KMeans聚类算法

一、fit_predict()

 def fit_predict(self, X, y=None):
        """Compute cluster centers and predict cluster index for each sample.

        Convenience method; equivalent to calling fit(X) followed by
        predict(X).
        """
        return self.fit(X).labels_

def fit_predict(data)
                       data=文件所有数据

返回每个数据对应的标签，并将标签值对应到相应的簇。

    def fit(self, X, y=None):
        """Compute k-means clustering.

计算簇中心。

 def predict(self, X):
        """Predict the closest cluster each sample in X belongs to.

predict:指定x中每个点所属于的簇的位置。

***KMeans计算方法：

默认采用欧氏距离：

double getDistXY(const Tuple& t1, const Tuple& t2)   
{  
    double sum = 0;  
    for(int i=1; i<=dimNum; ++i)  
    {  
        sum += (t1[i]-t2[i]) * (t1[i]-t2[i]);  
    }  
    return sqrt(sum);  
} 

kMeans代码解析笔记

import numpy as np
from sklearn.cluster import KMeans

def loadData(fileName):
    fr=open(fileName,'r+')
    lines=fr.readlines()
    cityName=[]
    data=[]
    for line in lines:
        items=line.strip().split(',')
        cityName.append(items[0])
        data.append([float(items[i]) for i in range(1,len(items))])#添加成为二维数组。里面一层数组外面再包一层括号
    return data,cityName

if __name__=="__main__":
    #加载文件数据代码引用函数
    data,cityName=loadData('city.txt')
    km=KMeans(n_clusters=4)
    label=km.fit_predict(data)#返回的label更像是city的分身，而这些分身经过计算已经分类到四个簇中，本身值等于簇的值。
    expenses=np.sum(km.cluster_centers_,axis=1)#横向计算每个城市的总开销，并把它归类到相应的簇里面。然后对每个簇进行求平均数。得到expense[i]i从0到3
    print(expenses)
    CityCluster=[[],[],[],[]]
    for i in range(len(cityName)):
        CityCluster[label[i]].append(cityName[i])
    for i in range(4):
        print('Expense:%.2f'%expenses[i])
        print(CityCluster[i])