Chạy code trên python

Mình có đoạn code sau, mình muốn chạy nó trên python vậy mình phải làm gì cả nhà. Cảm ơn.
MNIST
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In [12]:

# %reset
import numpy as np 
from mnist import MNIST
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import normalize
display_network.py

In [14]:

from display_network import *

mndata = MNIST('../MNIST/')
mndata.load_testing()
X = mndata.test_images
X0 = np.asarray(X)[:1000,:]/256.0
X = X0

K = 10
kmeans = KMeans(n_clusters=K).fit(X)

pred_label = kmeans.predict(X)

In [4]:

print(type(kmeans.cluster_centers_.T))
print(kmeans.cluster_centers_.T.shape)
A = display_network(kmeans.cluster_centers_.T, K, 1)

f1 = plt.imshow(A, interpolation='nearest', cmap = "jet")
f1.axes.get_xaxis().set_visible(False)
f1.axes.get_yaxis().set_visible(False)
plt.show()
# plt.savefig('a1.png', bbox_inches='tight')


# a colormap and a normalization instance
cmap = plt.cm.jet
norm = plt.Normalize(vmin=A.min(), vmax=A.max())

# map the normalized data to colors
# image is now RGBA (512x512x4) 
image = cmap(norm(A))

import scipy.misc
scipy.misc.imsave('aa.png', image)
<type 'numpy.ndarray'>
(784, 10)

Chọn một vài ảnh từ mỗi cluster.
In [5]:

print(type(pred_label))
print(pred_label.shape)
print(type(X0))
<type 'numpy.ndarray'>
(1000,)
<type 'numpy.ndarray'>

In [6]:

N0 = 20;
X1 = np.zeros((N0*K, 784))
X2 = np.zeros((N0*K, 784))

for k in range(K):
    Xk = X0[pred_label == k, :]

    center_k = [kmeans.cluster_centers_[k]]
    neigh = NearestNeighbors(N0).fit(Xk)
    dist, nearest_id  = neigh.kneighbors(center_k, N0)
    
    X1[N0*k: N0*k + N0,:] = Xk[nearest_id, :]
    X2[N0*k: N0*k + N0,:] = Xk[:N0, :]

In [7]:

plt.axis('off')
A = display_network(X2.T, K, N0)
f2 = plt.imshow(A, interpolation='nearest' )
plt.gray()
plt.show()

# import scipy.misc
# scipy.misc.imsave('bb.png', A)


# plt.axis('off')
# A = display_network(X1.T, 10, N0)
# scipy.misc.imsave('cc.png', A)
# f2 = plt.imshow(A, interpolation='nearest' )
# plt.gray()

# plt.show()

Segmentation and Compression
In [8]:

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

img = mpimg.imread('girl3.jpg')
plt.imshow(img)
imgplot = plt.imshow(img)
plt.axis('off')
plt.show()

In [9]:

X = img.reshape((img.shape[0]*img.shape[1], img.shape[2]))

Số lượng cluster khác nhau.
In [10]:

for K in [2, 5, 10, 15, 20]:
    kmeans = KMeans(n_clusters=K).fit(X)
    label = kmeans.predict(X)

    img4 = np.zeros_like(X)
    # replace each pixel by its center
    for k in range(K):
        img4[label == k] = kmeans.cluster_centers_[k]
    # reshape and display output image
    img5 = img4.reshape((img.shape[0], img.shape[1], img.shape[2]))
    plt.imshow(img5, interpolation='nearest')
    plt.axis('off')
    plt.show()