3.k均值算法
2021-02-15 13:17
标签:聚类 bsp plot model import turn lse shape datasets 用sklearn.cluster.KMeans,鸢尾花花瓣长度数据做聚类,并用散点图显示 3.k均值算法 标签:聚类 bsp plot model import turn lse shape datasets 原文地址:https://www.cnblogs.com/xwc520/p/12716119.html自主编写K-means算法 ,以鸢尾花花瓣长度数据做聚类,并用散点图显示。
from numpy import *
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_iris
data= load_iris()
iris_l = data.data[:,1:2]
y= len(iris_l)#150
m = iris_l.shape[1]#1
k = 3
dist = np.zeros([y,k+1])
#center
def initcent(x, k):
center = x[:k, :]
return center
#oushijuli
def nearest(x, center):
a=[]
for j in range(k):
a.append(abs(x-center[j,:]))
# print(a)
return a
#julei
def xclassify(x,dist, center):
for i in range(x.shape[0]):
dist[i,:k]=nearest(x[i,:],center)
dist[i, k] = np.argmin(dist[i,:k])
return dist
def kcmean(x, dist, center, k):
centerNew = np.zeros([k,m])
for c in range(k):
q = np.where(dist[:,k] == c)
n = np.mean(x[q])
if centerNew[c] != n:
centerNew[c] = n
return np.array(centerNew)
center = initcent(iris_l,k)
while True:
xclas = xclassify(iris_l, dist, center)
centerNew=kcmean(iris_l, xclas, center, k)
if all(center == centerNew):
break
else:
center = centerNew
a = iris_l.flatten()
plt.scatter(a, a, c=array(xclas[:,k]), s=50, cmap=‘rainbow‘, marker=‘p‘, alpha=0.5)
plt.show()from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
iris = load_iris()
data = iris[‘data‘]
long = data[:,1]
long = long.reshape(-1,1)
model = KMeans(n_clusters=3).fit(long)
pre = model.predict(long)
center = model.cluster_centers_
# print(long[:,0])
plt.scatter(long[:,0], long[:,0], c=pre, s=50, cmap=‘rainbow‘, marker=‘p‘, alpha=0.5)
plt.show()
鸢尾花完整数据做聚类并用散点图显示.from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
iris = load_iris()
data = iris[‘data‘]
model = KMeans(n_clusters=3).fit(data)
pre = model.predict(data)
center = model.cluster_centers_
plt.scatter(data[:,0], data[:,1], c=pre, s=50, cmap=‘rainbow‘, marker=‘p‘, alpha=0.5)
plt.show()