machinelearning_notebook/1_kmeans/k-means.py

# -*- coding: utf-8 -*-
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# # k-means demo

# +
# This line configures matplotlib to show figures embedded in the notebook, 
# instead of opening a new window for each figure. More about that later. 
# If you are using an old version of IPython, try using '%pylab inline' instead.
# %matplotlib inline

# import librarys
from numpy import *
import matplotlib.pyplot as plt
import pandas as pd

# Load dataset
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pd.read_csv("iris.csv", header=0, index_col=0)
dataset.head()

# -

#对类别进行编码，3个类别分别赋值0，1，2
dataset['class'][dataset['class']=='Iris-setosa']=0
dataset['class'][dataset['class']=='Iris-versicolor']=1
dataset['class'][dataset['class']=='Iris-virginica']=2


def originalDatashow(dataSet):
    #绘制原始的样本点
    num,dim=shape(dataSet)
    marksamples=['ob'] #样本图形标记
    for i in range(num):
        plt.plot(datamat.iat[i,0],datamat.iat[i,1],marksamples[0],markersize=5)
    plt.title('original dataset')
    plt.xlabel('sepal length')
    plt.ylabel('sepal width') 
    plt.show()
    

# + {"scrolled": true}
#获取样本数据
datamat = dataset.loc[:, ['sepal-length', 'sepal-width']]
# 真实的标签
labels = dataset.loc[:, ['class']]
#原始数据显示
originalDatashow(datamat)


# -

def randChosenCent(dataSet,k):
    """初始化聚类中心：通过在区间范围随机产生的值作为新的中心点"""

    # 样本数
    m=shape(dataSet)[0]
    # 初始化列表
    centroidsIndex=[]
    #生成类似于样本索引的列表
    dataIndex=list(range(m))
    for i in range(k):
        #生成随机数
        randIndex=random.randint(0,len(dataIndex))
        #将随机产生的样本的索引放入centroidsIndex
        centroidsIndex.append(dataIndex[randIndex])
        #删除已经被抽中的样本
        del dataIndex[randIndex]
    #根据索引获取样本
    centroids = dataSet.iloc[centroidsIndex]
    return mat(centroids)

# +

def distEclud(vecA, vecB):
    """算距离, 两个向量间欧式距离"""
    return sqrt(sum(power(vecA - vecB, 2))) #la.norm(vecA-vecB)


def kMeans(dataSet, k):
    # 样本总数
    m = shape(dataSet)[0]
    # 分配样本到最近的簇：存[簇序号,距离的平方] (m行 x 2 列)
    clusterAssment = mat(zeros((m, 2)))

    # step1: 通过随机产生的样本点初始化聚类中心
    centroids = randChosenCent(dataSet, k)
    print('最初的中心=', centroids)

    # 标志位，如果迭代前后样本分类发生变化值为Tree，否则为False
    clusterChanged = True
    # 查看迭代次数
    iterTime = 0
    
    # 所有样本分配结果不再改变，迭代终止
    while clusterChanged:
        clusterChanged = False
        
        # step2:分配到最近的聚类中心对应的簇中
        for i in range(m):
            # 初始定义距离为无穷大
            minDist = inf;
            # 初始化索引值
            minIndex = -1
            # 计算每个样本与k个中心点距离
            for j in range(k):
                # 计算第i个样本到第j个中心点的距离
                distJI = distEclud(centroids[j, :], dataSet.values[i, :])
                # 判断距离是否为最小
                if distJI < minDist:
                    # 更新获取到最小距离
                    minDist = distJI
                    # 获取对应的簇序号
                    minIndex = j
            # 样本上次分配结果跟本次不一样，标志位clusterChanged置True
            if clusterAssment[i, 0] != minIndex:
                clusterChanged = True
            clusterAssment[i, :] = minIndex, minDist ** 2  # 分配样本到最近的簇
            
        iterTime += 1
        sse = sum(clusterAssment[:, 1])
        print('the SSE of %d' % iterTime + 'th iteration is %f' % sse)
        
        # step3:更新聚类中心
        for cent in range(k):  # 样本分配结束后，重新计算聚类中心
            # 获取该簇所有的样本点
            ptsInClust = dataSet.iloc[nonzero(clusterAssment[:, 0].A == cent)[0]]
            # 更新聚类中心：axis=0沿列方向求均值。
            centroids[cent, :] = mean(ptsInClust, axis=0)
    return centroids, clusterAssment

# -

# 进行k-means聚类
k = 3  # 用户定义聚类数
mycentroids, clusterAssment = kMeans(datamat, k)

# +
def datashow(dataSet, k, centroids, clusterAssment):  # 二维空间显示聚类结果
    from matplotlib import pyplot as plt
    num, dim = shape(dataSet)  # 样本数num ,维数dim

    if dim != 2:
        print('sorry,the dimension of your dataset is not 2!')
        return 1
    marksamples = ['or', 'ob', 'og', 'ok', '^r', '^b', '<g']  # 样本图形标记
    if k > len(marksamples):
        print('sorry,your k is too large,please add length of the marksample!')
        return 1
        # 绘所有样本
    for i in range(num):
        markindex = int(clusterAssment[i, 0])  # 矩阵形式转为int值, 簇序号
        # 特征维对应坐标轴x,y；样本图形标记及大小
        plt.plot(dataSet.iat[i, 0], dataSet.iat[i, 1], marksamples[markindex], markersize=6)

    # 绘中心点
    markcentroids = ['o', '*', '^']  # 聚类中心图形标记
    label = ['0', '1', '2']
    c = ['yellow', 'pink', 'red']
    for i in range(k):
        plt.plot(centroids[i, 0], centroids[i, 1], markcentroids[i], markersize=15, label=label[i], c=c[i])
        plt.legend(loc='upper left')
    plt.xlabel('sepal length')
    plt.ylabel('sepal width')

    plt.title('k-means cluster result')  # 标题
    plt.show()
    
    
# 画出实际图像
def trgartshow(dataSet, k, labels):
    from matplotlib import pyplot as plt

    num, dim = shape(dataSet)
    label = ['0', '1', '2']
    marksamples = ['ob', 'or', 'og', 'ok', '^r', '^b', '<g']
    # 通过循环的方式，完成分组散点图的绘制
    for i in range(num):
        plt.plot(datamat.iat[i, 0], datamat.iat[i, 1], marksamples[int(labels.iat[i, 0])], markersize=6)
    for i in range(0, num, 50):
        plt.plot(datamat.iat[i, 0], datamat.iat[i, 1], marksamples[int(labels.iat[i, 0])], markersize=6,
                 label=label[int(labels.iat[i, 0])])
    plt.legend(loc='upper left')
    
    # 添加轴标签和标题
    plt.xlabel('sepal length')
    plt.ylabel('sepal width')
    plt.title('iris true result')  # 标题

    # 显示图形
    plt.show()
    # label=labels.iat[i,0]
# -

# 绘图显示
datashow(datamat, k, mycentroids, clusterAssment)
trgartshow(datamat, 3, labels)