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尝试动态创建图形

python-3.x python

ASH · 技术社区 · 5 年前

我可以手动创建一个带有5个质心的kmeans数据图表(代码如下)。

# computing K-Means with K = 5 (5 clusters)
centroids,_ = kmeans(data,5)
# assign each sample to a cluster
idx,_ = vq(data,centroids)

# some plotting using numpy's logical indexing
plot(data[idx==0,0],data[idx==0,1],'ob',
     data[idx==1,0],data[idx==1,1],'oy',
     data[idx==2,0],data[idx==2,1],'or',
     data[idx==3,0],data[idx==3,1],'og',
     data[idx==4,0],data[idx==4,1],'om')
plot(centroids[:,0],centroids[:,1],'sg',markersize=15)
show()

for i in range(2, 20):
   plot(data[idx==[i],0],data[idx==[i],1],'some_dynamic_color'
plot(centroids[:,0],centroids[:,1],'sg',markersize=15)
show()

最后,这里是我的数据数组,供参考。我甚至不确定这是否与眼前的问题有关。

array([[ 0.01160815,  0.28552583],
       [ 0.01495681,  0.24965798],
       [ 0.52218559,  0.26969486],
       [ 0.16408791,  0.30713289],
       [ 0.35037607,  0.28401598],
       [-0.32413957,  0.53144262],
       [ 0.10853278,  0.19756793],
       [ 0.08275109,  0.18140047],
       [-0.04350157,  0.26407197],
       [-0.04789838,  0.31644537],
       [-0.03852801,  0.21557165],
       [ 0.02213885,  0.20033466],
       [-0.80612714,  0.35888803],
       [-0.27971428,  0.3195602 ],
       [ 0.21359135,  0.14144335],
       [ 0.09936109,  0.22313638],
       [ 0.15504834,  0.17022939],
       [ 0.47012351,  0.41452523],
       [ 0.28616062,  0.23098198],
       [ 0.25941178,  0.14843141],
       [ 0.20049158,  0.23769455],
       [-0.19766684,  0.39110416],
       [-0.29619519,  0.53520109],
       [ 0.29319037,  0.23907492],
       [ 0.16644319,  0.18737667],
       [ 0.37407685,  0.22463339],
       [-0.34262982,  0.40264906],
       [ 0.52658291,  0.3542729 ],
       [ 0.5747167 ,  0.50042607],
       [ 0.15607962,  0.20861585],
       [-0.50769188,  0.34266008],
       [ 0.43373588,  0.22526141],
       [ 0.1624051 ,  0.29859298],
       [ 0.22789948,  0.20157262],
       [-0.1179015 ,  0.21471169],
       [ 0.26108742,  0.26604149],
       [ 0.10019146,  0.25547835],
       [ 0.18906467,  0.19078555],
       [-0.02575308,  0.2877592 ],
       [-0.45292564,  0.51866493],
       [ 0.11516754,  0.21504329],
       [ 0.10020043,  0.23943587],
       [ 0.21402611,  0.34297039],
       [ 0.24574342,  0.15734118],
       [ 0.58083355,  0.22886509],
       [ 0.33975699,  0.33309233],
       [ 0.19002609,  0.14372212],
       [ 0.35220577,  0.23879166],
       [ 0.27427999,  0.1529184 ],
       [ 0.06261825,  0.18908223],
       [ 0.25005859,  0.21363957],
       [ 0.1676683 ,  0.26111871],
       [ 0.14703364,  0.25532777],
       [ 0.26130579,  0.14012819],
       [-0.14897454,  0.23037735],
       [-0.26827493,  0.23193457],
       [ 0.51701526,  0.17887009],
       [-0.05870745,  0.18040883],
       [ 0.25651599,  0.227289  ],
       [ 0.06881783,  0.28114007],
       [ 0.43079653,  0.21510341]])

谢谢。

0 回复 | 直到 5 年前

shimo 5 年前

for循环的索引i应该在0到4之间(有5个质心)。

for i in range(0, 5):
   plot(data[idx==[i],0],data[idx==[i],1],'some_dynamic_color' ...

import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from sklearn.cluster import KMeans

data = np.array(#your data)

kmeans = KMeans(n_clusters=5)
kmeans.fit(data)
y_kmeans = kmeans.predict(data)
viridis = cm.get_cmap('viridis', 5)
for i in range(0, len(data)):
    plt.scatter(data[i,0], data[i,1], c=viridis(y_kmeans[i]), s= 50)
centers = kmeans.cluster_centers_
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)

https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html