sklearn MeanShift

MeanShift方法基本上是通过核加权实现质心漂移的方法。 sklearn.cluster.estimate_bandwith: 用于估计加权核的带宽，n_samples参数指定用于估计的样本数，quantile指定至少 被使用的指定数量样本数的分位数。（取值与[0, 1]） sklearn.cluster.MeanShift: bin_seeding用来设定初始核的位置参数的生成方式，default False,默认采用所有点的 位置平均，当改为True时使用离散后的点的平均，前者比后者慢。 plt.clf():清空图形窗口。 下面是一个例子： import numpy as np from sklearn.cluster import MeanShift, estimate_bandwidth from sklearn.datasets.samples_generator import make_blobs centers = [[1, 1], [-1, -1], [1, -1]] X, _ = make_blobs(n_samples = 10000, centers = centers, cluster_std = 0.6) bandwidth = estimate_bandwidth(X, quantile = 0.2, n_samples = 500) ms = MeanShift(bandwidth = bandwidth, bin_seeding = True) ms.fit(X) labels = ms.labels_ cluster_centers = ms.cluster_centers_ labels_unique = np.unique(labels) n_clusters_ = len(labels_unique) print "number of estimated clusters: %d" % n_clusters_ import matplotlib.pyplot as plt from itertools import cycle plt.figure(1) plt.clf() colors = cycle('bgrcmyk') for k, col in zip(range(n_clusters_), colors):  my_members = labels == k  cluster_center = cluster_centers[k]  plt.plot(X[my_members, 0], X[my_members, 1], col + '.')  plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor = col, markeredgecolor = 'k', markersize = 14) plt.title("Estimated number of clusters: %d" % n_clusters_) plt.show()