def test_subsampler_lag2():
n_traj, n_samples, n_features = 3, 100, 7
lag_time = 2
X_all_0 = [random.normal(size=(n_samples, n_features)) for i in range(n_traj)]
q_0 = np.concatenate(X_all_0)
subsampler = Subsampler(lag_time=lag_time)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(((n_samples - lag_time + 2) * n_traj, n_features), q_1.shape)
subsampler = Subsampler(lag_time=lag_time, sliding_window=False)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(((n_samples / lag_time) * n_traj, n_features), q_1.shape)
def test_subsampler_lag1():
n_traj, n_samples, n_features = 3, 100, 7
lag_time = 1
X_all_0 = [random.normal(size=(n_samples, n_features)) for i in range(n_traj)]
q_0 = np.concatenate(X_all_0)
subsampler = Subsampler(lag_time=lag_time)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(q_0.shape, q_1.shape)
eq(q_0.mean(0), q_1.mean(0))
eq(q_0.std(0), q_1.std(0))
subsampler = Subsampler(lag_time=lag_time, sliding_window=False)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(q_0.shape, q_1.shape)
eq(q_0.mean(0), q_1.mean(0))
eq(q_0.std(0), q_1.std(0))
def test_subsampler_tica():
n_traj, n_samples, n_features = 1, 500, 4
lag_time = 2
X_all_0 = [
random.normal(size=(n_samples, n_features)) for i in range(n_traj)
]
tica_0 = tICA(lag_time=lag_time)
tica_0.fit(X_all_0)
subsampler = Subsampler(lag_time=lag_time)
tica_1 = tICA()
pipeline = sklearn.pipeline.Pipeline([("subsampler", subsampler),
('tica', tica_1)])
pipeline.fit(X_all_0)
eq(tica_0.n_features, tica_1.n_features) # Obviously true
eq(tica_0.n_observations_, tica_1.n_observations_)
eq(
tica_0.eigenvalues_, tica_1.eigenvalues_
) # The eigenvalues should be the same. NOT the timescales, as tica_1 has timescales calculated in a different time unit
def test_subsampler_lag2():
n_traj, n_samples, n_features = 3, 100, 7
lag_time = 2
X_all_0 = [
random.normal(size=(n_samples, n_features)) for i in range(n_traj)
]
q_0 = np.concatenate(X_all_0)
subsampler = Subsampler(lag_time=lag_time)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(((n_samples - lag_time + 2) * n_traj, n_features), q_1.shape)
subsampler = Subsampler(lag_time=lag_time, sliding_window=False)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(((n_samples / lag_time) * n_traj, n_features), q_1.shape)
def test_subsampler_lag1():
n_traj, n_samples, n_features = 3, 100, 7
lag_time = 1
X_all_0 = [
random.normal(size=(n_samples, n_features)) for i in range(n_traj)
]
q_0 = np.concatenate(X_all_0)
subsampler = Subsampler(lag_time=lag_time)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(q_0.shape, q_1.shape)
eq(q_0.mean(0), q_1.mean(0))
eq(q_0.std(0), q_1.std(0))
subsampler = Subsampler(lag_time=lag_time, sliding_window=False)
X_all_1 = subsampler.transform(X_all_0)
q_1 = np.concatenate(X_all_1)
eq(q_0.shape, q_1.shape)
eq(q_0.mean(0), q_1.mean(0))
eq(q_0.std(0), q_1.std(0))
