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LH<J<@LHwN,IHLHH@LcHL艍@LHH)H~1HPHH9LH@LM~uHH~iLHJ4E1MLHL)fff.ff.HHI9uIHMM9uHLH u8LgHJLHLH HfW&H@I9uHxH+0HHH(#HH@HIHxH(H@M Ht.H@1L L(HPL(L M9LL)HP1HHJuIHtIHPHIH9uM9TMHPM)LHHHLLHcH HL(L0L,L0HL(H ILHLHH L( H LHPL(L9H0tMLHQHNIH9~*H(HAHPHHPHH I9 |H(HLI9uH`H0M)HLHx1IH H IHI9uH@LHHHpHHHHHL1H0%fDHLLHH L(L0L0L(H HLLL L(HPL L(H0IqHH9AH{HHHHH 9H H")Hff.UH1H OGHAWAVLMLEAUATSHHdH4%(HuHHEHE\H%G H(hH}E11A1HHHPuH@HEH HC HUL(LpLM9INHE1E1HEHFHxjjjIH HHE1E1HfFHUHxjjjIH H%MD$LHLELM1LMLEHEELLHUH}AAABLLH=p1&x A$x A$AxAtiHUdH+%(He[A\A]A^A_]HICH5H81xt 1H1@LHElHEfDxI@ff.A$xA$uL'1?yHBH5 H8VXHyBH5*H8bx 1fHABH5H8*xԃu1ff.HHEtA$HEgoLHELHESH6A$xA$tM1%DLHA$y1@ff.UH=*HATS HHH5GHNIċx MH*AI9D$1LA$H"Cx A$tuH=HB:HBI[H=&BA\]fDHQLHBw@Hy@H8u%H@H5rH82[1A\]H=HHfH!@H5H8A$xA$uLAH?H51H:UH?H5tH8:H?H51H:H?H5H8MHi?H5FH82HHvector::_M_default_appendO|pinvalid cost matrix objectcost matrix is infeasibleOOnumpy._core._multiarray_umathnumpy.core._multiarray_umath_ARRAY_API_ARRAY_API is NULL pointerlinear_sum_assignment_lsapcost_matrixmaximizecannot create std::vector larger than max_size()expected 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 importSolve 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 Solves the rectangular linear sum assignment.;t `p\p08 \zRx $XpFJ w?9*3$"D\`4t bAC DJJG A @ H 8X)SC BRED A y lf `DIC Au A84JC BEGD F V zPLRx" 0$UAC P G X ,EO U H ,\CEJ C K \ D BGNU`~FDO{"type":"deb","os":"ubuntu","name":"scipy","version":"1.16.3-4build1","architecture":"amd64"}DSa  d0mmox0  o(   ox oo0 o m0@P`p 0@P`pp@0*C@Lp@@/usr/lib/debug/.dwz/x86_64-linux-gnu/python3-scipy.debug/\G,ճSW6_d8d0d61e921262afc7a47a855e4ccf3b35065e.debug ݶ.shstrtab.note.gnu.build-id.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.plt.got.plt.sec.text.fini.rodata.eh_frame_hdr.eh_frame.gcc_except_table.note.gnu.property.note.package.init_array.fini_array.dynamic.got.plt.data.bss.gnu_debugaltlink.gnu_debuglink $o$( 00H0xx8o0 0 FEox x T  ^B( ( hc  pnw`c@d0d0 @@0 0M0MtMMHOO`PPPP pPpPpm]m]m]ro_Po_ p` qaaM&b44b5