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_lazy_srw(G): # pragma: no cover
import networkx as nx
import matplotlib.pyplot as plt
mc = mkm.nx_graph_lazy_srw(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_lazy_srw(G): # pragma: no cover
import networkx as nx
import matplotlib.pyplot as plt
mc = mkm.nx_graph_lazy_srw(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()
import markovmixing as mkm
import networkx as nx
import numpy as np
####################### 50-CYCLE EXAMPLE #########################
# create a graph
G = nx.cycle_graph(50)
# create a MarkovChain that is lazy simple random walk on the graph
mc = mkm.nx_graph_lazy_srw(G)
# plot the total variation mixing
mc.add_random_delta_distributions(1)
mc.plot_tv_mixing(y_tol=0.01, threshold=0.01)
####################### BIASED LINE EXAMPLE #########################
# create the transition matrix
P = mkm.line_lazy_transition_matrix(1000, p=0.51)
# create the MarkovChain with the given transition matrix
mc = mkm.MarkovChain(P)
# add some initial distributions
for i in [0,500,999]:
mc.add_distributions(mkm.delta_distribution(1000,x=i))
# plot the total variation mixing
mc.plot_tv_mixing(y_tol=0.01, threshold=1e-5)
