ELF>@q@8 @P P   @@@99]mm]mm888$$PtdPMPMPMddQtdRtd]mm``GNU: ,ѭQ$}!! *_ y, UF"qN9 @;__gmon_start___ITM_deregisterTMCloneTable_ITM_registerTMCloneTable__cxa_finalizePyArg_ParseTupleAndKeywordsPyEval_SaveThreadPyEval_RestoreThreadPy_BuildValue_Py_DeallocPyExc_ValueErrorPyErr_FormatPyExc_TypeErrorPyErr_SetStringPyInit__lsapPyImport_ImportModulePyObject_GetAttrStringPyCapsule_TypePyCapsule_GetPointerPyModule_Create2PyExc_ModuleNotFoundErrorPyErr_ExceptionMatchesPyErr_PrintPyExc_ImportErrorPyErr_ClearPyExc_RuntimeError_ZdlPvmmemmove_Znwmmemset_ZSt20__throw_length_errorPKcmemcpy_Unwind_Resume__gxx_personality_v0libstdc++.so.6libgcc_s.so.1libc.so.6GCC_3.0GLIBC_2.14GLIBC_2.2.5CXXABI_1.3CXXABI_1.3.9GLIBCXX_3.4B P&y ZP0bui m3ӯkyyѯ t)m`m mmp@p@q@qC q`q`q@hqpxqCoooooo o o ooqpppp p (p0p8p@pHpPpXp`phpppxppppppp HH_HtH5_%_@%_h%_h%_h%_h%_h%_h%_h%_hp%_h`%_h P%z_h @%r_h 0%j_h %b_h %Z_h%R_h%J_h%B_h%:_h%2_h%*_h%"_hH=/dH=;XHt$`HLhH$PKH$ >Ht$`LAH$H$H)HucHt$`LHt$`HHt$`LH$H|$H$HD$HH)HtHHHH=@:Hf.@H=^H^H9tH\Ht H=^H5^H)HH?HHHtH\HtfD=}^u+UH=\Ht H=nZIdU^]wAWH1HAVH =]Hv,AUATUSHHLL$,LD$0HD$0D$,H] H(hH|$0E11HA1HHPLpMH@ Hl$8HL LhLM9INHE1E1HD$@Hb]HxjjjIH HH;]HE1E1HHxjjjHH H4MGLHLD$LL$1LL$LHD$D$,LLLD$EH|$AAADHLH= +1HxHHxff.Ix HIHUx^HHUuTHHD$HD$HH[]A\A]A^A_fH ZH5J+H81Hx HHt1HH[]A\A]A^A_H(fDHx HHIxHIuLfHyHYH5)H8 Hnrf.HIYH5*H8Hx HHt 1HxIxHIH1HHD$GIHD$~fHXH5A)H8ZHy1L@HIy@LHD$HD$-fL_UH=(SHHHH5)HsHHExHHEHHXH9C1HHHYx HHtwHHY1HY@HH=X[] HQHHiYu@HiWH8u-H)WH5"+H8H1[]fH='oHH봐H)WH5(H8zHxHHuH"H5(HVH81_]HVH5,*H8$BHVH5(1H: HVH5)H8HyVH5'H8DAWAVAUATUSHH|$LD$xL$HIHII׉LH9  HD$HIIHDŽ$HDŽ$M~h ?31DAf.wf.{>AHD$HHtH$HE1HĈD[]A\A]A^A_@HL9uLH<VJLT$ HLD$HL$H$1HHHD$LD$LH<L$JL$HH\$`L\$kHH1IKHSL$HHH@L\$LT$ HD$XLHH|$(t0H1HLD$HL$LT$ LD$HL$HD$XLt$`L$LD$0LL$HLIH$fHDŽ$)$HHHھH$HH$gLH$LdHHH\$`IH;<|HSHCH;<}AHPHHPHLL9|L/HI9uH[]A\A]A^A_DuH뎐HL/f.f.f.f.f.DHBAWIAVAUIATIAUI?HSIILl$L9:MIfNff.fHPHHPHL L9|HLI9uHH[]A\A]A^A_f.HGڍHHcHJHJI9HHBLHL)HH LHLLbLLLsHHLI9TL1H=*HHO|pinvalid cost matrix objectcost matrix is infeasibleOOnumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointercost_matrixmaximize_lsaplinear_sum_assignmentvector::_M_default_appendexpected a matrix (2-D array), got a %d arraymatrix contains invalid numeric entries_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.23) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimenumpy._core.multiarray failed to importSolves the rectangular linear sum assignment.Solve the linear sum assignment problem. Parameters ---------- cost_matrix : array The cost matrix of the bipartite graph. maximize : bool (default: False) Calculates a maximum weight matching if true. Returns ------- row_ind, col_ind : array An array of row indices and one of corresponding column indices giving the optimal assignment. The cost of the assignment can be computed as ``cost_matrix[row_ind, col_ind].sum()``. The row indices will be sorted; in the case of a square cost matrix they will be equal to ``numpy.arange(cost_matrix.shape[0])``. See Also -------- scipy.sparse.csgraph.min_weight_full_bipartite_matching : for sparse inputs Notes ----- The linear sum assignment problem [1]_ is also known as minimum weight matching in bipartite graphs. A problem instance is described by a matrix C, where each C[i,j] is the cost of matching vertex i of the first partite set (a 'worker') and vertex j of the second set (a 'job'). The goal is to find a complete assignment of workers to jobs of minimal cost. Formally, let X be a boolean matrix where :math:`X[i,j] = 1` iff row i is assigned to column j. Then the optimal assignment has cost .. math:: \min \sum_i \sum_j C_{i,j} X_{i,j} where, in the case where the matrix X is square, each row is assigned to exactly one column, and each column to exactly one row. This function can also solve a generalization of the classic assignment problem where the cost matrix is rectangular. If it has more rows than columns, then not every row needs to be assigned to a column, and vice versa. This implementation is a modified Jonker-Volgenant algorithm with no initialization, described in ref. [2]_. .. versionadded:: 0.17.0 References ---------- .. [1] https://en.wikipedia.org/wiki/Assignment_problem .. [2] DF Crouse. On implementing 2D rectangular assignment algorithms. *IEEE Transactions on Aerospace and Electronic Systems*, 52(4):1679-1696, August 2016, :doi:`10.1109/TAES.2016.140952` Examples -------- >>> import numpy as np >>> cost = np.array([[4, 1, 3], [2, 0, 5], [3, 2, 2]]) >>> from scipy.optimize import linear_sum_assignment >>> row_ind, col_ind = linear_sum_assignment(cost) >>> col_ind array([1, 0, 2]) >>> cost[row_ind, col_ind].sum() 5 cannot create std::vector larger than max_size();d pP 80tp00<zRx $HpFJ w?;*3$"DpBJP B(A0A8DXBBM]PBBM 8A0A(B BBBJ n 8A0A(B BBBH 4;AHD  MAH Z CAJ  x?A}P(KBE B(D0A8H@ 8A0A(B BBBF PX|HFEB E(H0H8 0A(B BBBH  0A(B BBBH T TEI I(D0E8G@ 8A0A(B BBBA @H0TLBB B(A0A8DP 8D0A(B BBBK zPLRx]! 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