def decompose_features(features, decomposer, n_components=None, lag_time=1):
'''
Decomposing features is a way to reduce the dimension of the features.
Each of the components is a eigenvector of the feature space, dimension: (n_features,)
The old features are transformed to the new feature space.
Consider one sample, which is vectorized to (n_features,).T,
apply the transform matrix, which is in the shape (n_components, n_features),
we will get its projection onto the new space (n_components,).
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Input
features : array-like, length n_trajs, each of shape (n_samples, n_features)
Output
features_new : array-like, length n_trajs, each of shape (n_samples, n_components) ((n_samples, n_samples) if n_components = None)
dcmp.components_ : shape (n_components, n_features), ((n_samples, n_features) if n_components = None)
PCA : Principal axes in feature space, representing the directions of maximum variance in the data.
tICA : Components with maximum autocorrelation.
'''
if decomposer == 'PCA':
from msmbuilder.decomposition import PCA
dcmp = PCA(n_components=n_components)
elif decomposer == 'tICA':
from msmbuilder.decomposition import tICA
dcmp = tICA(n_components=n_components, lag_time=lag_time)
features_new = dcmp.fit_transform(features)
return features_new, dcmp.components_
def decompose_features(features, decomposer, n_components=None, lag_time=1):
'''
Input
features : list of arrays, length n_trajs, each of shape (n_samples, n_features)
Output
features_new : list of arrays, length n_trajs, each of shape (n_samples, n_features_new)
'''
if decomposer == 'PCA':
from msmbuilder.decomposition import PCA
dcmp = PCA(n_components=n_components)
elif decomposer == 'tICA':
from msmbuilder.decomposition import tICA
dcmp = tICA(n_components=n_components, lag_time=lag_time)
return dcmp.fit_transform(features)
