打印每个集群的图像

Question

我使用 sklearn KMeans 来形成图像集群，我在打印每个集群的图像时遇到了困难。

1. 我有一个 np 数组维度：(10000, 100, 100, 3)
2. 然后我将图像展平，使每一行都呈现一个图像。训练维度：(10000, 30000)

3. 我应用了 KMeans。

   from scipy import ndimage
   
   from sklearn.cluster import KMeans
   
   kmeans = KMeans(n_clusters=10, random_state=0)
   
   clusters = kmeans.fit_predict(train)
   
   centers = kmeans.cluster_centers_
   

在此之后我想打印每个集群的图像，

Answer 1

对于十个集群，您将获得十个集群中心。您现在可以打印它们，也可以将它们可视化 - 这是我假设您想要做的。

import numpy as np
import matplotlib.pyplot as plt

#fake centers 
centers = np.random.random((10,100,100,3))

#print centers
for ci in centers:
    print(ci)

#visualize centers:
for ci in centers: 
    plt.imshow(ci)
    plt.show()

编辑：我知道您不仅希望可视化中心，还希望可视化每个集群中的其他成员。

您可以对单个随机成员执行以下操作：

from scipy import ndimage
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt
import random

#PARAMS
n_clusters=10  

#fake train data
original_train = np.random.random((100, 100, 100, 3)) #100 images of each 100 px,py and RGB 

n,x,y,c = original_train.shape

flat_train = original_train.reshape((n,x*y*c))

kmeans = KMeans(n_clusters, random_state=0)

clusters = kmeans.fit_predict(flat_train)

centers = kmeans.cluster_centers_

#visualize centers:
for ci in centers: 
    plt.imshow(ci.reshape(x,y,c))
    plt.show()

#visualize other members
for cluster in np.arange(n_clusters):

    cluster_member_indices = np.where(clusters == cluster)[0]
    print("There are %s members in cluster %s" % (len(cluster_member_indices), cluster))

    #pick a random member
    random_member = random.choice(cluster_member_indices)
    plt.imshow(original_train[random_member,:,:,:])
    plt.show()