import numpy as np
data = 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]])
x1 = sum(data[:, 0]) / 10.0
y1 = sum(data[:, 1]) / 10.0
DataAdjust = data - [x1, y1]
cov1 = np.array(np.cov(DataAdjust[:, 0], data[:, 1]))
eig1, eig2 = np.linalg.eig(cov1)
a = eig2[:, 0] if eig1[0] > eig1[1] else eig2[:, 1]
b = a.T
res = np.dot(DataAdjust, a)
print(res)
