]j?SSKrSSKJr SSKJrJr SSKJrJ r S/r Sr Sr Sr \"\/\/\\"S 15/S .S S 9S S .Sj5rg)N)linear_sum_assignment) StrOptionsvalidate_params) check_arraycheck_consistent_lengthconsensus_scorecd[U6 [U6 Sn[X 5up4[X!5upVX4XV4$)z9Unpacks the row and column arrays and checks their shape.c[USS9$)NF) ensure_2d)r)xs _/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sklearn/metrics/cluster/_bicluster.py)_check_rows_and_columns..s {16)rmap)abchecksa_rowsa_colsb_rowsb_colss r _check_rows_and_columnsr s8QQ 6F^NF^NF 6 ))rcX-R5X-R5-nUR5UR5-nUR5UR5-nXEU-U- - $)z:Jaccard coefficient on the elements of the two biclusters.)sum)rrrr intersectiona_sizeb_sizes r _jaccardrs`O((*fo-B-B-DDL ZZ\FJJL (F ZZ\FJJL (F F?\9 ::rc,[X5up4pVURSnURSn[R"[ U5V V s/sH/n [ U5V s/sHo"X9XIXZXj5PM sn PM1 sn n 5n U $s sn fs sn n f)zwComputes pairwise similarity matrix. result[i, j] is the Jaccard coefficient of a's bicluster i and b's bicluster j. r)rshapenparrayrange) rr similarityrrrrn_an_bijresults r _pairwise_similarityr+!s&=Q%B"FF ,,q/C ,,q/C XX3Z NSSVZ XZZ 69fi CZ X F M Y s B B :B B jaccard)rrr%T)prefer_skip_nested_validation)r%cUS:Xa[n[XU5n[SU- 5upE[US5n[US5n[ X4U4R 5[ Xg5- 5$)aThe similarity of two sets of biclusters. Similarity between individual biclusters is computed. Then the best matching between sets is found by solving a linear sum assignment problem, using a modified Jonker-Volgenant algorithm. The final score is the sum of similarities divided by the size of the larger set. Read more in the :ref:`User Guide `. Parameters ---------- a : tuple (rows, columns) Tuple of row and column indicators for a set of biclusters. b : tuple (rows, columns) Another set of biclusters like ``a``. similarity : 'jaccard' or callable, default='jaccard' May be the string "jaccard" to use the Jaccard coefficient, or any function that takes four arguments, each of which is a 1d indicator vector: (a_rows, a_columns, b_rows, b_columns). Returns ------- consensus_score : float Consensus score, a non-negative value, sum of similarities divided by size of larger set. See Also -------- scipy.optimize.linear_sum_assignment : Solve the linear sum assignment problem. References ---------- * Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis for bicluster acquisition `__. Examples -------- >>> from sklearn.metrics import consensus_score >>> a = ([[True, False], [False, True]], [[False, True], [True, False]]) >>> b = ([[False, True], [True, False]], [[True, False], [False, True]]) >>> consensus_score(a, b, similarity='jaccard') 1.0 r,g?r)rr+rlenfloatrmax)rrr%matrix row_indices col_indicesr&r's r rr4snpY !! 3F4S6\BK ad)C ad)C [01557#c-G HHr)numpyr"scipy.optimizersklearn.utils._param_validationrrsklearn.utils.validationrr__all__rrr+tuplecallablerrr r=sm0GI  *;&WWYK!89 #' )26I6Ir