示例#1
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def nx_graph_nbrw(G):
    import networkx as nx

    A = nx.to_scipy_sparse_matrix(G)
    P = mkm.graph_nbrw_transition_matrix(A)
    mc = mkm.MarkovChain(P)
    mc.set_stationary_distribution(mkm.uniform_distribution(mc.get_n()))

    return mc
示例#2
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def nx_graph_nbrw(G):
	import networkx as nx

	A = nx.to_scipy_sparse_matrix(G)
	P = mkm.graph_nbrw_transition_matrix(A)
	mc = mkm.MarkovChain(P)
	mc.set_stationary_distribution(mkm.uniform_distribution(mc.get_n()))

	return mc
示例#3
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def nx_graph_nbrw(G):
	"""Returns the Markov chain for the NBRW on the graph G. 
	"""
	import networkx as nx

	A = nx.to_scipy_sparse_matrix(G)
	P = mkm.graph_nbrw_transition_matrix(A)
	mc = mkm.MarkovChain(P)
	mc.set_stationary(mkm.uniform_distribution(mc.get_n()))

	return mc
示例#4
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def nx_graph_nbrw(G):
    """Returns the Markov chain for the NBRW on the graph G. 
	"""
    import networkx as nx

    A = nx.to_scipy_sparse_matrix(G)
    P = mkm.graph_nbrw_transition_matrix(A)
    mc = mkm.MarkovChain(P)
    mc.set_stationary(mkm.uniform_distribution(mc.get_n()))

    return mc
示例#5
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def tets_transition_matrix():
	assert(mkm.is_transition_matrix(numpy.eye(1)))
	assert(mkm.is_transition_matrix(numpy.eye(5)))
	assert(mkm.is_transition_matrix(numpy.ones((1,2,3))) == False)

	assert(mkm.is_transition_matrix(mkm.line_lazy_transition_matrix(100, p = 0.51)))

	A = numpy.ones((3,3))
	numpy.fill_diagonal(A,0)
	P = mkm.graph_nbrw_transition_matrix(ssp.dok_matrix(A))
	assert(mkm.is_transition_matrix(P))
	print P
示例#6
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def tets_transition_matrix():
    assert (mkm.is_transition_matrix(numpy.eye(1)))
    assert (mkm.is_transition_matrix(numpy.eye(5)))
    assert (mkm.is_transition_matrix(numpy.ones((1, 2, 3))) == False)

    assert (mkm.is_transition_matrix(
        mkm.line_lazy_transition_matrix(100, p=0.51)))

    A = numpy.ones((3, 3))
    numpy.fill_diagonal(A, 0)
    P = mkm.graph_nbrw_transition_matrix(ssp.dok_matrix(A))
    assert (mkm.is_transition_matrix(P))
    print P
示例#7
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def tets_transition_matrix():
    assert (mkm.is_transition_matrix(numpy.eye(1)))
    assert (mkm.is_transition_matrix(numpy.eye(5)))
    assert (mkm.is_transition_matrix(numpy.ones((1, 2, 3))) == False)

    assert (mkm.is_transition_matrix(
        mkm.line_lazy_transition_matrix(100, p=0.51)))

    # graph_nbrw_transition_matrix
    A = numpy.ones((3, 3))
    numpy.fill_diagonal(A, 0)
    P = mkm.graph_nbrw_transition_matrix(A)
    assert (mkm.is_transition_matrix(P))
    print A
    print P

    # tree_nbrw_transition_matrix
    A = numpy.array([[0, 1, 0, 0, 0], [1, 0, 1, 1, 0], [0, 1, 0, 0, 0],
                     [0, 1, 0, 0, 1], [0, 0, 0, 1, 0]])
    P = mkm.tree_nbrw_transition_matrix(A, 0)
    assert (mkm.is_transition_matrix(P))
    print A
    print P
示例#8
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# plot the total variation mixing
mc.plot_tv_mixing(y_tol=0.01, threshold=1e-5)


####################### NBRW EXAMPLE #########################
import matplotlib.pyplot as plt

# load a 6-regular graph with 50.000 nodes from file
G_6_regular = nx.read_sparse6('6_regular.s6')

# get the adjacency matrix
A = nx.to_scipy_sparse_matrix(G_6_regular)

# transition matrix for NBRW on the graph from the adjacency matrix 
P = mkm.graph_nbrw_transition_matrix(A)

# Markov chain with the transition marix
mc = mkm.MarkovChain(P)

# the stationary distribution is uniform
mc.set_stationary(mkm.uniform_distribution(mc.get_n()))

# add a random starting position to the Markov chain
mc.add_random_delta_distributions(1)

# determine the mixing
mc.compute_tv_mixing()

# plot the mixing
(x,tv) = mc.distribution_tv_mixing(0)