ó î&]\c@`sØddlmZmZmZddlZddljZddl m Z m Z ddl m Z ddlmZddd fZd efd „ƒYZd efd „ƒYZdefd„ƒYZdefd„ƒYZdS(i(tdivisiontprint_functiontabsolute_importNi(tapprox_derivativet group_columns(tHessianUpdateStrategy(tLinearOperators2-points3-pointtcstScalarFunctioncB`sMeZdZd„Zd„Zd„Zd„Zd„Zd„Zd„Z RS(s‘Scalar function and its derivatives. This class defines a scalar function F: R^n->R and methods for computing or approximating its first and second derivatives. Notes ----- This class implements a memoization logic. There are methods `fun`, `grad`, hess` and corresponding attributes `f`, `g` and `H`. The following things should be considered: 1. Use only public methods `fun`, `grad` and `hess`. 2. After one of the methods is called, the corresponding attribute will be set. However, a subsequent call with a different argument of *any* of the methods may overwrite the attribute. c `sºtˆƒ r1ˆtkr1tdjtƒƒ‚ntˆƒpUˆtkpUtˆtƒsptdjtƒƒ‚nˆtkr—ˆtkr—tdƒ‚ntj|ƒjt ƒˆ_ ˆj j ˆ_ dˆ_ dˆ_dˆ_tˆ_tˆ_tˆ_i‰ˆtkr*ˆˆd<|ˆd<|ˆdR^m and methods for computing or approximating its first and second derivatives. Notes ----- This class implements a memoization logic. There are methods `fun`, `jac`, hess` and corresponding attributes `f`, `J` and `H`. The following things should be considered: 1. Use only public methods `fun`, `jac` and `hess`. 2. After one of the methods is called, the corresponding attribute will be set. However, a subsequent call with a different argument of *any* of the methods may overwrite the attribute. c `stˆƒ r1ˆtkr1tdjtƒƒ‚ntˆƒpUˆtkpUtˆtƒsptdjtƒƒ‚nˆtkr—ˆtkr—tdƒ‚ntj|ƒjt ƒˆ_ ˆj j ˆ_ dˆ_ dˆ_dˆ_tˆ_tˆ_tˆ_i‰ˆtkrjˆˆd<|ˆd<|dk rHt|ƒ} || fˆdR!tsparse_jacobianRPR$Rt_update_jac_implR&RR%R?R*RXRARB(RRRCRNR"RDtfinite_diff_jac_sparsityRER`tsparsity_groupsRRRR'R1(( RRRR"R#RNRVRORsGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRFÕsÎ                                         cC`s.tj||jƒs*||_t|_ndS(N(RRGRSR,R/(RRS((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyt _update_vŸs cC`s)tj||jƒs%|j|ƒndS(N(RRGRRB(RR((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyt _update_x¤scC`s#|js|jƒt|_ndS(N(R-R<R;(R((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyR¨s  cC`s#|js|jƒt|_ndS(N(R[RaR;(R((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRW­s  cC`s#|js|jƒt|_ndS(N(R/RAR;(R((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyR0²s  cC`s|j|ƒ|jƒ|jS(N(ReRR(RR((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyR·s  cC`s|j|ƒ|jƒ|jS(N(ReRWRQ(RR((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRN¼s  cC`s+|j|ƒ|j|ƒ|jƒ|jS(N(RdReR0R&(RRRS((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyR"Ás   ( RHRIRJRFRdReRRWR0RRNR"(((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRKÄs Ê       tLinearVectorFunctioncB`s;eZdZd„Zd„Zd„Zd„Zd„ZRS(sLinear vector function and its derivatives. Defines a linear function F = A x, where x is n-dimensional vector and A is m-by-n matrix. The Jacobian is constant and equals to A. The Hessian is identically zero and it is returned as a csr matrix. cC`s|s!|dkr?tj|ƒr?tj|ƒ|_t|_nEtj|ƒri|jƒ|_t|_nt j |ƒ|_t|_|jj \|_ |_ t j|ƒjtƒ|_|jj|jƒ|_t|_t j|j dtƒ|_tj|j |j fƒ|_dS(Ntdtype(R@R R>R!RQR;R`RPR,RR$tshapeR_R:RR7R8RRURR-tzerosRSR&(RtARCR`((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRFÐs!    cC`s.tj||jƒs*||_t|_ndS(N(RRGRR,R-(RR((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyReäs cC`s>|j|ƒ|js7|jj|ƒ|_t|_n|jS(N(ReR-RQRURR;(RR((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRés    cC`s|j|ƒ|jS(N(ReRQ(RR((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRNðs cC`s|j|ƒ||_|jS(N(ReRSR&(RRRS((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyR"ôs  (RHRIRJRFReRRNR"(((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRfÉs     tIdentityVectorFunctioncB`seZdZd„ZRS(sþIdentity vector function and its derivatives. The Jacobian is the identity matrix, returned as a dense array when `sparse_jacobian=False` and as a csr matrix otherwise. The Hessian is identically zero and it is returned as a csr matrix. cC`sqt|ƒ}|s|dkr<tj|ddƒ}t}ntj|ƒ}t}tt|ƒj |||ƒdS(NR5tcsr( tlenR@R teyeR;RR,tsuperRkRF(RRCR`R:Rj((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRFs  (RHRIRJRF(((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyRkús(t __future__RRRtnumpyRt scipy.sparsetsparseR t_numdiffRRt_hessian_update_strategyRtscipy.sparse.linalgRR3tobjectRRKRfRk(((sGlib/python2.7/site-packages/scipy/optimize/_differentiable_functions.pyts ¸ÿ1