进入了scikit-learn的学习，检查自己是否已经安装，之前已经安装过了Anaconda，安装教程看我另一个地方的文章把添加链接描述
在命令行输入
python -m pip show scikit-learn # 查看scikit-learn安装的位置及安装的版本
python -m pip freeze # 查看所有在虚拟环境中已下载的包
python -c "import sklearn; sklearn.show_versions()"
来检查是否安装
pycharm被我卸载了，我直接用的python.
均值移位聚类算法的一个例子
print(__doc__)
import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn.datasets import make_blobs
# #############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)
# #############################################################################
# Compute clustering with MeanShift
# The following bandwidth can be automatically detected using
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_)
# #############################################################################
# Plot result
import matplotlib.pyplot as plt
from itertools import cycle
plt.figure(1)
plt.clf()
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
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()
