t-SNE Example 1

tSNE_example_1.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn import manifold, datasets
#Prepare the data
digits = datasets.load_digits(n_class=6)
X, y = digits.data, digits.target
n_samples, n_features = X.shape
n = 20  
img = np.zeros((10 * n, 10 * n))
for i in range(n):
    ix = 10 * i + 1
    for j in range(n):
        iy = 10 * j + 1
        img[ix:ix + 8, iy:iy + 8] = X[i * n + j].reshape((8, 8))
plt.figure(figsize=(8, 8))
plt.imshow(img, cmap=plt.cm.binary)
plt.xticks([])
plt.yticks([])
plt.show()

#t-SNE
X_tsne = manifold.TSNE(n_components=2, init='random', random_state=5, verbose=1).fit_transform(X)

#Data Visualization
x_min, x_max = X_tsne.min(0), X_tsne.max(0)
X_norm = (X_tsne - x_min) / (x_max - x_min)  #Normalize
plt.figure(figsize=(8, 8))
for i in range(X_norm.shape[0]):
    plt.text(X_norm[i, 0], X_norm[i, 1], str(y[i]), color=plt.cm.Set1(y[i]), 
             fontdict={'weight': 'bold', 'size': 9})
plt.xticks([])
plt.yticks([])
plt.show()