+ Ai!:^RIt^RIt^RIHt^RIHtRtRt R#)N)sparse)_ncut_cyc\P!VRR7pVP^R7p\P!V^3VP R7P 4pW13#)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)format)axis)shape)nxto_scipy_sparse_arraysumr dia_arrayr tocsc)graphWentriesDs& Q/var/www/html/photoedit/myenv/lib/python3.14/site-packages/skimage/graph/_ncut.py DW_matricesrsO$   u5AeeemG'1QWW5;;=A 4Kcp\P!V4p\P!WPVP VP VP^,R7pVPV,P4pVPV(,P4pW4, W5, ,#)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. )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