def test_lagged_mi():
"""Test estimation of lagged MI."""
n = 10000
cov = 0.4
source = [rn.normalvariate(0, 1) for r in range(n)]
target = [0] + [sum(pair) for pair in zip(
[cov * y for y in source[0:n - 1]],
[(1 - cov) * y for y in
[rn.normalvariate(0, 1) for r in range(n - 1)]])]
source = np.array(source)
target = np.array(target)
settings = {
'discretise_method': 'equal',
'n_discrete_bins': 4,
'history': 1,
'history_target': 1,
'lag_mi': 1,
'source_target_delay': 1}
est_te_k = JidtKraskovTE(settings)
te_k = est_te_k.estimate(source, target)
est_te_d = JidtDiscreteTE(settings)
te_d = est_te_d.estimate(source, target)
est_d = JidtDiscreteMI(settings)
mi_d = est_d.estimate(source, target)
est_k = JidtKraskovMI(settings)
mi_k = est_k.estimate(source, target)
est_g = JidtGaussianMI(settings)
mi_g = est_g.estimate(source, target)
_compare_result(mi_d, te_d, 'JidtDiscreteMI', 'JidtDiscreteTE',
'lagged MI', tol=0.05)
_compare_result(mi_k, te_k, 'JidtKraskovMI', 'JidtKraskovTE',
'lagged MI', tol=0.05)
_compare_result(mi_g, te_k, 'JidtGaussianMI', 'JidtKraskovTE',
'lagged MI', tol=0.05)
def test_one_two_dim_input_gaussian():
"""Test one- and two-dimensional input for Gaussian estimators."""
expected_mi, src_one, s, target_one = _get_gauss_data(expand=False)
src_two = np.expand_dims(src_one, axis=1)
target_two = np.expand_dims(target_one, axis=1)
ar_src_one, s = _get_ar_data(expand=False)
ar_src_two = np.expand_dims(ar_src_one, axis=1)
# MI
mi_estimator = JidtGaussianMI(settings={})
mi_cor_one = mi_estimator.estimate(src_one, target_one)
_assert_result(mi_cor_one, expected_mi, 'JidtGaussianMI', 'MI')
mi_cor_two = mi_estimator.estimate(src_two, target_two)
_assert_result(mi_cor_two, expected_mi, 'JidtGaussianMI', 'MI')
_compare_result(mi_cor_one, mi_cor_two,
'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'MI')
# CMI
cmi_estimator = JidtGaussianCMI(settings={})
mi_cor_one = cmi_estimator.estimate(src_one, target_one)
_assert_result(mi_cor_one, expected_mi, 'JidtGaussianCMI', 'CMI')
mi_cor_two = cmi_estimator.estimate(src_two, target_two)
_assert_result(mi_cor_two, expected_mi, 'JidtGaussianCMI', 'CMI')
_compare_result(mi_cor_one, mi_cor_two,
'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'CMI')
# TE
te_estimator = JidtGaussianTE(settings={'history_target': 1})
mi_cor_one = te_estimator.estimate(src_one[1:], target_one[:-1])
_assert_result(mi_cor_one, expected_mi, 'JidtGaussianTE', 'TE')
mi_cor_two = te_estimator.estimate(src_one[1:], target_one[:-1])
_assert_result(mi_cor_two, expected_mi, 'JidtGaussianTE', 'TE')
_compare_result(mi_cor_one, mi_cor_two,
'JidtGaussianMI one dim', 'JidtGaussianMI two dim', 'TE')
# AIS
ais_estimator = JidtGaussianAIS(settings={'history': 2})
mi_cor_one = ais_estimator.estimate(ar_src_one)
mi_cor_two = ais_estimator.estimate(ar_src_two)
_compare_result(mi_cor_one, mi_cor_two,
'JidtGaussianAIS one dim', 'JidtGaussianAIS two dim',
'AIS (AR process)')
def test_one_two_dim_input_gaussian():
"""Test one- and two-dimensional input for Gaussian estimators."""
expected_mi, src_one, s, target_one = _get_gauss_data(expand=False,
seed=SEED)
src_two = np.expand_dims(src_one, axis=1)
target_two = np.expand_dims(target_one, axis=1)
ar_src_one, s = _get_ar_data(expand=False, seed=SEED)
ar_src_two = np.expand_dims(ar_src_one, axis=1)
# MI
mi_estimator = JidtGaussianMI(settings={})
mi_cor_one = mi_estimator.estimate(src_one, target_one)
_assert_result(mi_cor_one, expected_mi, 'JidtGaussianMI', 'MI')
mi_cor_two = mi_estimator.estimate(src_two, target_two)
_assert_result(mi_cor_two, expected_mi, 'JidtGaussianMI', 'MI')
_compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim',
'JidtGaussianMI two dim', 'MI')
# CMI
cmi_estimator = JidtGaussianCMI(settings={})
mi_cor_one = cmi_estimator.estimate(src_one, target_one)
_assert_result(mi_cor_one, expected_mi, 'JidtGaussianCMI', 'CMI')
mi_cor_two = cmi_estimator.estimate(src_two, target_two)
_assert_result(mi_cor_two, expected_mi, 'JidtGaussianCMI', 'CMI')
_compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim',
'JidtGaussianMI two dim', 'CMI')
# TE
te_estimator = JidtGaussianTE(settings={'history_target': 1})
mi_cor_one = te_estimator.estimate(src_one[1:], target_one[:-1])
_assert_result(mi_cor_one, expected_mi, 'JidtGaussianTE', 'TE')
mi_cor_two = te_estimator.estimate(src_one[1:], target_one[:-1])
_assert_result(mi_cor_two, expected_mi, 'JidtGaussianTE', 'TE')
_compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianMI one dim',
'JidtGaussianMI two dim', 'TE')
# AIS
ais_estimator = JidtGaussianAIS(settings={'history': 2})
mi_cor_one = ais_estimator.estimate(ar_src_one)
mi_cor_two = ais_estimator.estimate(ar_src_two)
_compare_result(mi_cor_one, mi_cor_two, 'JidtGaussianAIS one dim',
'JidtGaussianAIS two dim', 'AIS (AR process)')
def test_lagged_mi():
"""Test estimation of lagged MI."""
n = 10000
cov = 0.4
source = [rn.normalvariate(0, 1) for r in range(n)]
target = [0] + [
sum(pair) for pair in zip([cov * y for y in source[0:n - 1]], [
(1 - cov) * y
for y in [rn.normalvariate(0, 1) for r in range(n - 1)]
])
]
source = np.array(source)
target = np.array(target)
settings = {
'discretise_method': 'equal',
'n_discrete_bins': 4,
'history': 1,
'history_target': 1,
'lag_mi': 1,
'source_target_delay': 1
}
est_te_k = JidtKraskovTE(settings)
te_k = est_te_k.estimate(source, target)
est_te_d = JidtDiscreteTE(settings)
te_d = est_te_d.estimate(source, target)
est_d = JidtDiscreteMI(settings)
mi_d = est_d.estimate(source, target)
est_k = JidtKraskovMI(settings)
mi_k = est_k.estimate(source, target)
est_g = JidtGaussianMI(settings)
mi_g = est_g.estimate(source, target)
_compare_result(mi_d,
te_d,
'JidtDiscreteMI',
'JidtDiscreteTE',
'lagged MI',
tol=0.05)
_compare_result(mi_k,
te_k,
'JidtKraskovMI',
'JidtKraskovTE',
'lagged MI',
tol=0.05)
_compare_result(mi_g,
te_k,
'JidtGaussianMI',
'JidtKraskovTE',
'lagged MI',
tol=0.05)