def __init__(self, *args, time_lag=10, **kwargs):
super().__init__(*args, **kwargs)
self.time_lag = time_lag
if time_lag > 0:
self.model = tICA(n_components=self.n_latent, lag_time=time_lag)
elif time_lag == 0:
self.model = FastICA(n_components=self.n_latent,
random_state=self.random_state)
else:
raise ValueError(
"Time delay parameter must be greater than or equal to zero.")
def train_tica(X, num_hidden=10, time_lag=10, random_seed=None):
"""
Instantiate and fit a tICA model, and return a function that embeds
additional datasets
Inputs:
- X : ndarray with shape (n_timepoints, t_lag, n_features)
- num_hidden : int, the number of embedding coordinates
- time_lag : int, the time lag to use to construct the embedding
- random_seed : ignored
Returns:
- embed_func : a function that takes additional coordinates and embeds them
"""
tica = tICA(n_components=num_hidden, lag_time=time_lag)
tica.fit([np.reshape(X, (X.shape[0], -1))])
embed_func = lambda y: tica.transform([np.reshape(y, (y.shape[0], -1))])[0]
return embed_func