msaf/algorithms/cnmf/segmenter.py
def compute_labels(X, rank, R, bound_idxs, niter=300):
    """Computes the labels using the bounds."""

    try:
        F, G = cnmf(X, rank, niter=niter, hull=False)
    except:
        return [1]

    label_frames = filter_activation_matrix(G.T, R)
    label_frames = np.asarray(label_frames, dtype=int)

    #labels = [label_frames[0]]
    labels = []
    bound_inters = zip(bound_idxs[:-1], bound_idxs[1:])
    for bound_inter in bound_inters:
        if bound_inter[1] - bound_inter[0] <= 0:
            labels.append(np.max(label_frames) + 1)
        else:
            labels.append(most_frequent(
                label_frames[bound_inter[0]: bound_inter[1]]))
        #print bound_inter, labels[-1]
    #labels.append(label_frames[-1])

    return labels
def compute_labels(X, rank, R, bound_idxs, niter=300):
    """Computes the labels using the bounds."""

    try:
        F, G = cnmf(X, rank, niter=niter, hull=False)
    except:
        return [1]

    label_frames = filter_activation_matrix(G.T, R)
    label_frames = np.asarray(label_frames, dtype=int)

    #labels = [label_frames[0]]
    labels = []
    bound_inters = zip(bound_idxs[:-1], bound_idxs[1:])
    for bound_inter in bound_inters:
        if bound_inter[1] - bound_inter[0] <= 0:
            labels.append(np.max(label_frames) + 1)
        else:
            labels.append(most_frequent(
                label_frames[bound_inter[0]: bound_inter[1]]))
        #print bound_inter, labels[-1]
    #labels.append(label_frames[-1])

    return labels
Computes the labels using the bounds.