python中K-means算法基础知识点

Kamilia ·

更新时间:2024-11-13

· 726 次阅读

能够学习和掌握编程，最好的学习方式，就是去掌握基本的使用技巧，再多的概念意义，总归都是为了使用服务的，K-means算法又叫K-均值算法，是非监督学习中的聚类算法。主要有三个元素，其中N是元素个数，x表示元素，c(j)表示第j簇的质心，下面就使用方式给大家简单介绍实例使用。

K-Means算法进行聚类分析


km = KMeans(n_clusters = 3)
km.fit(X)
centers = km.cluster_centers_
print(centers)

三个簇的中心点坐标为：

[[5.006 3.428 ]

[6.81276596 3.07446809]

[5.77358491 2.69245283]]

比较一下K-Means聚类结果和实际样本之间的差别：


predicted_labels = km.labels_
fig, axes = plt.subplots(1, 2, figsize=(16,8))
axes[0].scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, 
        edgecolor='k', s=150)
axes[1].scatter(X[:, 0], X[:, 1], c=predicted_labels, cmap=plt.cm.Set1,
        edgecolor='k', s=150)
axes[0].set_xlabel('Sepal length', fontsize=16)
axes[0].set_ylabel('Sepal width', fontsize=16)
axes[1].set_xlabel('Sepal length', fontsize=16)
axes[1].set_ylabel('Sepal width', fontsize=16)
axes[0].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20)
axes[1].tick_params(direction='in', length=10, width=5, colors='k', labelsize=20)
axes[0].set_title('Actual', fontsize=18)
axes[1].set_title('Predicted', fontsize=18)

k-means算法实例扩展内容：


# -*- coding: utf-8 -*- 
"""Excercise 9.4"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import random
data = pd.read_csv(filepath_or_buffer = '../dataset/watermelon4.0.csv', sep = ',')[["密度","含糖率"]].values
########################################## K-means ####################################### 
k = int(sys.argv[1])
#Randomly choose k samples from data as mean vectors
mean_vectors = random.sample(data,k)
def dist(p1,p2):
 return np.sqrt(sum((p1-p2)*(p1-p2)))
while True:
 print mean_vectors
 clusters = map ((lambda x:[x]), mean_vectors) 
 for sample in data:
  distances = map((lambda m: dist(sample,m)), mean_vectors) 
  min_index = distances.index(min(distances))
  clusters[min_index].append(sample)
 new_mean_vectors = []
 for c,v in zip(clusters,mean_vectors):
  new_mean_vector = sum(c)/len(c)
  #If the difference betweenthe new mean vector and the old mean vector is less than 0.0001
  #then do not updata the mean vector
  if all(np.divide((new_mean_vector-v),v) < np.array([0.0001,0.0001]) ):
   new_mean_vectors.append(v) 
  else:
   new_mean_vectors.append(new_mean_vector) 
 if np.array_equal(mean_vectors,new_mean_vectors):
  break
 else:
  mean_vectors = new_mean_vectors 
#Show the clustering result
total_colors = ['r','y','g','b','c','m','k']
colors = random.sample(total_colors,k)
for cluster,color in zip(clusters,colors):
 density = map(lambda arr:arr[0],cluster)
 sugar_content = map(lambda arr:arr[1],cluster)
 plt.scatter(density,sugar_content,c = color)
plt.show()

到此这篇关于python中K-means算法基础知识点的文章就介绍到这了,更多相关python中K-means算法是什么内容请搜索软件开发网以前的文章或继续浏览下面的相关文章希望大家以后多多支持软件开发网！

您可能感兴趣的文章:python实现爬山算法的思路详解爬山算法简介和Python实现实例python 图像增强算法实现详解python 实现Harris角点检测算法Python实现迪杰斯特拉算法并生成最短路径的示例代码python里反向传播算法详解python动态规划算法实例详解python的数学算法函数及公式用法Python实现冒泡排序算法的完整实例python 实现非极大值抑制算法（Non-maximum suppression, NMS）Python实现随机爬山算法

k-means算法 k-means Python

1024 个赞