def test_mi(data_with_redundancy): # pylint: disable=redefined-outer-name
random.seed(SEED)
from ndd.nsb import mutual_information
h1 = ndd.from_data(data_with_redundancy[1])
h2 = ndd.from_data(data_with_redundancy[2])
h12 = ndd.from_data(data_with_redundancy[[1, 2]])
mi = h1 + h2 - h12
assert numpy.isclose(mutual_information(data_with_redundancy[[1, 2]]), mi)
def test_mi(data_with_redundancy):
random.seed(SEED)
from ndd.nsb import mutual_information
h1 = ndd.from_data(data_with_redundancy[1])
h2 = ndd.from_data(data_with_redundancy[2])
h12 = ndd.from_data(data_with_redundancy[[1, 2]])
mi = h1 + h2 - h12
estimate = mutual_information(data_with_redundancy[[1, 2]])
assert numpy.isclose(estimate, mi)
def test_conditional_entropy(data_with_redundancy):
random.seed(SEED)
from ndd.nsb import mutual_information
data = data_with_redundancy[[1, 2]]
estimate = (ndd.from_data(data) - ndd.conditional_entropy(data, c=0) -
ndd.conditional_entropy(data, c=1))
assert numpy.isclose(estimate, mutual_information(data), atol=0.01)
def test_conditional_entropy(data_with_redundancy): # pylint: disable=redefined-outer-name
random.seed(SEED)
from ndd.nsb import mutual_information
data = data_with_redundancy[[1, 2]]
assert numpy.isclose(mutual_information(data),
ndd.from_data(data) -
ndd.conditional_entropy(data, c=0) -
ndd.conditional_entropy(data, c=1),
atol=0.01)
def test_mmi(data_with_redundancy): # pylint: disable=redefined-outer-name
random.seed(SEED)
from ndd.nsb import interaction_information
h0 = ndd.from_data(data_with_redundancy[0])
h1 = ndd.from_data(data_with_redundancy[1])
h2 = ndd.from_data(data_with_redundancy[2])
h01 = ndd.from_data(data_with_redundancy[[0, 1]])
h02 = ndd.from_data(data_with_redundancy[[0, 2]])
h12 = ndd.from_data(data_with_redundancy[[1, 2]])
h012 = ndd.from_data(data_with_redundancy)
mmi = -(h0 + h1 + h2 - h01 - h02 - h12 + h012)
assert numpy.isclose(interaction_information(data_with_redundancy), mmi)
def test_mmi(data_with_redundancy):
random.seed(SEED)
from ndd.nsb import interaction_information
h0 = ndd.from_data(data_with_redundancy[0], ks=[3])
h1 = ndd.from_data(data_with_redundancy[1], ks=[3])
h2 = ndd.from_data(data_with_redundancy[2], ks=[3])
h01 = ndd.from_data(data_with_redundancy[[0, 1]], ks=[3] * 2)
h02 = ndd.from_data(data_with_redundancy[[0, 2]], ks=[3] * 2)
h12 = ndd.from_data(data_with_redundancy[[1, 2]], ks=[3] * 2)
h012 = ndd.from_data(data_with_redundancy, ks=[3] * 3)
mmi = -(h0 + h1 + h2 - h01 - h02 - h12 + h012)
estimate = interaction_information(data_with_redundancy, ks=[3] * 3)
assert numpy.isclose(estimate, mmi, atol=0.01)