def comparing_pipeline(t_max=10000):
"""Generate various hypergraphs, run diffusion and compare models
"""
# TODO rewrite to use new hypergraph models with differenty created transition matrices
for n in range(20, 31, 5):
for k in range(3, 4):
for f in range(90, 91, 10):
f = float(f) / 100
# number_of_nodes, cardinality, fraction_of_hyperedges
print(n, k, f)
HG = utils.create_graph(n, k, f)
number_of_nodes = analytical.prediction(HG)
number_of_nodes_clique = analytical.prediction(HG,
model="clique")
# TODO change to matrix model nodes
markov_matrix_loops = create_markov_matrix(HG.hyper_edges(),
count_itself=True)
markov_matrix = create_markov_matrix(HG.hyper_edges(),
count_itself=False)
simulated_n_o_n = diffusion_on_hypergraph(HG, markov_matrix,
t_max)
simulated_n_o_n_i = diffusion_on_hypergraph(HG,
markov_matrix_loops,
t_max)
simulated_n_o_n_c = diffusion_on_clique(HG, t_max=t_max)
plt.figure(figsize=(12, 10))
width = 0.15
plt.bar(HG.nodes(), simulated_n_o_n,
width=width, color='crimson',
label='Simulated markov hypergraph')
plt.bar(np.array(HG.nodes()) + width, simulated_n_o_n_i, width,
color='burlywood',
label='Simulated markov hypergraph with loops')
plt.bar(np.array(HG.nodes()) + 2 * width, number_of_nodes,
width,
label='Analytical diffusion model on hypergraph',
color="#65df25")
plt.bar(np.array(HG.nodes()) + 3 * width,
simulated_n_o_n_c, width,
label='Simulated clique graph')
plt.bar(np.array(HG.nodes()) + 4 * width,
number_of_nodes_clique, width,
label='Analytical diffusion model on clique',
color="#dcab11")
plt.legend(loc=0)
plt.savefig("next_diffusion_%s_%s_%s.png" % (n, k, f))
t_per_walker=100,
title=None,
filename=None):
"""Simulate diffusion and show how entropy changes in time"""
nodes = HG.nodes()
edges = HG.hyper_edges()
states_per_time = compute_states_per_time(HG, t_max, t_per_walker)
state_indices = list(range(len(states_per_time))[4:])
ys = [
entropy_value(states_per_time[:i], nodes, edges) for i in state_indices
]
plt.plot(state_indices, ys)
if title:
plt.title(title)
if filename:
plt.savefig(filename)
if __name__ == '__main__':
k = 3
f = 1.6
for n in range(10, 30, 5):
HG = utils.create_graph(n, k, f)
filename = 'entropy_h_%s_%s_%s.png' % (n, k, f)
compare_entropy(HG, filename=filename)
def compare_entropy(HG, t_max=10000, t_per_walker=100, title=None,
filename=None):
"""Simulate diffusion and show how entropy changes in time"""
nodes = HG.nodes()
edges = HG.hyper_edges()
states_per_time = compute_states_per_time(HG, t_max, t_per_walker)
state_indices = list(range(len(states_per_time))[4:])
ys = [entropy_value(states_per_time[:i], nodes, edges)
for i in state_indices]
plt.plot(state_indices, ys)
if title:
plt.title(title)
if filename:
plt.savefig(filename)
if __name__ == '__main__':
k = 3
f = 1.6
for n in range(10, 30, 5):
HG = utils.create_graph(n, k, f)
filename = 'entropy_h_%s_%s_%s.png' % (n, k, f)
compare_entropy(HG, filename=filename)