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kmeans.py
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# -*- coding: utf-8 -*-
"""
2019/04/17
Liu Jianlin
"""
import numpy as np
import time
import matplotlib.pyplot as plt
# calculate Euclidean distance
def eucldistance(vector1, vector2):
return abs(vector2 - vector1) # 求这两个矩阵的距离，vector1、2均为矩阵
# init centroids with random samples
# 在样本集中随机选取k个样本点作为初始质心
def initcentroids(dataSet, k):
numSamples, dim = dataSet.shape # 矩阵的行数、列数
centroids = np.zeros((k, dim)) # 感觉要不要你都可以
for i in range(k):
index = int(np.random.uniform(0, numSamples)) # 随机产生一个浮点数，然后将其转化为int型
centroids[i, :] = dataSet[index, :]
return centroids
####
# k-means cluster
# dataSet为一个矩阵
# k为将dataSet矩阵中的样本分成k个类
def kmeans(dataSet, k):
numSamples = dataSet.shape[0] # 读取矩阵dataSet的第一维度的长度,即获得有多少个样本数据
# first column stores which cluster this sample belongs to,
# second column stores the error between this sample and its centroid
clusterAssment = np.mat(np.zeros((numSamples, 2))) # 得到一个N*2的零矩阵
clusterChanged = True
## step 1: init centroids
centroids = initcentroids(dataSet, k) # 在样本集中随机选取k个样本点作为初始质心
# print('centroids')
# print(centroids)
while clusterChanged:
clusterChanged = False
## for each sample
for i in range(numSamples): # range
minDist = 100000.0
minIndex = 0
## for each centroid
# ## step 2: find the centroid who is closest
# #计算每个样本点与质点之间的距离，将其归内到距离最小的那一簇
for j in range(k):
distance = eucldistance(centroids[j, :], dataSet[i, :])
if distance < minDist:
minDist = distance
minIndex = j
## step 3: update its cluster
# k个簇里面与第i个样本距离最小的的标号和距离保存在clusterAssment中
# 若所有的样本不在变化，则退出while循环
if clusterAssment[i, 0] != minIndex:
clusterChanged = True
clusterAssment[i, :] = minIndex, minDist # 两个**表示的是minDist的平方
## step 4: update centroids
for j in range(k):
# clusterAssment[:,0].A==j是找出矩阵clusterAssment中第一列元素中等于j的行的下标，返回的是一个以array的列表，第一个array为等于j的下标
pointsInCluster = dataSet[np.nonzero(clusterAssment[:, 0].A == j)[0]] # 将dataSet矩阵中相对应的样本提取出来
centroids[j, :] = np.mean(pointsInCluster, axis=0) # 计算标注为j的所有样本的平均值
print('Congratulations, cluster complete!')
return centroids, clusterAssment
def kmeans_cel(x, cel_num, k, save_path):
print('x.shape:' + str(x.shape))
sample_num = int(x.shape[0]*x.shape[1])
fc1_w = x.reshape([sample_num])
print('x.reshape.shape:' + str(fc1_w.shape))
print('cel_num:' + str(cel_num))
print('k:' + str(k))
index_array = np.argsort(fc1_w)
fc1_w.sort()
num = int(np.ceil((sample_num)/cel_num))
a = np.zeros([num, 1])
for i in range(0, num-1):
a[i] = np.mean(fc1_w[i*cel_num:(i+1)*cel_num])
a[-1] = np.mean(fc1_w[(num-1)*cel_num:])
centroids, clusterAssment = kmeans(a, k) #调用KMeans文件中定义的kmeans方法。
clusterAssment = clusterAssment[:, 0]
mask = np.zeros([sample_num, 1])
for i in range(0, num-1):
for j in range(0, cel_num):
mask[index_array[i*cel_num+j]] = clusterAssment[i]
mask[(num-1)*cel_num:] = clusterAssment[-1]
print('mask.shape:' + str(mask.shape))
np.savez(save_path, fc1_w_clusterAssment=mask)
# show your cluster only available with 2-D data
# centroids为k个类别，其中保存着每个类别的质心
# clusterAssment为样本的标记，第一列为此样本的类别号，第二列为到此类别质心的距离
def showCluster(dataSet, k, centroids, clusterAssment):
numSamples, dim = dataSet.shape
mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
if k > len(mark):
print("Sorry!")
return 1
# draw all samples
for i in range(numSamples):
markIndex = int(clusterAssment[i, 0]) # 为样本指定颜色
plt.plot(dataSet[i], mark[markIndex])
mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
# draw the centroids
for i in range(k):
plt.plot(centroids[i], mark[i], markersize=12)
plt.show()