cluster.py 文件源码

def get_initial_clustering(service, metadata, metrics):

    s_score, cluster_metrics = msu.get_cluster_metrics(metadata, service)

    if not cluster_metrics:
        return None

    common_metrics_all = set()
    initial_idx = np.zeros(len(metrics), dtype=np.int)

    for key, value in cluster_metrics.iteritems():
        other_metrics = value['other_metrics']

        common_metrics = set(metrics) & set(other_metrics)
        for metric in list(common_metrics):
            initial_idx[list(metrics).index(metric)] = int(key)

        common_metrics_all = (common_metrics_all | common_metrics)

    # assign remaining metrics to a new cluster (if any)
    remaining_metrics = list(metrics - common_metrics_all)
    remaining_cluster = max(initial_idx) + 1
    if remaining_metrics:
        for metric in remaining_metrics:
            initial_idx[list(metrics).index(metric)] = remaining_cluster

    return initial_idx