def test_networkx_graphs(): G = nx.path_graph(10) mc_srw = mkm.nx_graph_srw(G) mc_lswr = mkm.nx_graph_lazy_srw(G) G = nx.hypercube_graph(10) print nx.number_of_nodes(G) mkm.nx_graph_lazy_srw(G) G = nx.complete_graph(50) print mkm.nx_graph_nbrw(G).get_n()
def nx_graph_analyze_nbrw(G): # pragma: no cover import networkx as nx import matplotlib.pyplot as plt mc = mkm.nx_graph_nbrw(G) mc.add_distributions(mkm.random_delta_distributions(mc.get_n(), 5)) mc.compute_tv_mixing() plt.figure() for i in range(mc.num_distributions()): (x, tv) = mc.distribution_tv_mixing(i) plt.plot(x, tv) plt.xlabel("t") plt.ylabel("Distance to stationary distribution in total variation") plt.show()
def nx_graph_analyze_nbrw(G): # pragma: no cover import networkx as nx import matplotlib.pyplot as plt mc = mkm.nx_graph_nbrw(G) mc.add_distributions(mkm.random_delta_distributions(mc.get_n(),5)) mc.compute_tv_mixing() plt.figure() for i in range(mc.num_distributions()): (x,tv) = mc.distribution_tv_mixing(i) plt.plot(x, tv) plt.xlabel("t") plt.ylabel("Distance to stationary distribution in total variation") plt.show()