Using Scikit-Learn's PCA to reproduce the results of the Lindsay Smith PCA Tutorial Paper

pca_tutorial_lindsay_smith.py

import numpy as np
from numpy.linalg import inv
from scipy import linalg
from sklearn.decomposition import PCA
from sklearn import utils

X = np.array([[2.5, 2.4], [0.5, 0.7], [2.2, 2.9], [1.9, 2.2], [3.1, 3.0], [2.3, 2.7], [2, 1.6], [1, 1.1], [1.5, 1.6], [1.1, 0.9]])
mean_ = np.mean(X, axis=0)
X = X - mean_

pca = PCA(n_components=2)
pca.fit(X)
pca.components_
pca.transform(X)
print(X)
print('components = ', pca.components_)
print('================')
print(pca.transform(X))