6eabf1eeaa3c9d64b67d3f29b3c546baa8235671,seglearn/features.py,,_compute_features,#Any#Any#,186

Before Change

    D = X["ts"].shape[2]

    Nuv = len(features)
    fts = np.zeros((N, Nuv * D))

    // computed features
    for i in range(Nuv):
        fts[:, i * D:((i + 1) * D)] = features[i](X["ts"])

    // static features
    hnames = [h for h in X.dtype.names if h != "ts"]
    for h in hnames:
        fts = np.column_stack([fts,np.full(N,X[h])])

    return fts

def mean_crossings(X):

After Change

    N = X["ts"].shape[0]
    Xt = np.atleast_3d(X["ts"])
    // computed features
    fts = [features[i](Xt) for i in range(len(features))]
    // static features
    s_fts = [np.full(N,X[s]) for s in X.dtype.names if s!= "ts"]
    fts = np.column_stack(fts+s_fts)

Instances