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_causal_flow_net(bowtie_network):
"""Compare the clunky python version with our C++ version"""
f = CausalFlowAnalyzer(bowtie_network.factory.world)
j = f.analyse_network(bowtie_network)
# We just access the 0th element, as we only sent one network for analysis
c_joint = numpy.asarray(j)[0]
# Test that each of the signals has a complete joint probability. It
# should sum to 1.0
for per_reg in c_joint:
for per_output in per_reg:
assert per_output.sum() == 1.0
# Get the slow version from python.
p_joint = get_causal_flow(bowtie_network)
# TODO: They should be the same.
# This actually fails, but not sure why right now. I think it is minor
# stuff. Note that the INFORMATION calculated from these is the same
# below. We're not using causal flow right now anyway...
# numpy.testing.assert_allclose(p_joint, c_joint)
# So should the information.
info = Information(j)
c_info = numpy.asarray(info)[0]
p_info = calc_info_from_probs(p_joint)
numpy.testing.assert_allclose(c_info, p_info)
