text_plain_output_1.png
from sklearn.datasets import load_digits

from sklearn.preprocessing import minmax_scale

import numpy as np # linear algebra

digits = load_digits()

K = 10
X = digits['data']
feature_names = digits['feature_names']
X = minmax_scale(X, feature_range=(0, 1))
npixels = int(np.sqrt(X.shape[1]))
y = digits['target']
target_names = digits['target_names']
images = digits['images']
for i in range(1, 101):
    ax = plt.subplot(10, 10, i)
    ax.imshow(images[i], cmap='Greys')
    ax.axes.xaxis.set_visible(False)
    ax.axes.yaxis.set_visible(False)