sklearn 是一个 Python 的 科学计算库，提供了数种聚类算法可供选择
numpy、scipy 是 Python 的科学运算库，matplotlib 是图形库，用于绘图
首先是安装环境
sudo pipinstallnumpy scipy sklearn matplotlib
# 安装完成后跑一下 scipy 的测试
importscipyscipy.test()
如果有报错的话就把 numpy 和 scipy 卸载了重新装一次
装 sklearn 后常常会有冲突，重装一下 numpy 和 scipy 就好了
sudo pipuninstallnumpy, scipy
sudo pipinstallnumpy, scipy
如果是 Mac 系统的话，建议把 /Library/Python/2.7/site-packages/ 里面的 numpy、scipy 等库都删了再安装，不然很容易因为版本不同而冲突
效果图
范例代码（等以后有空再来逐行解释）
#! /usr/bin/env python
# -*- coding: utf-8
from __future__ import unicode_literals
import datetime
from collections import Counter
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from sklearn import cluster
from sklearn import datasets
iris = datasets.load_iris()
# X_iris = iris.data[50: 100]
X_iris = iris.data
Y_iris = iris.target
geo = 231
# X_iris = np.delete(X_iris, 3, axis=1)
# X_iris /= 10.
def timeit(name=None):
"""
@decorate
"""
def wrapper2(func):
def wrapper1(*args, **kargs):
start = datetime.datetime.now()
r = func(*args, **kargs)
end = datetime.datetime.now()
print '---------------'
print 'project name: %s' % name
print 'start at: %s' % start
print 'end at: %s' % end
print 'cost: %s' % (end - start)
print 'res: %s' % r
print 'err1: %s' \
% (50 - Counter(r[: 50]).most_common()[0][1])
print 'err2: %s' \
% (50 - Counter(r[50: 100]).most_common()[0][1])
print 'err3: %s' \
% (50 - Counter(r[100: 150]).most_common()[0][1])
print '---------------'
return r
return wrapper1
return wrapper2
def randrange(n, vmin, vmax):
return (vmax - vmin) * np.random.rand(n) + vmin
@timeit('target')
def target(fig):
global X_iris, Y_iris, geo
ax = fig.add_subplot(geo + 0, projection='3d', title='target')
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], c=['r', 'y', 'g'][Y_iris[n]], marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return Y_iris
# kmeans
@timeit('kmeans')
def kmeans(fig):
global X_iris, geo
ax = fig.add_subplot(geo + 1, projection='3d', title='k-means')
k_means = cluster.KMeans(init='random', n_clusters=3)
k_means.fit(X_iris)
res = k_means.labels_
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], c='bgrcmyk'[res[n] % 7], marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return res
@timeit('mini_batch_kmeans')
def mini_batch(fig):
global X_iris, geo
ax = fig.add_subplot(geo + 2, projection='3d', title='mini-batch')
mini_batch = cluster.MiniBatchKMeans(init='random', n_clusters=3)
mini_batch.fit(X_iris)
res = mini_batch.labels_
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], c='bgrcmyk'[res[n] % 7], marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return res
@timeit('affinity')
def affinity(fig):
global X_iris, geo
ax = fig.add_subplot(geo + 3, projection='3d', title='affinity')
affinity = cluster.AffinityPropagation(preference=-50)
affinity.fit(X_iris)
res = affinity.labels_
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], c='bgrcmyk'[res[n] % 7], marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return res
@timeit('mean_shift')
def mean_shift(fig):
global X_iris, geo
ax = fig.add_subplot(geo + 4, projection='3d', title='mean_shift')
bandwidth = cluster.estimate_bandwidth(X_iris, quantile=0.2, n_samples=50)
mean_shift = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
mean_shift.fit(X_iris)
res = mean_shift.labels_
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], c='bgrcmyk'[res[n] % 7], marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return res
@timeit('dbscan')
def dbscan(fig):
global X_iris, geo
ax = fig.add_subplot(geo + 5, projection='3d', title='dbscan')
dbscan = cluster.DBSCAN()
dbscan.fit(X_iris)
res = dbscan.labels_
core = dbscan.core_sample_indices_
print repr(core)
size = [5 if i not in core else 40 for i in range(len(X_iris))]
print repr(size)
for n, i in enumerate(X_iris):
ax.scatter(*i[: 3], s=size[n], c='bgrcmyk'[res[n] % 7],
alpha=0.8, marker='o')
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
return res
def main():
fig = plt.figure()
target(fig)
kmeans(fig)
mini_batch(fig)
affinity(fig)
mean_shift(fig)
dbscan(fig)
plt.show()
if __name__ == '__main__':
main()
--------------------
参考资料
