o i @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)selffunverbosedebugargsrS/home/kim/smarthome/.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`. ZdtypeNr () = r) nparrayfloatrZsqueezerrr 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) r4 __module__ __qualname____doc__rr+propertyr0r(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$lbr%_xlub_xuinfxlisnanxuall is_feasible count_nonzeromronessizepcs)rboundsrrrrss  zBoundConstraints.__init__cCr,)z| Lower bound. Returns ------- `numpy.ndarray`, shape (n,) Lower bound. )r<r/rrrr@r1zBoundConstraints.xlcCr,)z| Upper bound. Returns ------- `numpy.ndarray`, shape (n,) Upper bound. )r>r/rrrrBr1zBoundConstraints.xucCstj|td}||S)0 Evaluate the maximum constraint violation. Parameters ---------- x : array_like, shape (n,) Point at which the maximum constraint violation is evaluated. Returns ------- float Maximum constraint violation at `x`. r)r#asarrayr% violationrr)rrrmaxcvs zBoundConstraints.maxcvcCs|jr tdgS|j|S)Nr)rDr#r$rIrMrNrrrrMs  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. )rDr#clipr@rBrNrrrprojectszBoundConstraints.projectN) r4r6r7r8rr9r@rBrOrMrQrrrrr: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$g|]}|jjrt|tqSr)ArHrr#rG).0cnrr s z.LinearConstraints.__init__..)rlistrrr#empty_a_ub_b_ub_a_eq_b_eqabsr=r;r anyZvstacka_eqrT concatenateb_eqrCa_ubb_ubrAisinfrI)r constraintsrXr constraint is_equalityZundef_ubZundef_eqrrWrrsf               zLinearConstraints.__init__cCr,)z Left-hand side matrix of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear inequality constraints. )r\r/rrrrer1zLinearConstraints.a_ubcCr,)z Right-hand side vector of the linear inequality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Right-hand side vector of the linear inequality constraints. )r]r/rrrrf#r1zLinearConstraints.b_ubcCr,)z Left-hand side matrix of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Left-hand side matrix of the linear equality constraints. )r^r/rrrrb/r1zLinearConstraints.a_eqcCr,)z Right-hand side vector of the linear equality constraints. Returns ------- `numpy.ndarray`, shape (m, n) Right-hand side vector of the linear equality constraints. )r_r/rrrrd;r1zLinearConstraints.b_eqcC|jjSz Number of linear inequality constraints. Returns ------- int Number of linear inequality constraints. )rfrHr/rrrm_ubG zLinearConstraints.m_ubcCrkz Number of linear equality constraints. Returns ------- int Number of linear equality constraints. )rdrHr/rrrm_eqSrnzLinearConstraints.m_eqcCstj||ddS)rKr initialr#maxrMrNrrrrO_szLinearConstraints.maxcvcs.t|jrtfdd|jDStgS)Ncg|]}|qSrrMrUpcr)rrrYqz/LinearConstraints.violation..)lenrIr#rcr$rNrryrrMos  zLinearConstraints.violationN)r4r6r7r8rr9rerfrbrdrmrprOrMrrrrrRs"D       rRc@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) rrZrr _constraintsrIr_map_ub_map_eq_m_ub_m_eq)rrhrrrirrrrzs 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"NrSr)#r{r}rrr#r$r%rIr~rrZjaccopyZhessrrZ f_updatedappendZarangerFrJr r`rE enumeraterr&r rr5r4r'r?rcrH)rr)rirVrxidxr;r=Zarr_tolrjZc_ubZc_eqivalfun_nameZeq_idxZub_idxZub_valr@rBZ finite_xl_vZ finite_xuZeq_valZmidpointrrrr+s                            zNonlinearConstraints.__call__cC|jdur td|jS)a Number of nonlinear inequality constraints. Returns ------- int Number of nonlinear inequality constraints. Raises ------ ValueError If the number of nonlinear inequality constraints is unknown. Nz:The number of nonlinear inequality constraints is unknown.)r ValueErrorr/rrrrm zNonlinearConstraints.m_ubcCr)a Number of nonlinear equality constraints. Returns ------- int Number of nonlinear equality constraints. Raises ------ ValueError If the number of nonlinear equality constraints is unknown. Nz8The number of nonlinear equality constraints is unknown.)rrr/rrrrprzNonlinearConstraints.m_eqcCst|jr |jdjjSdS)r.r)r{rIrZnfevr/rrrr0/s zNonlinearConstraints.n_evalNcCstj|j|||dddSa Evaluate the maximum constraint violation. Parameters ---------- x : array_like, shape (n,) Point at which the maximum constraint violation is evaluated. cub_val : array_like, shape (m_nonlinear_ub,), optional Values of the nonlinear inequality constraints. If not provided, the nonlinear inequality constraints are evaluated at `x`. ceq_val : array_like, shape (m_nonlinear_eq,), optional Values of the nonlinear equality constraints. If not provided, the nonlinear equality constraints are evaluated at `x`. Returns ------- float Maximum constraint violation at `x`. )cub_valceq_valr rqrsrr)rrrrrrO>szNonlinearConstraints.maxcvcstfdd|jDS)NcrurrvrwryrrrYWrzz2NonlinearConstraints.violation..)r#rcrIrrryrrMVszNonlinearConstraints.violationNN) r4r6r7r8rr+r9rmrpr0rOrMrrrrr|usl    r|c@seZdZdZddZd0ddZeddZed d Zed d Z ed dZ eddZ eddZ eddZ eddZeddZeddZeddZeddZedd Zed!d"Zed#d$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.rS)8rrr:rRr|r%rint_obj_linear _nonlinearr TypeError _callbackrrHr@rreshaper rBr#r` _fixed_idx _fixed_valrP _orig_boundsr_boundsrQ_x0rdrbrr?rfrXrDrCisfinite_scaling_factor_scaling_shiftrGZdiagZzeros_feasibility_tol _filter_size _fun_filter _maxcv_filter _x_filter_store_history _history_size _fun_history_maxcv_history _x_history)robjrrJlinearZ nonlinearcallbackZfeasibility_tolscaleZ store_historyZ history_sizeZ filter_sizerrXZtolrdrrrr_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#rArU 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#rLr%build_xrrrOrrrrrr{rpoprArrCziprrrangerrr best_evalset parametersr StopIterationr r rtminmaximumminimum)rr)penaltyx_fullrrZ include_pointkZ remove_pointsigZx_bestZfun_best_rexcrrrr+ s                               zProblem.__call__cCrk)zt Number of variables. Returns ------- int Number of variables. )rrHr/rrrrXrnz Problem.ncCrk)z Number of variables in the original problem (with fixed variables). Returns ------- int Number of variables in the original problem (with fixed variables). )rrHr/rrrn_origrnzProblem.n_origcCr,)z Initial guess. Returns ------- `numpy.ndarray`, shape (n,) Initial guess. )rr/rrrrr1z Problem.x0cCrkr-)rr0r/rrrr0rnzProblem.n_evalcCrk)r2)rr(r/rrrrrnzProblem.fun_namecCr,)z} Bound constraints. Returns ------- BoundConstraints Bound constraints. )rr/rrrrJr1zProblem.boundscCr,)z Linear constraints. Returns ------- LinearConstraints Linear constraints. )rr/rrrrr1zProblem.linearcCrk)z Number of bound constraints. Returns ------- int Number of bound constraints. )rJrFr/rrrm_boundsrnzProblem.m_boundscCrkrl)rrmr/rrr m_linear_ubrnzProblem.m_linear_ubcCrkro)rrpr/rrr m_linear_eqrnzProblem.m_linear_eqcCrk)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. )rrmr/rrrm_nonlinear_ubzProblem.m_nonlinear_ubcCrk)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. )rrpr/rrrm_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$rr%r/rrr fun_history( zProblem.fun_historycCr)z History of maximum constraint violations. Returns ------- `numpy.ndarray`, shape (n_eval,) History of maximum constraint violations. r)r#r$rr%r/rrr 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-constrainedZ unconstrained)rrrrrrr/rrrtype@s   z Problem.typecCs |jdkS)z Whether the problem is a feasibility problem. Returns ------- bool Whether the problem is a feasibility problem. r3)rr/rrris_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[rrrrrrrQ)rr)rrrrrjs   zProblem.build_xNcCs,|j|||d}t|rtj|ddSdSr)rMr#rErt)rr)rrrMrrrrO~s z Problem.maxcvcCsg}|jjs|j|}||t|jjr"|j|}||t|jjr5|j|||}||t|r>t |SdSr) rJrDrMrr{rrIrr#rc)rr)rrrMblcZnlcrrrrMs        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)r{rrr#r$rrrrarrCrAZnanminrErZ flatnonzeroZ full_likenanrJrQ) rrrrZx_filterZ finite_idxZ feasible_idxZ fun_min_idxrZ merit_filterZ min_maxcv_idxZ merit_min_idxrrrrsZ           zProblem.best_eval)r r)r4r6r7r8rr+r9rXrrr0rrJrrrrrrrrrrrrOrMrrrrrrZsT ,                   r) contextlibrinspectrrnumpyr#Zscipy.optimizerrrrZscipy.optimize._constraintsrsettingsr r utilsr r rrr:rRr|rrrrrs   Z`(f