkrrrlrrrrzs zNonlinearConstraints.__init__c Cst|jsd|_|_tgtgfStj|td}t|jsg|_g|_ d|_d|_|jD]q}t |j sLt |}dd|_ dd|_ t||}nt||}d|j_|j|t|jj}|jd|jd}}t||}t|||k} |j || |j|| |jt| 7_|jt| 7_q0g} g} t|jD]\} }|j|} |jrtjd it/tt|j| jj}t|d|d | Wd n1swYWd n1swY|j | }|j| }| |}t|r=|jd|}|jd|}|tj k}||||}| ||tj k}||||}| || |}t|rZd |jd||jd|}||8}| |q|jrjt!| } ntg} |jryt!| } ntg} | j"|_| j"|_| | fS) a Calculates the residual (slack) for the constraints. Parameters ---------- x : array_like, shape (n,) Point at which the constraints are evaluated. Returns ------- `numpy.ndarray`, shape (m_nonlinear_ub,) Nonlinear inequality constraint slack values. `numpy.ndarray`, shape (m_nonlinear_eq,) Nonlinear equality constraint slack values. rrcSs|SNr)x0rrrz/NonlinearConstraints.__call__..cSsdS)Nr!r)rvrrrrrTr r"r#NrUr)#rrrrr$r%r&rKrrrjaccopyhessrr f_updatedappendarangerHrLr rbrG enumeraterr(r rr7r6r)rArfrJ)rr+rlrXr}idxr=r?arr_tolrmc_ubc_eqivalfun_nameeq_idxub_idxub_valrBrD finite_xl_v finite_xueq_valmidpointrrrr-s                            zNonlinearConstraints.__call__cC|jdur td|jS)a Number of nonlinear inequality constraints. Returns ------- int Number of nonlinear inequality constraints. Raises ------ ValueError If the number of nonlinear inequality constraints is unknown. Nz:The number of nonlinear inequality constraints is unknown.)r ValueErrorr1rrrrr zNonlinearConstraints.m_ubcCr)a Number of nonlinear equality constraints. Returns ------- int Number of nonlinear equality constraints. Raises ------ ValueError If the number of nonlinear equality constraints is unknown. Nz8The number of nonlinear equality constraints is unknown.)rrr1rrrrurzNonlinearConstraints.m_eqcCst|jr |jdjjSdS)r0r)rrKrnfevr1rrrr2/s zNonlinearConstraints.n_evalNcCstj|j|||dddSa Evaluate the maximum constraint violation. Parameters ---------- x : array_like, shape (n,) Point at which the maximum constraint violation is evaluated. cub_val : array_like, shape (m_nonlinear_ub,), optional Values of the nonlinear inequality constraints. If not provided, the nonlinear inequality constraints are evaluated at `x`. ceq_val : array_like, shape (m_nonlinear_eq,), optional Values of the nonlinear equality constraints. If not provided, the nonlinear equality constraints are evaluated at `x`. Returns ------- float Maximum constraint violation at `x`. )cub_valceq_valr!rvrxrr+rrrrrrQ>szNonlinearConstraints.maxcvcstfdd|jDS)Ncrzrr{r|r~rrr[Wrz2NonlinearConstraints.violation..)r$rfrKrrr~rrOVszNonlinearConstraints.violationNN) r6r8r9r:rr-r;rrrur2rQrOrrrrrusl    rc@seZdZdZddZd0ddZeddZed d Zed d Z ed dZ eddZ eddZ eddZ eddZeddZeddZeddZeddZedd Zed!d"Zed#d$Zed%d&Zd'd(Zd1d*d+Zd1d,d-Zd.d/Zd)S)2Problemz Optimization problem. c Cs| rVt|ts Jt|tsJt|tsJt|tsJt|ts%Jt|ts,Jt| ts3Jt| ts:J| rB| dksBJt| tsIJ| dksOJt| tsVJ||_||_ ||_ |durkt |skt d||_ t|d}|j} |jj| krtd| d|jjd| krtd| d t|j|j}|j|jkt|j|j|k@|_d |j|j|j|j|_t|j|j|j|j|j|_||_tt|j|j|j|j|_|j||j|_|j |j!dd|jf|j}tt"|jdd|jftj# |j$|jdd|jf|jt"|j!dd|jf||g|j%| |_ |oM|jj&oMt't(|jjoMt't(|jj}|rd |jj|jj|_)d |jj|jj|_*ttt+|j% t+|j%|_|j j |j j!|j*}tt"|j jt,|j)tj# |j j$|j j|j*t"|j j!t,|j)||g|j%| |_ |j|j*|j)|_nt+|j%|_)t-|j%|_*||_.| |_/g|_0g|_1g|_2| |_3| |_4g|_5g|_6g|_7dS) aY Initialize the nonlinear problem. The problem is preprocessed to remove all the variables that are fixed by the bound constraints. Parameters ---------- obj : ObjectiveFunction Objective function. x0 : array_like, shape (n,) Initial guess. bounds : BoundConstraints Bound constraints. linear : LinearConstraints Linear constraints. nonlinear : NonlinearConstraints Nonlinear constraints. callback : {callable, None} Callback function. feasibility_tol : float Tolerance on the constraint violation. scale : bool Whether to scale the problem according to the bounds. store_history : bool Whether to store the function evaluations. history_size : int Maximum number of function evaluations to store. filter_size : int Maximum number of points in the filter. debug : bool Whether to make debugging tests during the execution. rNz)The callback must be a callable function.z#The initial guess must be a vector.zThe bounds must have z elements.r z@The left-hand side matrices of the linear constraints must have z columns.rU)8rrr<rTrr&rint_obj_linear _nonlinearr TypeError _callbackrrJrBrrhshaper rDr$rb _fixed_idx _fixed_valrR _orig_boundsr_boundsrS_x0rgrerrArirZrFrEisfinite_scaling_factor_scaling_shiftrIdiagzeros_feasibility_tol _filter_size _fun_filter _maxcv_filter _x_filter_store_history _history_size _fun_history_maxcv_history _x_history)robjrrLlinear nonlinearcallbackfeasibility_tolscale store_history history_size filter_sizerrZtolrgrrrr_s0            zProblem.__init__r!c sTtj|td}||}||||\}}|||||jrN|j |j |j |t |j|j krN|jd|j d|j dtr`tr`t |jdk}n=trvtfddt|j|jD}n'trtfddt|j|jD}ntfddt|j|jD}|r*|j |j |j |tt |jdddD]Q}trt|j|}n,trt|j|}nt|j|pt|j|p|j|ko|j|k}|r|j||j||j|qt |j|jkr*|jd|jd|jd|jd urpt|j} z*||\} } } || } t| jd hkrYt| | d } |j| d n|| Wntyo}zt|d }~wwtrxtt|t|<t|t|<t t!tt t"t#|tt }t"t#|tt }||fS) a Evaluate the objective and nonlinear constraint functions. Parameters ---------- x : array_like, shape (n,) Point at which the functions are evaluated. penalty : float, optional Penalty parameter used to select the point in the filter to forward to the callback function. Returns ------- float Objective function value. `numpy.ndarray`, shape (m_nonlinear_ub,) Nonlinear inequality constraint function values. `numpy.ndarray`, shape (m_nonlinear_eq,) Nonlinear equality constraint function values. Raises ------ `cobyqa.utils.CallbackSuccess` If the callback function raises a ``StopIteration``. rrc3s0|]\}}t|r|kpt|VqdSrr$rCrW fun_filter maxcv_filter) maxcv_valrr 7  z#Problem.__call__..c3s0|]\}}t|r|kpt|VqdSrrr)fun_valrrrArc3s$|] \}}|kp |kVqdSrrrrrrrrKs  Nintermediate_result)r+r)r)$r$rNr&build_xrrrQrrrrrrrpoprCrrEziprrrangerrr best_evalset parametersr StopIterationr r ryminmaximumminimum)rr+penaltyx_fullrr include_pointk remove_pointsigx_bestfun_best_rexcrrrr- s                               zProblem.__call__cCrp)zt Number of variables. Returns ------- int Number of variables. )rrJr1rrrrZrsz Problem.ncCrp)z Number of variables in the original problem (with fixed variables). Returns ------- int Number of variables in the original problem (with fixed variables). )rrJr1rrrn_origrszProblem.n_origcCr.)z Initial guess. Returns ------- `numpy.ndarray`, shape (n,) Initial guess. )rr1rrrrr3z Problem.x0cCrpr/)rr2r1rrrr2rszProblem.n_evalcCrp)r4)rr*r1rrrrrszProblem.fun_namecCr.)z} Bound constraints. Returns ------- BoundConstraints Bound constraints. )rr1rrrrLr3zProblem.boundscCr.)z Linear constraints. Returns ------- LinearConstraints Linear constraints. )rr1rrrrr3zProblem.linearcCrp)z Number of bound constraints. Returns ------- int Number of bound constraints. )rLrHr1rrrm_boundsrszProblem.m_boundscCrprq)rrrr1rrr m_linear_ubrszProblem.m_linear_ubcCrprt)rrur1rrr m_linear_eqrszProblem.m_linear_eqcCrp)a  Number of nonlinear inequality constraints. Returns ------- int Number of nonlinear inequality constraints. Raises ------ ValueError If the number of nonlinear inequality constraints is not known. )rrrr1rrrm_nonlinear_ubzProblem.m_nonlinear_ubcCrp)a Number of nonlinear equality constraints. Returns ------- int Number of nonlinear equality constraints. Raises ------ ValueError If the number of nonlinear equality constraints is not known. )rrur1rrrm_nonlinear_eqrzProblem.m_nonlinear_eqcCtj|jtdS)z History of objective function evaluations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of objective function evaluations. r)r$r%rr&r1rrr fun_history( zProblem.fun_historycCr)z History of maximum constraint violations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of maximum constraint violations. r)r$r%rr&r1rrr maxcv_history4rzProblem.maxcv_historycCs`z%|jdks |jdkrWdS|jdks|jdkrWdS|jdkr#WdSWdSty/YdSw)z Type of the problem. The problem can be either 'unconstrained', 'bound-constrained', 'linearly constrained', or 'nonlinearly constrained'. Returns ------- str Type of the problem. rznonlinearly constrainedzlinearly constrainedzbound-constrained unconstrained)rrrrrrr1rrrtype@s   z Problem.typecCs |jdkS)z Whether the problem is a feasibility problem. Returns ------- bool Whether the problem is a feasibility problem. r5)rr1rrris_feasibility^s zProblem.is_feasibilitycCs<t|j}|j||j<||j|j||j<|j|S)a0 Build the full vector of variables from the reduced vector. Parameters ---------- x : array_like, shape (n,) Reduced vector of variables. Returns ------- `numpy.ndarray`, shape (n_orig,) Full vector of variables. ) r$r]rrrrrrrS)rr+rrrrrjs   zProblem.build_xNcCs,|j|||d}t|rtj|ddSdSr)rOr$rGry)rr+rrrOrrrrQ~s z Problem.maxcvcCsg}|jjs|j|}||t|jjr"|j|}||t|jjr5|j|||}||t|r>t |SdSr) rLrFrOrrrrKrr$rf)rr+rrrOblcnlcrrrrOs        zProblem.violationc Cst|jdkr ||jt|j}t|j}t|j}t|}t|r||j k}t|rat t ||sa||t ||k@}t |dkrY||t||kM}t|d}nt|rnt|d}nzt|tj} |||||| |<t t | r|t |k} t| d}nP| t | k} t | dkr| |t|| kM} t | dkr| |t|| kM} t| d}nt t |s|t |k}t|d}nt|d}|j||ddf||||fS)a Return the best point in the filter and the corresponding objective and nonlinear constraint function evaluations. Parameters ---------- penalty : float Penalty parameter Returns ------- `numpy.ndarray`, shape (n,) Best point. float Corresponding objective function value. float Corresponding maximum constraint violation. rr rN)rrrr$r%rrrrcrrErCnanminrGr flatnonzero full_likenanrLrS) rrrrx_filter finite_idx feasible_idx fun_min_idxr merit_filter min_maxcv_idx merit_min_idxrrrrsZ           zProblem.best_eval)r!r)r6r8r9r:rr-r;rZrrr2rrLrrrrrrrrrrrrQrOrrrrrrZsT ,                   r) contextlibrinspectrrnumpyr$scipy.optimizerrrrscipy.optimize._constraintsrsettingsr r utilsr r rrr<rTrrrrrrs   Z`(f