1-Ph!:ddlZddlZddlmZddlmZdZdZ dS)N)sparse)_ncut_cyctj|d}|d}tj|df|j}||fS)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/test/jupyter/venv/lib/python3.11/site-packages/skimage/graph/_ncut.py DW_matricesrsZ$  u555AeeemmG'1QW555;;==A a4Kc$tj|}tj||j|j|j|jd}|j|}|j|}||z ||z zS)a|Returns 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 afaiAGTUJWWWHfSkooGfcTl  G w 8g#5 66r) networkxr numpyrscipyrrrr"rrr(si2 7 7 7 7 7r