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 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()
