吴裕雄 python 机器学习——K均值聚类KMeans模型

import numpy as np

import matplotlib.pyplot as plt

from sklearn import  cluster

from sklearn.metrics import adjusted_rand_score

from sklearn.datasets.samples_generator import make_blobs

def create_data(centers,num=100,std=0.7):

    X, labels_true = make_blobs(n_samples=num, centers=centers, cluster_std=std)

    return  X,labels_true

# 用于产生聚类的中心点

centers=[[1,1],[2,2],[1,2],[10,20]]

# 产生用于聚类的数据集

X,labels_true=create_data(centers,1000,0.5)

#K-MEANS聚类模型

def test_Kmeans(*data):

    X,labels_true=data

    clst=cluster.KMeans()

    clst.fit(X)

    predicted_labels=clst.predict(X)

    print("ARI:%s"% adjusted_rand_score(labels_true,predicted_labels))

    print("Sum center distance %s"%clst.inertia_)

# 用于产生聚类的中心点

centers=[[1,1],[2,2],[1,2],[10,20]]

# 产生用于聚类的数据集

X,labels_true=create_data(centers,1000,0.5)

#  调用 test_Kmeans 函数

test_Kmeans(X,labels_true)

def test_Kmeans_nclusters(*data):

    '''

    测试 KMeans 的聚类结果随 n_clusters 参数的影响

    '''

    X,labels_true=data

    nums=range(1,50)

    ARIs=[]

    Distances=[]

    for num in nums:

        clst=cluster.KMeans(n_clusters=num)

        clst.fit(X)

        predicted_labels=clst.predict(X)

        ARIs.append(adjusted_rand_score(labels_true,predicted_labels))

        Distances.append(clst.inertia_)

    ## 绘图

    fig=plt.figure()

    ax=fig.add_subplot(1,2,1)

    ax.plot(nums,ARIs,marker="+")

    ax.set_xlabel("n_clusters")

    ax.set_ylabel("ARI")

    ax=fig.add_subplot(1,2,2)

    ax.plot(nums,Distances,marker='o')

    ax.set_xlabel("n_clusters")

    ax.set_ylabel("inertia_")

    fig.suptitle("KMeans")

    plt.show()

test_Kmeans_nclusters(X,labels_true) #  调用 test_Kmeans_nclusters 函数

def test_Kmeans_n_init(*data):

    '''

    测试 KMeans 的聚类结果随 n_init 和 init  参数的影响

    '''

    X,labels_true=data

    nums=range(1,50)

    ## 绘图

    fig=plt.figure()

    ARIs_k=[]

    Distances_k=[]

    ARIs_r=[]

    Distances_r=[]

    for num in nums:

            clst=cluster.KMeans(n_init=num,init='k-means++')

            clst.fit(X)

            predicted_labels=clst.predict(X)

            ARIs_k.append(adjusted_rand_score(labels_true,predicted_labels))

            Distances_k.append(clst.inertia_)

            clst=cluster.KMeans(n_init=num,init='random')

            clst.fit(X)

            predicted_labels=clst.predict(X)

            ARIs_r.append(adjusted_rand_score(labels_true,predicted_labels))

            Distances_r.append(clst.inertia_)

    ax=fig.add_subplot(1,2,1)

    ax.plot(nums,ARIs_k,marker="+",label="k-means++")

    ax.plot(nums,ARIs_r,marker="+",label="random")

    ax.set_xlabel("n_init")

    ax.set_ylabel("ARI")

    ax.set_ylim(0,1)

    ax.legend(loc='best')

    ax=fig.add_subplot(1,2,2)

    ax.plot(nums,Distances_k,marker='o',label="k-means++")

    ax.plot(nums,Distances_r,marker='o',label="random")

    ax.set_xlabel("n_init")

    ax.set_ylabel("inertia_")

    ax.legend(loc='best')

    fig.suptitle("KMeans")

    plt.show()

test_Kmeans_n_init(X,labels_true) #  调用 test_Kmeans_n_init 函数