diff env/lib/python3.7/site-packages/networkx/linalg/bethehessianmatrix.py @ 5:9b1c78e6ba9c draft default tip

"planemo upload commit 6c0a8142489327ece472c84e558c47da711a9142"

--- a/env/lib/python3.7/site-packages/networkx/linalg/bethehessianmatrix.py	Thu May 14 16:47:39 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,92 +0,0 @@
-# -*- coding: utf-8 -*-
-#    Copyright (C) 2004-2019 by
-#    Aric Hagberg <[e-mail]>
-#    Dan Schult <[e-mail]>
-#    Pieter Swart <[e-mail]>
-#    Jean-Gabriel Young <[e-mail]>
-#    All rights reserved.
-#    BSD license.
-#
-# Authors: Jean-Gabriel Young ([e-mail])
-"""Bethe Hessian or deformed Laplacian matrix of graphs."""
-import networkx as nx
-from networkx.utils import not_implemented_for
-
-__all__ = ['bethe_hessian_matrix']
-
-
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def bethe_hessian_matrix(G, r=None, nodelist=None):
-    r"""Returns the Bethe Hessian matrix of G.
-
-    The Bethe Hessian is a family of matrices parametrized by r, defined as
-    H(r) = (r^2 - 1) I - r A + D where A is the adjacency matrix, D is the
-    diagonal matrix of node degrees, and I is the identify matrix. It is equal
-    to the graph laplacian when the regularizer r = 1.
-
-    The default choice of regularizer should be the ratio [2]
-
-    .. math::
-      r_m = \left(\sum k_i \right)^{-1}\left(\sum k_i^2 \right) - 1
-
-    Parameters
-    ----------
-    G : Graph
-       A NetworkX graph
-
-    r : float
-       Regularizer parameter
-
-    nodelist : list, optional
-       The rows and columns are ordered according to the nodes in nodelist.
-       If nodelist is None, then the ordering is produced by G.nodes().
-
-
-    Returns
-    -------
-    H : Numpy matrix
-      The Bethe Hessian matrix of G, with paramter r.
-
-    Examples
-    --------
-    >>> import networkx as nx
-    >>> k =[3, 2, 2, 1, 0]
-    >>> G = nx.havel_hakimi_graph(k)
-    >>> H = nx.modularity_matrix(G)
-
-
-    See Also
-    --------
-    bethe_hessian_spectrum
-    to_numpy_matrix
-    adjacency_matrix
-    laplacian_matrix
-
-    References
-    ----------
-    .. [1] A. Saade, F. Krzakala and L. Zdeborová
-       "Spectral clustering of graphs with the bethe hessian",
-       Advances in Neural Information Processing Systems. 2014.
-    .. [2] C. M. Lee, E. Levina
-       "Estimating the number of communities in networks by spectral methods"
-       arXiv:1507.00827, 2015.
-    """
-    import scipy.sparse
-    if nodelist is None:
-        nodelist = list(G)
-    if r is None:
-        r = sum([d ** 2 for v, d in nx.degree(G)]) /\
-            sum([d for v, d in nx.degree(G)]) - 1
-    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, format='csr')
-    n, m = A.shape
-    diags = A.sum(axis=1)
-    D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format='csr')
-    I = scipy.sparse.eye(m, n, format='csr')
-    return (r ** 2 - 1) * I - r * A + D
-
-
-# fixture for pytest
-def setup_module(module):
-    import pytest
-    numpy = pytest.importorskip('numpy')