def test_causal_flow_pop(bowtie_database, bowtie_env_categories):
# This what it was evolved to do (see the generate.py file)
pop = bowtie_database.population
f_flow = CausalFlowAnalyzer(bowtie_database.world)
j_flow = f_flow.analyse_collection(pop)
flow = Information(j_flow)
c_flow = numpy.asarray(flow)
f_mut = MutualInfoAnalyzer(bowtie_database.world, bowtie_env_categories)
j_mut = f_mut.analyse_collection(pop)
mut = Information(j_mut)
c_mut = numpy.asarray(mut)
# Let's just try the first few
for i, n in enumerate(pop):
p_joint = get_causal_flow(n)
p_flow = calc_info_from_probs(p_joint)
numpy.testing.assert_allclose(c_flow[i], p_flow)
_, p_mut = calc_mutual_info(n, bowtie_env_categories)
numpy.testing.assert_allclose(c_mut[i, :, 0], p_mut)
# It takes too long....
if i == 50:
break
def test_mutual_info_cython(bowtie_network, bowtie_env_categories):
f = MutualInfoAnalyzer(bowtie_network.factory.world,
bowtie_env_categories)
j = f.analyse_network(bowtie_network)
p_joint, p_info = calc_mutual_info(bowtie_network,
bowtie_env_categories)
info = Information(j)
c_info = numpy.asarray(info)[0]
# Need to account for different shape
numpy.testing.assert_allclose(c_info[:, 0], p_info)
