import matplotlib.pyplot as plt
import seaborn as sns; sns.set()  # for plot styling
import numpy as np
from sklearn.cluster import KMeans


from sklearn.datasets import load_digits
digits = load_digits()
digits.data.shape


kmeans =                                       # Instantiation of KMeans
clusters =                                     # Run k-means on digits.data
kmeans.cluster_centers_.shape


# This part you shouldn't need to change- This shows the 10 cluster centers
# as 8x8 blocks


fig, ax = plt.subplots(2, 5, figsize=(8, 3))
centers = kmeans.cluster_centers_.reshape(10, 8, 8)
for axi, center in zip(ax.flat, centers):
    axi.set(xticks=[], yticks=[])
    axi.imshow(center, interpolation='nearest', cmap=plt.cm.binary)