note from Stanford NLP lecture

svd_example.py

#written in python 3.5
import numpy as np
import matplotlib.pyplot as plt
la = np.linalg
words = ['I', 'like', 'enjoy', 'deep', 'learning', 'NLP', 'flying', '.']

X = np.array([[0,2,1,0,0,0,0,0],
              [2,0,0,1,0,1,0,0],
              [1,0,0,0,0,0,1,0],
              [0,1,0,0,1,0,0,0],
              [0,0,0,1,0,0,0,1],
              [0,1,0,0,0,0,0,1],
              [0,0,1,0,0,0,0,1],
              [0,0,0,0,1,1,1,0]])

#U for left vector, s for diagonal matrix
#use svd to reduce dimensions
U, s, Vh = la.svd(X, full_matrices=False)

for i in range(len(words)):
    #use the first and second element of each word vector to represent X and Y position
    #for making sure that we picked up the most important information of original vector
    plt.text(U[i,0], U[i,1], words[i])
plt.show()