Simple_function.py 文件源码

def k_means_cluster_Predict(data_list,info):
    array_diagnal=np.array([[data_list[0][x],data_list[1][x]] for x in range(len(data_list[0]))])
    ks = list(range(1,len(info)))
    KMeans = [cluster.KMeans(n_clusters = i, init="k-means++").fit(array_diagnal) for i in ks]
    BIC = [compute_bic(kmeansi,array_diagnal) for kmeansi in KMeans]
    ks_picked=ks[BIC.index(max(BIC))]
    if ks_picked==1:
        return [data_list]
    else:
        out=[]
        std_rec=[scipy.std(data_list[0]),scipy.std(data_list[1])]
        whitened = whiten(array_diagnal)
        centroids, distortion=kmeans(whitened,ks_picked)
        idx,_= vq(whitened,centroids)
        for x in range(ks_picked):
            group1=[[int(i) for i in array_diagnal[idx==x,0]],[int(i) for i in array_diagnal[idx==x,1]]]
            out.append(group1)
        return out