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Book4_Power-of-Matrix/Book4_Ch13_Python_Codes/Bk4_Ch13_02.py
2022-07-16 22:00:56 -04:00

97 lines
2.2 KiB
Python

###############
# Authored by Weisheng Jiang
# Book 4 | From Basic Arithmetic to Machine Learning
# Published and copyrighted by Tsinghua University Press
# Beijing, China, 2022
###############
# Bk4_Ch13_02.py
import numpy as np
import matplotlib.pyplot as plt
def visualize(X_circle,X_vec,title_txt):
fig, ax = plt.subplots()
plt.plot(X_circle[0,:], X_circle[1,:],'k',
linestyle = '--',
linewidth = 0.5)
plt.quiver(0,0,X_vec[0,0],X_vec[1,0],
angles='xy', scale_units='xy',scale=1,
color = [0, 0.4392, 0.7529])
plt.quiver(0,0,X_vec[0,1],X_vec[1,1],
angles='xy', scale_units='xy',scale=1,
color = [1,0,0])
plt.axvline(x=0, color= 'k', zorder=0)
plt.axhline(y=0, color= 'k', zorder=0)
plt.ylabel('$x_2$')
plt.xlabel('$x_1$')
ax.set_aspect(1)
ax.set_xlim([-2.5, 2.5])
ax.set_ylim([-2.5, 2.5])
ax.grid(linestyle='--', linewidth=0.25, color=[0.5,0.5,0.5])
ax.set_xticks(np.linspace(-2,2,5));
ax.set_yticks(np.linspace(-2,2,5));
plt.title(title_txt)
plt.show()
theta = np.linspace(0, 2*np.pi, 100)
circle_x1 = np.cos(theta)
circle_x2 = np.sin(theta)
V_vec = np.array([[np.sqrt(2)/2, -np.sqrt(2)/2],
[np.sqrt(2)/2, np.sqrt(2)/2]])
X_circle = np.array([circle_x1, circle_x2])
# plot original circle and two vectors
visualize(X_circle,V_vec,'Original')
A = np.array([[1.25, -0.75],
[-0.75, 1.25]])
# plot the transformation of A
visualize(A@X_circle, A@V_vec,'$A$')
#%% Eigen deomposition
# A = V @ D @ V.T
lambdas, V = np.linalg.eig(A)
D = np.diag(np.flip(lambdas))
V = V.T # reverse the order
print('=== LAMBDA ===')
print(D)
print('=== V ===')
print(V)
# plot the transformation of V.T
visualize(V.T@X_circle, V.T@V_vec,'$V^T$')
# plot the transformation of D @ V.T
visualize(D@V.T@X_circle, D@V.T@V_vec,'$\u039BV^T$')
# plot the transformation of V @ D @ V.T
visualize(V@D@V.T@X_circle, V@D@V.T@V_vec,'$V\u039BV^T$')
# plot the transformation of A
visualize(A@X_circle, A@V_vec,'$A$')