cluster.py 文件源码

def silhouette_score(series, clusters):
    distances = np.zeros((series.shape[0], series.shape[0]))
    for idx_a, metric_a in enumerate(series):
        for idx_b, metric_b in enumerate(series):
            distances[idx_a, idx_b] = _sbd(metric_a, metric_b)[0]
    labels = np.zeros(series.shape[0])
    for i, (cluster, indicies) in enumerate(clusters):
        for index in indicies:
            labels[index] = i

    # silhouette is only defined, if we have 2 clusters with assignments at 
    # minimum
    if len(np.unique(labels)) == 1 or (len(np.unique(labels)) >= distances.shape[0]):
    #if len(np.unique(labels)) == 1:
        return labels, -1
    else:
        return labels, _silhouette_score(distances, labels, metric='precomputed')