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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()vector::_M_default_append��������������;l ��������l�����`���(��������������`0���|����p���(���������zRx �$����FJ w�?;*3$"tD�����B�J�P �B(�A0�A8�D���X�B�B�M�]�P�B�B�P�� 8D0A(B BBBE 4�0���B�H�D � MBE Z CBA �����?A�}P�����K�B�E �B(�A0�D8�H@� 8A0A(B BBBF P������Td8���YF�E�B �E(�H0�H8�� 0A(B BBBH O0A(B BBBT�@���%T�E�E �E(�D0�A8�D@� 8A0A(B BBBA \������H���^L�B�B �B(�A0�A8�D@� 8D0A(B BBBK zPLRx�Q! �L$����[ �B�B�B �B(�A0�A8�G�� 8D0A(B BBBE ,zPLRx��  ��������4������T���,`�����T���������������������������������]2AO  �,�]�]���o`�� � `(   ���o����o���oH���o �]6FVfv��������&6FVfv���0�0�0�3���������a�0 �3GCC: (GNU) 10.2.1 20210130 (Red Hat 10.2.1-11)8`��H�     � �, 0�=>@A�]�]�]�]�_`�`�a�� [ * ��C��N `P �c �y�a��]� ��]���  ���`��a�ah��a@C�� <A�� (%��a��=� �,�`  0*^> �%���a��]� p%  �%?M �&Y� �,��]  G[l {����� � ">"Zo����� ���� &6Wj��rectangular_lsap.cpp_ZL5solvellPdbPlS0__ZL5solvellPdbPlS0_.coldcrtstuff.cderegister_tm_clones__do_global_dtors_auxcompleted.0__do_global_dtors_aux_fini_array_entryframe_dummy__frame_dummy_init_array_entry_lsap.ckwlist.0PyArray_APImoduledeflsap_methods__FRAME_END___ZSt16__introsort_loopIN9__gnu_cxx17__normal_iteratorIPlSt6vectorIlSaIlEEEElNS0_5__ops15_Iter_comp_iterIZ12argsort_iterIlES5_RKS3_IT_SaISA_EEEUlllE_EEEvSA_SA_T0_T1_DW.ref.__gxx_personality_v0__GNU_EH_FRAME_HDR_fini_GLOBAL_OFFSET_TABLE__Z12argsort_iterIlESt6vectorIlSaIlEERKS0_IT_SaIS3_EE_ZSt16__insertion_sortIN9__gnu_cxx17__normal_iteratorIPlSt6vectorIlSaIlEEEENS0_5__ops15_Iter_comp_iterIZ12argsort_iterIlES5_RKS3_IT_SaISA_EEEUlllE_EEEvSA_SA_T0___TMC_END____dso_handlesolve_rectangular_linear_sum_assignment_ZNSt13_Bvector_baseISaIbEE13_M_deallocateEv_ZSt13__adjust_heapIN9__gnu_cxx17__normal_iteratorIPlSt6vectorIlSaIlEEEEllNS0_5__ops15_Iter_comp_iterIZ12argsort_iterIlES5_RKS3_IT_SaISA_EEEUlllE_EEEvSA_T0_SH_T1_T2__ZdlPvm_DYNAMIC_initPyExc_ImportError_ZSt20__throw_length_errorPKc@@GLIBCXX_3.4memset@@GLIBC_2.2.5PyExc_ValueError__gmon_start___ZdlPv@@GLIBCXX_3.4PyCapsule_TypePyExc_TypeErrorPyExc_ModuleNotFoundErrorPyErr_Format_ITM_deregisterTMCloneTablePyErr_ExceptionMatches_ITM_registerTMCloneTablePyArg_ParseTupleAndKeywords__cxa_finalize@@GLIBC_2.2.5PyEval_RestoreThreadPyEval_SaveThreadPyExc_RuntimeErrormemmove@@GLIBC_2.2.5Py_BuildValuePyObject_GetAttrStringPyInit__lsapPyErr_Print_Py_DeallocPyImport_ImportModulePyErr_ClearPyModule_Create2PyErr_SetString__gxx_personality_v0@@CXXABI_1.3_Znwm@@GLIBCXX_3.4_Unwind_Resume@@GCC_3.0memcpy@@GLIBC_2.14PyCapsule_GetPointer.symtab.strtab.shstrtab.note.gnu.build-id.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.gcc_except_table.init_array.fini_array.data.rel.ro.dynamic.got.got.plt.data.bss.comment88$.���o``$8 ��0@���H���oHHDU���o���d  nB (xs  �~�����,�, �00� ��=�=l�>>@�@A@Aa��]�M��]�M��]�M��]�M���_�OP�`P���`�P� ��a�Q0�Q/�Q�= �Z�ra 
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