def test_ais_gaussian():
"""Test multivariate TE estimation on correlated Gaussians.
Run the multivariate TE algorithm on two sets of random Gaussian data with
a given covariance. The second data set is shifted by one sample creating
a source-target delay of one sample. This example is modeled after the
JIDT demo 4 for transfer entropy. The resulting TE can be compared to the
analytical result (but expect some error in the estimate).
Note:
This test runs considerably faster than other system tests.
This produces strange small values for non-coupled sources. TODO
"""
n = 1000
source = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
# Cast everything to numpy so the idtxl estimator understands it.
source = np.array(source)
analysis_opts = {
'kraskov_k': 4,
'normalise': 'false',
'theiler_t': 0,
'noise_level': 1e-8,
'local_values': False,
'tau': 1,
'history': 1,
}
ais_est = Estimator_ais('jidt_kraskov')
ais = ais_est.estimate(source, analysis_opts)
print('AIS for random normal data without memory (calling estimator '
'directly, expected is something close to 0): {0}'.format(ais))
def test_ais_estimator_jidt_gaussian():
"""Test Gaussian AIS estimation on normally-distributed time series.
"""
# Set length of time series
n = 1000
# Set tolerance for assert
assert_tolerance = 0.01
# Generate random normally-distributed time series
source = [rn.normalvariate(0, 1) for r in range(n)]
# Cast everything to numpy so the idtxl estimator understands it.
source = np.array(source)
# Call JIDT to perform estimation
opts = {
'normalise': False,
'theiler_t': 0,
'noise_level': 1e-8,
'local_values': False,
'tau': 1,
'history': 1,
}
est = Estimator_ais('jidt_gaussian')
res = est.estimate(process=source, opts=opts)
# Compute theoretical value for comparison
theoretical_res = 0
print('AIS result: {0:.4f} nats; expected to be '
'{1:.4f} nats.'.format(res, theoretical_res))
assert (np.abs(res - theoretical_res) < assert_tolerance), (
'Test for Gaussians AIS estimator on Gaussian data failed'
'(error larger than {1:.4f}).'.format(assert_tolerance))
def test_ais_estimator_kraskov():
"""Test multivariate TE estimation on correlated Gaussians.
Run the multivariate TE algorithm on two sets of random Gaussian data with
a given covariance. The second data set is shifted by one sample creating
a source-target delay of one sample. This example is modeled after the
JIDT demo 4 for transfer entropy. The resulting TE can be compared to the
analytical result (but expect some error in the estimate).
Note:
This test runs considerably faster than other system tests.
This produces strange small values for non-coupled sources. TODO
"""
n = 1000
source = [rn.normalvariate(0, 1) for r in range(n)] # correlated src
# Cast everything to numpy so the idtxl estimator understands it.
source = np.array(source)
analysis_opts = {
'kraskov_k': 4,
'normalise': False,
'theiler_t': 0,
'noise_level': 1e-8,
'local_values': False,
'tau': 1,
'history': 1,
}
ais_est = Estimator_ais('jidt_kraskov')
ais = ais_est.estimate(source, analysis_opts)
assert np.abs(ais) < 0.1, ('AIS should be close to 0, but is '
'{0}.'.format(ais))
def test_ais_local_values():
"""Test local AIS estimation."""
n = 1000
source = [rn.normalvariate(0, 1) for r in range(n)]
analysis_opts = {
'kraskov_k': 4,
'normalise': False,
'theiler_t': 0,
'noise_level': 1e-8,
'local_values': True,
'history': 3,
}
ais_est = Estimator_ais('jidt_kraskov')
ais_res = ais_est.estimate(np.array(source), analysis_opts)
assert ais_res.shape[0] == n, 'Local AIS estimator did not return an array'
def test_ais_local_values():
"""Test local AIS estimation."""
n = 1000
source = [rn.normalvariate(0, 1) for r in range(n)]
analysis_opts = {
'kraskov_k': 4,
'normalise': 'false',
'theiler_t': 0,
'noise_level': 1e-8,
'local_values': True,
'history': 3,
}
ais_est = Estimator_ais('jidt_kraskov')
ais_res = ais_est.estimate(np.array(source),
analysis_opts)
assert ais_res.shape[0] == n, 'Local AIS estimator did not return an array'
def test_jidt_kraskov_input():
"""Test handling of wrong inputs to the JIDT Kraskov TE-estimator."""
ais_est = Estimator_ais('jidt_kraskov')
process = np.empty((100))
# Wrong type for options dictinoary
with pytest.raises(TypeError):
ais_est.estimate(process=process, opts=None)
# Missing history for the target
analysis_opts = {}
with pytest.raises(RuntimeError):
ais_est.estimate(process=process, opts=analysis_opts)
# Run analysis with all default vales
analysis_opts = {'history': 3}
ais_est.estimate(process=process, opts=analysis_opts)