能夠?qū)W習(xí)和掌握編程，最好的學(xué)習(xí)方式，就是去掌握基本的使用技巧，再多的概念意義，總歸都是為了使用服務(wù)的，K-means算法又叫K-均值算法，是非監(jiān)督學(xué)習(xí)中的聚類算法。主要有三個(gè)元素，其中N是元素個(gè)數(shù)，x表示元素，c(j)表示第j簇的質(zhì)心，下面就使用方式給大家簡(jiǎn)單介紹實(shí)例使用。
K-Means算法進(jìn)行聚類分析
km = KMeans(n_clusters = 3)
km.fit(X)
centers = km.cluster_centers_
print(centers)
三個(gè)簇的中心點(diǎn)坐標(biāo)為：
[[5.006 3.428 ]
[6.81276596 3.07446809]
[5.77358491 2.69245283]]
比較一下K-Means聚類結(jié)果和實(shí)際樣本之間的差別：
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算法實(shí)例擴(kuò)展內(nèi)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()
到此這篇關(guān)于python中K-means算法基礎(chǔ)知識(shí)點(diǎn)的文章就介紹到這了,更多相關(guān)python中K-means算法是什么內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
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