def test_correlation(self):
"""Auto-correlation"""
acorr_amp = np.dot(self.mu * self.obs1, self.R) * np.dot(
self.L, self.obs1)
acorr = np.dot(self.ev_t, acorr_amp)
acorrn = correlation(self.T, self.obs1, k=self.k, times=self.times)
assert_allclose(acorrn, acorr)
"""Cross-correlation"""
corr_amp = np.dot(self.mu * self.obs1, self.R) * np.dot(
self.L, self.obs2)
corr = np.dot(self.ev_t, corr_amp)
corrn = correlation(self.T,
self.obs1,
obs2=self.obs2,
k=self.k,
times=self.times)
assert_allclose(corrn, corr)
def test_correlation(self):
"""Auto-correlation"""
"""k=None"""
acorr_amp = np.dot(self.mu * self.obs1, self.R) * np.dot(
self.L, self.obs1)
acorr = np.dot(self.ev_t, acorr_amp)
acorrn = correlation(self.T, self.obs1, times=self.times)
assert_allclose(acorrn, acorr)
"""k=4"""
k = self.k
acorr_amp = np.dot(self.mu * self.obs1, self.R[:, 0:k]) * np.dot(
self.L[0:k, :], self.obs1)
acorr = np.dot(self.ev_t[:, 0:k], acorr_amp)
acorrn = correlation(self.T, self.obs1, times=self.times, k=k)
assert_allclose(acorrn, acorr)
"""Cross-correlation"""
"""k=None"""
corr_amp = np.dot(self.mu * self.obs1, self.R) * np.dot(
self.L, self.obs2)
corr = np.dot(self.ev_t, corr_amp)
corrn = correlation(self.T,
self.obs1,
obs2=self.obs2,
times=self.times)
assert_allclose(corrn, corr)
"""k=4"""
k = self.k
corr_amp = np.dot(self.mu * self.obs1, self.R[:, 0:k]) * np.dot(
self.L[0:k, :], self.obs2)
corr = np.dot(self.ev_t[:, 0:k], corr_amp)
corrn = correlation(self.T,
self.obs1,
obs2=self.obs2,
times=self.times,
k=k)
assert_allclose(corrn, corr)
def test_correlation(fingerprints_data, observables):
obs1, obs2 = observables
T = fingerprints_data.transition_matrix
R, D, L = rdl_decomposition(T, k=4 if fingerprints_data.sparse else None)
mu = fingerprints_data.stationary_distribution
times = np.array([1, 5, 10, 20, 100])
ev = np.diagonal(D)
ev_t = ev[np.newaxis, :]**times[:, np.newaxis]
if not fingerprints_data.sparse:
"""k=None"""
acorr_amp = np.dot(mu * obs1, R) * np.dot(L, obs1)
acorr = np.dot(ev_t, acorr_amp)
acorrn = correlation(T, obs1, times=times)
assert_allclose(acorrn, acorr)
"""k=4"""
k = 4
acorr_amp = np.dot(mu * obs1, R[:, 0:k]) * np.dot(L[0:k, :], obs1)
acorr = np.dot(ev_t[:, 0:k], acorr_amp)
acorrn = correlation(T, obs1, times=times, k=k)
assert_allclose(acorrn, acorr)
"""Cross-correlation"""
if not fingerprints_data.sparse:
"""k=None"""
corr_amp = np.dot(mu * obs1, R) * np.dot(L, obs2)
corr = np.dot(ev_t, corr_amp)
corrn = correlation(T, obs1, obs2=obs2, times=times)
assert_allclose(corrn, corr)
"""k=4"""
k = k
corr_amp = np.dot(mu * obs1, R[:, 0:k]) * np.dot(L[0:k, :], obs2)
corr = np.dot(ev_t[:, 0:k], corr_amp)
corrn = correlation(T, obs1, obs2=obs2, times=times, k=k)
assert_allclose(corrn, corr)