The K-means algorithm is independently compiled, clustering is performed on the iris petal length data, and displayed with a scatterplot.
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()
Use sklearn.cluster.KMeans, iris petal length data for clustering, and display it with a scatterplot
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()
The complete data of iris flowers are clustered and displayed with a scatterplot.
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()
