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【人工智能】机器学习之聚类算法Kmeans及其应用,调用sklearn中聚类算法以及手动实现Kmeans算法。

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【人工智能】机器学习之聚类算法Kmeans及其应用,调用sklearn中聚类算法以及手动实现Kmeans算法。

 

南蓬幽

 

2022年07月26日 09:47·  阅读 12

 

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实现Kmeans算法实现聚类

 

要求: 1、根据算法流程,手动实现Kmeans算法; 2、调用sklearn中聚类算法,对给定数据集进行聚类分析; 3、对比上述2中Kmeans算法的聚类效果。

 

读取文件

 

def loadFile(path):
    dataList = []
    #打开文件:以二进制读模式、utf-8格式的编码方式                                                                                                打开
    fr = open(path,"r",encoding='UTF-8')
    record = fr.read()
    fr.close
    #按照行转换为一维表即包含各行作为元素的列表,分隔符有'\r', '\r
', 
'
    recordList = record.splitlines()
    #逐行遍历:行内字段按'\t'分隔符分隔,转换为列表
    for line in recordList:
         if line.strip():
             dataList .append(list(map(float, line.split('\t'))))
    #返回转换后的矩阵
    recordmat = np.mat(dataList )
    return recordmat

 

手动实现Kmeans算法

 

def kMeans(dataset, k):
    m = np.shape(dataset)[0]
    ClustDist = np.mat(np.zeros((m, 2)))
    cents = randCents(dataset, k)
    clusterChanged = True
    # 循环迭代,得到最近的聚类中心
    while clusterChanged:
        clusterChanged = False
        for i in range(m):
            DistList = [distEclud(dataset[i, :], cents[jk,:]) for jk in range(k)]
            minDist = min(DistList)
            minIndex = DistList.index(minDist)
            if ClustDist[i, 0] != minIndex:
                clusterChanged = True
            ClustDist[i, :] = minIndex, minDist
        # 更新聚类
        for cent in range(k):
            ptsInClust = dataset[np.nonzero(ClustDist[:, 0].A == cent)[0]]
            # 更新聚类中心cents,axis=0按列求均值
            cents[cent, :] = np.mean(ptsInClust, axis=0)
    # 返回聚类中心和聚类分配矩阵
    return cents, ClustDist

 

处理数据

 

path_file = "TESTDATA.TXT"
recordMat = loadFile(path_file)
k = 4
cents, distMat = kMeans(recordMat, k)

 

绘制数据散点图

 

plt.subplot(311)
plt.grid(True)# 生成网格
for indx in range(len(distMat)):
    if distMat[indx, 0] == 0:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='red', marker='o')
    if distMat[indx, 0] == 1:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='blue', marker='o')
    if distMat[indx, 0] == 2:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='cyan', marker='o')
    if distMat[indx, 0] == 3:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='green', marker='o')
    #if distMat[indx, 0] == 4:
        #plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='black', marker='o')

 

绘制聚类中心

 

x = [cents[i,0] for i in range(k)]
y = [cents[i,1] for i in range(k)]
plt.scatter(x, y, s = 80, c='yellow', marker='o')
plt.title('Kmeans')

 

调用sklearn中聚类算法

 

from sklearn.cluster import KMeans
X = np.array(recordMat) # 生成初始聚类数据
#kmeans_model = KMeans(n_clusters=k, init='k-means++')  # 聚类模型
kmeans_model = KMeans(n_clusters=k, init='random')  # 聚类模型
kmeans_model.fit(X)  # 训练聚类模型

 

绘制k-Means聚类结果

 

# plt.figure()# 创建窗口
plt.subplot(312)
plt.axis([np.min(X[:,0])-1, np.max(X[:,0]+1), np.min(X[:,1])-1, np.max(X[:,1])+1])# 坐标轴
plt.grid(True)# 生成网格
colors = ['r', 'g', 'b','c'] # 聚类颜色
markers = ['o', 's', 'D', '+'] # 聚类标志
for i, l in enumerate(kmeans_model.labels_):
    plt.plot(X[i][0], X[i][1], color=colors[l],marker=markers[l],ls='None')
    plt.title('K = %s,random' %(k))

 

对比效果:

 

 

整合代码:

 

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
def loadFile(path):
    dataList = []
    #打开文件:以二进制读模式、utf-8格式的编码方式                                                                                                打开
    fr = open(path,"r",encoding='UTF-8')
    record = fr.read()
    fr.close
    #按照行转换为一维表即包含各行作为元素的列表,分隔符有'\r', '\r
', 
'
    recordList = record.splitlines()
    #逐行遍历:行内字段按'\t'分隔符分隔,转换为列表
    for line in recordList:
         if line.strip():
             dataList .append(list(map(float, line.split('\t'))))
    #返回转换后的矩阵
    recordmat = np.mat(dataList )
    return recordmat
def distEclud(vecA, vecB):
    return np.linalg.norm(vecA-vecB, ord=2)
def randCents(dataSet, k):
    n = np.shape(dataSet)[1]
    cents = np.mat(np.zeros((k,n)))
    for j in range(n):
        #质心必须在数据集范围内,也就是在min到max之间
        minCol = min(dataSet[:,j])
        maxCol = max(dataSet[:,j])
        #利用随机函数生成0到1.0之间的随机数
        cents [:,j] = np.mat(minCol + float(maxCol - minCol) * np.random.rand(k,1))
    return cents
def kMeans(dataset, k):
    m = np.shape(dataset)[0]
    ClustDist = np.mat(np.zeros((m, 2)))
    cents = randCents(dataset, k)
    clusterChanged = True
    # 循环迭代,得到最近的聚类中心
    while clusterChanged:
        clusterChanged = False
        for i in range(m):
            DistList = [distEclud(dataset[i, :], cents[jk,:]) for jk in range(k)]
            minDist = min(DistList)
            minIndex = DistList.index(minDist)
            if ClustDist[i, 0] != minIndex:
                clusterChanged = True
            ClustDist[i, :] = minIndex, minDist
        # 更新聚类
        for cent in range(k):
            ptsInClust = dataset[np.nonzero(ClustDist[:, 0].A == cent)[0]]
            # 更新聚类中心cents,axis=0按列求均值
            cents[cent, :] = np.mean(ptsInClust, axis=0)
    # 返回聚类中心和聚类分配矩阵
    return cents, ClustDist
path_file = "TESTDATA.TXT"
recordMat = loadFile(path_file)
k = 4
cents, distMat = kMeans(recordMat, k)
# 绘制数据散点图
plt.subplot(311)
plt.grid(True)# 生成网格
for indx in range(len(distMat)):
    if distMat[indx, 0] == 0:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='red', marker='o')
    if distMat[indx, 0] == 1:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='blue', marker='o')
    if distMat[indx, 0] == 2:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='cyan', marker='o')
    if distMat[indx, 0] == 3:
        plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='green', marker='o')
    #if distMat[indx, 0] == 4:
        #plt.scatter(recordMat[indx, 0], recordMat[indx, 1], c='black', marker='o')
# 绘制聚类中心
x = [cents[i,0] for i in range(k)]
y = [cents[i,1] for i in range(k)]
plt.scatter(x, y, s = 80, c='yellow', marker='o')
plt.title('Kmeans')

X = np.array(recordMat) # 生成初始聚类数据
# plt.figure()# 创建窗口
plt.subplot(312)
plt.axis([np.min(X[:,0])-1, np.max(X[:,0]+1), np.min(X[:,1])-1, np.max(X[:,1])+1])# 坐标轴
plt.grid(True)# 生成网格
colors = ['r', 'g', 'b','c'] # 聚类颜色
markers = ['o', 's', 'D', '+'] # 聚类标志
#kmeans_model = KMeans(n_clusters=k, init='k-means++')  # 聚类模型
kmeans_model = KMeans(n_clusters=k, init='random')  # 聚类模型
kmeans_model.fit(X)  # 训练聚类模型
# 绘制k-Means聚类结果
for i, l in enumerate(kmeans_model.labels_):
    plt.plot(X[i][0], X[i][1], color=colors[l],marker=markers[l],ls='None')
    plt.title('K = %s,random' %(k))
X = np.array(recordMat) # 生成初始聚类数据
# plt.figure()# 创建窗口
plt.subplot(313)
plt.axis([np.min(X[:,0])-1, np.max(X[:,0]+1), np.min(X[:,1])-1, np.max(X[:,1])+1])# 坐标轴
plt.grid(True)# 生成网格
colors = ['r', 'g', 'b','c'] # 聚类颜色
markers = ['o', 's', 'D', '+'] # 聚类标志
kmeans_model = KMeans(n_clusters=k, init='k-means++')  # 聚类模型
# kmeans_model = KMeans(n_clusters=k, init='random')  # 聚类模型
kmeans_model.fit(X)  # 训练聚类模型
# 绘制k-Means聚类结果
for i, l in enumerate(kmeans_model.labels_):
    plt.plot(X[i][0], X[i][1], color=colors[l],marker=markers[l],ls='None')
    plt.title('K = %s,k-means++' %(k))
plt.show()

 

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