o :ήc@s<ddlZddlZddlmZddlmZddZddZ dS) N)sparse)_ncut_cycCs:tj|dd}|jdd}tj|df|jd}||fS)aReturns the diagonal and weight matrices of a graph. Parameters ---------- graph : RAG A Region Adjacency Graph. Returns ------- D : csc_matrix 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_matrix 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_matrixsumr dia_matrixrtocsc)graphWentriesDrA/tmp/pip-target-vg8gfxp4/lib/python/skimage/future/graph/_ncut.py DW_matricess rcCsDt|}t||}|j|}|j|}||||S)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_matrix The diagonal matrix of the graph. W : csc_matrix 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. )nparrayrcut_costdatar )cutrrrassoc_aassoc_brrr ncut_cost s  r) networkxr numpyrscipyrrrrrrrrs