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2.6.7 例1–对人工数据集的K均值聚类
import tensorflow as tf
import numpy as np
import time
import matplotlib.pyplot as plt
import matplotlib
from sklearn.datasets.samples_generator import make_blobs
from sklearn.datasets.samples_generator import make_circles
DATA_TYPE='blobs'
# 数据样本数 N
N=200
# 如果我们选择环状，聚类数只有2个(Number of clusters,if we choose circles,only 2 will be enough)
if (DATA_TYPE=='circle'):
K=2
else:
K=4
#最大迭代次数设置，如果没有说明(Maximum number of iterations, if the conditions are not met)
MAX_ITERS=1000
# 开始时间
start=time.time()
centers=[(-2,-2),(-2,1.5),(1.5,-2),(2,1.5)]
if (DATA_TYPE=='circle'):
data,features=make_circles(n_samples=N,shuffle=True,noise=0.01,factor=0.4)
# n_samples 数据的数目
# shuffle 数据是否打乱(True/False)
# noise 添加到圆形数据集上的随机噪声数据
# factor 环形数据间的比例因子
else:
data,features=make_blobs(n_samples=N,centers=centers,n_features=2,shuffle=False,random_state=42)
# n_samples 数据的数目
# centers 类的中心
# n_features 特征数据的列的数目(维度)
# shuffle 数据是否打乱(True/False)
# random_state 随机种子
# 聚类中心点
fig,ax=plt.subplots()
ax.scatter(np.array(centers).transpose()[0],np.array(centers).transpose()[1],marker='o',s=200)
plt.show()
#如果DATA_TYPE是blobs型，绘图
fig,ax=plt.subplots()
if (DATA_TYPE=='blobs'):
ax.scatter(np.asarray(centers).transpose()[0],np.asarray(centers).transpose()[1],marker='o',s=250,c='0.1')
ax.scatter(np.asarray(data).transpose()[0],np.asarray(data).transpose()[1],marker='o',s=80,c=features,cmap=plt.cm.coolwarm)
plt.show()
# points 用来存放数据集的点的坐标
points=tf.Variable(data) # 200*2
# cluster_assigments用来存放为每个数据元素分配的类的索引
cluster_assigments=tf.Variable(tf.zeros([N],dtype=tf.int64))
# centrroids 用于存放每个组质心的坐标,随机选择相应的聚类中心
centroids=tf.Variable(tf.slice(points.initialized_value(),[0,0],[K,2]))
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(centroids)
rep_centriods=tf.reshape(tf.tile(centroids,[N,1]),[N,K,2])
# tf.tile(centroids,[N,b]) 的结果是：
# [centroids ... centroids
# . .
# . .
# . .
# centroids ... centroids]的 N*b 矩阵
rep_points=tf.reshape(tf.tile(points,[1,K]),[N,K,2]) # 复制原数据拓展列数为原数据的K倍
# sum_squares计算每一个样本到每一个质心点之间在所有维度上的距离
sum_squares=tf.reduce_sum(tf.square(rep_points-rep_centriods),reduction_indices=2) # 减少索引，第三层求和，总量变为(200,4) . reduction_indices=0(列和)|reduction_indices=1(行和)
# 对所有维度求和，得到和最小的那个索引(这个索引就是每个点所属的新的类)
best_centroids=tf.argmin(sum_squares,1) # 数据为200
# did_assignents_change 用来判断是否有变更
did_assignents_change=tf.reduce_any(tf.not_equal(best_centroids,cluster_assigments)) # 布尔值，结果为True
# ------------1------------tf.not_equal
# t=tf.not_equal(a,b)#只要前后是不一样的，那就符合要求，为True,否则为False
# t=tf.not_equal([1,1],[0,0])
# print(t.eval(session=sess))
# t=tf.not_equal([0,1],[0,0])
# print(t.eval(session=sess))
# t=tf.not_equal([1,1],[0,1])
# print(t.eval(session=sess))
# ------------2------------tf.constant
# x = tf.constant([[True, True], [False, False]])
# tf.reduce_any(x) # True
# tf.reduce_any(x, 0) # [True, True]
# tf.reduce_any(x, 1) # [True, False]
def bucket_mean(data,bucket_ids,num_buckets):
total = tf.unsorted_segment_sum(data,bucket_ids,num_buckets)# 统计每个类的数据总和
count = tf.unsorted_segment_sum(tf.ones_like(data),bucket_ids,num_buckets)# 统计每个类的数量
# 关于 tf.ones_like 只要含有数字都变为1
# tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
# tf.ones_like(tensor) # [[1, 1, 1], [1, 1, 1]]
return total/count # 返回每个类的均值
means=bucket_mean(points,best_centroids,K) # 样本数据的均值 4*2
with tf.control_dependencies([did_assignents_change]):
do_updates=tf.group(centroids.assign(means),cluster_assigments.assign(best_centroids)) # 更新聚类中心和分类索引号
changed=True
iters=0
fig,ax=plt.subplots()
if (DATA_TYPE=='blobs'):
colourindexes=[2,1,4,3]
else:
colourindexes=[2,1]
while changed and iters<MAX_ITERS:
fig,ax=plt.subplots()
iters +=1
[changed,_]=sess.run([did_assignents_change,do_updates])
[centers,assignments]=sess.run([centroids,cluster_assigments])
ax.scatter(sess.run(points).transpose()[0],sess.run(points).transpose()[1],marker='o',s=200,c=assignments,cmap=plt.cm.coolwarm)
ax.scatter(centers[:,0],centers[:,1],marker='^',s=550,c=colourindexes,cmap=plt.cm.plasma)
ax.set_title('Iteration'+str(iters))
plt.savefig('kmeans'+str(iters)+'.jpg')
ax.scatter(sess.run(points).transpose()[0],sess.run(points).transpose()[1],marker='o',s=200,c=assignments,cmap=plt.cm.coolwarm)
plt.show()
end=time.time()
print('Found in %.2f seconds'%(end-start),iters,'Iterations')
print('Centroids:')
print(centers)
print('Cluster assignments:',assignments)
输出图片为：
输出内容结果为：
Found in 2.29 seconds 7 Iterations
Centroids:
[[ 1.73827021 -2.0632449 ]
[-2.06727397 1.59287742]
[-2.08722519 -2.12335454]
[ 2.17032956 1.60369927]]
Cluster assignments: [2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 3 0 0 0 0 0 0
0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
3 3 3 3 3 3 1 3 3 3 3 3 3 3 3]