PCA学习

import numpy as np

mat = np.array([[2.5, 2.4],
                [0.5, 0.7],
                [2.2, 2.9],
                [1.9, 2.2],
                [3.1, 3],
                [2.3, 2.7],
                [2, 1.6],
                [1, 1.1],
                [1.5, 1.6],
                [1.1, 0.9]])
n = np.size(mat,1) #列 2列
l = np.size(mat,0) #行
avg = np.zeros([1,n]) # 行 列
for i in range(n):
    avg[0, i] = np.mean(mat[:, i])
for i in range(n):
    mat[:,i] = mat[:,i] - avg[0,i]
todd=np.cov(mat,rowvar=False)
val, vec = np.linalg.eig(todd)
print("特征值：", val)
print("特征向量：", vec)
vec = np.transpose(vec)
flag=0
for i in range(n):
    if val[i] > flag:
        flag = val[i]
        index = i
result=np.dot(mat,vec[index])
print("最终结果：",result)