def entropy_value(states, nodes, edges):
"""Compute entropy values from states, nodes and edges.
It recomputes edge occurrences to node occurences.
It's too much coupled with bipartite model.
#TODO - decouple it
"""
cum_states = chain(*states)
most_common = Counter(cum_states).most_common()
most_common_nodes = count_nodes(nodes, edges, most_common)
frequencies = (utils.get_names_and_occurrences(most_common_nodes)[1])
return entropy(frequencies)
def entropy_value(states, nodes, edges):
"""Compute entropy values from states, nodes and edges.
It recomputes edge occurrences to node occurences.
It's too much coupled with bipartite model.
#TODO - decouple it
"""
cum_states = chain(*states)
most_common = Counter(cum_states).most_common()
most_common_nodes = count_nodes(nodes, edges, most_common)
frequencies = (utils.get_names_and_occurrences(most_common_nodes)[1])
return entropy(frequencies)
def diffusion_on_clique(hypergraph, t_max, plot_results=False):
"""Simulate diffusion on corresponding clique graph"""
nodes = hypergraph.nodes()
hyper_edges = hypergraph.hyper_edges()
clique_graph = converters.convert_to_clique_graph(nodes, hyper_edges)
markov_matrix = create_markov_matrix(clique_graph.edges(),
count_itself=False)
edges = clique_graph.edges()
most_common, most_common_nodes, states = simulate_diffusion(
nodes, edges, markov_matrix, t_max)
if plot_results:
return plot_diffusion_results(most_common, most_common_nodes,
hyper_edges, "clique")
else:
return utils.get_names_and_occurrences(most_common_nodes)[1]
def diffusion_on_clique(hypergraph, t_max, plot_results=False):
"""Simulate diffusion on corresponding clique graph"""
nodes = hypergraph.nodes()
hyper_edges = hypergraph.hyper_edges()
clique_graph = converters.convert_to_clique_graph(nodes, hyper_edges)
markov_matrix = create_markov_matrix(clique_graph.edges(),
count_itself=False)
edges = clique_graph.edges()
most_common, most_common_nodes, states = simulate_diffusion(
nodes, edges, markov_matrix, t_max)
if plot_results:
return plot_diffusion_results(most_common, most_common_nodes,
hyper_edges, "clique")
else:
return utils.get_names_and_occurrences(most_common_nodes)[1]
def diffusion_on_hypergraph(hypergraph, markov_matrix,
t_max, plot_results=False):
"""Simulate numerically diffusion on a hypergraph,
Diffusion is simulated using Markov Chains.
"""
nodes = hypergraph.nodes()
hyper_edges = hypergraph.hyper_edges()
most_common, most_common_nodes, states = simulate_diffusion(
nodes, hyper_edges, markov_matrix, t_max)
if plot_results:
return plot_diffusion_results(most_common, most_common_nodes,
hyper_edges, "hypergraph")
else:
return utils.get_names_and_occurrences(most_common_nodes)[1]
