cluster.py 文件源码

def main():
    centers = get_list('out_center.txt')
    labels = get_list('142-label.txt')
    judge(centers, labels)
    n_class = int(len(centers) * 0.18)
    est = KMeans(n_clusters=n_class, max_iter=1000)
    est.fit(centers)
    new_list = []
    for x, y in est.cluster_centers_:
        min_num = 10000
        min_x = -1
        min_y = -1
        for x_, y_ in centers:
            dist = distance(x, y, x_, y_)
            if (dist < min_num) or (min_x == -1):
                min_num = dist
                min_x = x_
                min_y = y_
        new_list.append([min_x, min_y])
    judge(new_list, labels)
    judge(est.cluster_centers_, labels)

    # db = DBSCAN(eps=0.3, min_samples=180).fit(centers)
    # print(db.core_sample_indices_)
    # judge(new_list, labels)
    # print(est.cluster_centers_)
    # save_list('result.txt', est.cluster_centers_)
    # af = AffinityPropagation(preference=180).fit(centers)
    # judge(af.cluster_centers_, labels)