d114db1e8ef23b2b3ff1fb5d566f2c37b9ee3664,mir_eval/segment.py,,boundary_detection,#Any#Any#Any#Any#Any#,13

Before Change

    // And the f-measure
    F = 0.0

    if P > 0 or R > 0:
        F = (1 + beta**2) * P * R / ((beta**2) * P + R)

    return P, R, F

def boundary_deviation(annotated_boundaries, predicted_boundaries, trim=True):
    """Compute the median deviations between annotated and predicted boundary times.

After Change

    recall      = np.mean(dist.max(axis=1))

    // And the f-measure
    f_measure   = util.f_measure(precision, recall, beta=beta)

    return precision, recall, f_measure

def boundary_deviation(annotated_boundaries, predicted_boundaries, trim=True):
    """Compute the median deviations between annotated and predicted boundary times.

Instances