update

.CondaPkg/env/Lib/site-packages/skimage/graph/_ncut.py

@@ -0,0 +1,64 @@
+import networkx as nx
+import numpy as np
+from scipy import sparse
+from . import _ncut_cy
+
+
+def DW_matrices(graph):
+    """Returns 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`.
+    """
+    # sparse.eighsh is most efficient with CSC-formatted input
+    W = nx.to_scipy_sparse_array(graph, format='csc')
+    entries = W.sum(axis=0)
+    D = sparse.dia_matrix((entries, 0), shape=W.shape).tocsc()
+
+    return D, W
+
+
+def ncut_cost(cut, D, W):
+    """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.
+    """
+    cut = np.array(cut)
+    cut_cost = _ncut_cy.cut_cost(cut, W.data, W.indices, W.indptr, num_cols=W.shape[0])
+
+    # D has elements only along the diagonal, one per node, so we can directly
+    # index the data attribute with cut.
+    assoc_a = D.data[cut].sum()
+    assoc_b = D.data[~cut].sum()
+
+    return (cut_cost / assoc_a) + (cut_cost / assoc_b)