9i!8SSKrSSKrSSKJr SSKJr SrSr g)N)sparse)_ncut_cyc[R"USS9nURSS9n[R"US4UR S9R 5nX14$)aReturns the diagonal and weight matrices of a graph. Parameters ---------- graph : RAG A Region Adjacency Graph. Returns ------- D : csc_array The diagonal matrix of the graph. ``D[i, i]`` is the sum of weights of all edges incident on `i`. All other entries are `0`. W : csc_array The weight matrix of the graph. ``W[i, j]`` is the weight of the edge joining `i` to `j`. csc)formatr)axis)shape)nxto_scipy_sparse_arraysumr dia_arrayr tocsc)graphWentriesDs S/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/graph/_ncut.py DW_matricesrsO$   u5AeeemG'1QWW5;;=A 4Kc:[R"U5n[R"XRUR UR URSS9nURUR5nURU)R5nX4- X5- -$)a0Returns the N-cut cost of a bi-partition of a graph. Parameters ---------- cut : ndarray The mask for the nodes in the graph. Nodes corresponding to a `True` value are in one set. D : csc_array The diagonal matrix of the graph. W : csc_array The weight matrix of the graph. Returns ------- cost : float The cost of performing the N-cut. References ---------- .. [1] Normalized Cuts and Image Segmentation, Jianbo Shi and Jitendra Malik, IEEE Transactions on Pattern Analysis and Machine Intelligence, Page 889, Equation 2. r)num_cols) nparrayrcut_costdataindicesindptrr r )cutrrrassoc_aassoc_bs r ncut_costr" s{0 ((3-C  ffaiiAGGTUJWHffSkooGffcTl G  8#5 66r) networkxr numpyrscipyrrrr"rrr(s2 7r