def test_peakentropy_infodyn_kernel(self):
self.entropies_infodyn_kernel = []
for timelag in range(self.delay - 5, self.delay + 6):
print("Results for timelag of: ", str(timelag))
[_, x_hist, y_hist] = autoreg_datagen(
self.delay, timelag, self.samples, self.sub_samples
)
# Normalize data
# Not explicitly required as this is done by infodynamics package
# if setProperty("NORMALISE", "true" is called), but good practice
# for general example.
x_hist_norm = preprocessing.scale(x_hist, axis=1)
y_hist_norm = preprocessing.scale(y_hist, axis=1)
# Calculate transfer entropy according to infodynamics method:
result_infodyn, _ = te_info(
"infodynamics.jar",
"kernel",
x_hist_norm[0],
y_hist_norm[0],
**{"kernel_width": 0.1}
)
self.entropies_infodyn_kernel.append(result_infodyn)
print("Infodynamics TE result: %.4f bits" % result_infodyn)
print(self.entropies_infodyn_kernel)
maxval = max(self.entropies_infodyn_kernel)
# The maximum lags with one sample
delayedval = self.entropies_infodyn_kernel[self.delay]
self.assertEqual(maxval, delayedval)
def test_peakentropy_infodyn_kraskov_noautoembed(self):
self.entropies_infodyn_kraskov = []
for timelag in range(self.delay - 5, self.delay + 6):
print("Results for timelag of: ", str(timelag))
[x_pred, x_hist,
y_hist] = autoreg_datagen(self.delay, timelag, self.samples,
self.sub_samples)
# Normalize data
# Not explicitly required as this is done by infodyns package if
# setProperty("NORMALISE", "true" is called), but good practice
# for general example.
x_hist_norm = preprocessing.scale(x_hist, axis=1)
y_hist_norm = preprocessing.scale(y_hist, axis=1)
# Calculate transfer entropy according to infodynamics method:
result_infodyn, [significance, properties] = \
te_info('infodynamics.jar', 'kraskov',
x_hist_norm[0], y_hist_norm[0],
test_significance=False,
auto_embed=False)
self.entropies_infodyn_kraskov.append(result_infodyn)
print("Infodynamics TE result: %.4f bits" % result_infodyn)
print(properties)
print(significance)
print(self.entropies_infodyn_kraskov)
maxval = max(self.entropies_infodyn_kraskov)
# The maximum lags with one sample
delayedval = self.entropies_infodyn_kraskov[self.delay]
self.assertEqual(maxval, delayedval)
def test_peakentropy_infodyn_kraskov_autoembed(self):
self.entropies_infodyn_kraskov = []
for timelag in range(self.delay - 5, self.delay + 6):
print("Results for timelag of: ", str(timelag))
[x_pred, x_hist, y_hist] = autoreg_datagen(
self.delay, timelag, self.samples, self.sub_samples
)
# Normalize data
# Not explicitly required as this is done by infodyns package if
# setProperty("NORMALISE", "true" is called), but good practice
# for general example.
x_hist_norm = preprocessing.scale(x_hist, axis=1)
y_hist_norm = preprocessing.scale(y_hist, axis=1)
# Calculate transfer entropy according to infodynamics method:
result_infodyn, [
[te_significance, mi_significance],
properties,
mutualinfo,
] = te_info(
"infodynamics.jar",
"kraskov",
x_hist_norm[0],
y_hist_norm[0],
test_significance=False,
auto_embed=True,
)
self.entropies_infodyn_kraskov.append(result_infodyn)
print("Infodynamics TE result: %.4f bits" % result_infodyn)
print(properties)
print(te_significance)
print(mi_significance)
print(mutualinfo)
print(self.entropies_infodyn_kraskov)
maxval = max(self.entropies_infodyn_kraskov)
# The maximum lags with one sample
delayedval = self.entropies_infodyn_kraskov[self.delay]
self.assertEqual(maxval, delayedval)