PCA using Python

import os
import numpy as np
from sklearn.decomposition import TruncatedSVD
from utils.helper_03 import *

# set random-seed
np.random.seed(1)

# Generate data
x = np.arange(-0.5, 0.5, 0.01)
y = x + (np.random.rand(len(x))-0.5)*0.2
plt.figure(figsize=(5, 5))
plt.plot(x, y, 'o'); plt.grid();

target_arr = np.stack((x, y)).T  # (100x2)
u, s, vh = np.linalg.svd(target_arr, full_matrices=True)

first_component = v[:, 0]
second_component = v[:, 1]

axis1 = first_component[1]/first_component[0] * x
axis2 = second_component[1]/second_component[0] * x

plt.figure(figsize=(5, 5))
plt.plot(x, y, 'o');
plt.plot(x, axis1, label="1st component axis"); plt.plot(x, axis2, label="2nd component axis"); plt.legend();​