针对上述回答有报错:ValueError: shapes (3,3) and (9,3) not aligned: 3 (dim 1) != 9 (dim 0)
时间: 2024-03-13 15:47:33 浏览: 25
非常抱歉,我这里有一处代码写错了,已经修正。以下是正确的代码:
```python
import numpy as np
def pls(X,Y,K):
"""
X: n*m的光谱矩阵
Y: n*1的响应变量矩阵
K: 取前K个主成分
"""
n,m = X.shape
# 中心化X和Y
X_mean = X.mean(axis=0)
X = X - np.tile(X_mean,(n,1))
Y_mean = Y.mean()
Y = Y - Y_mean
# 初始化
T = np.zeros((n,K))
P = np.zeros((m,K))
W = np.zeros((m,K))
Q = np.zeros((1,K))
B = np.zeros((K,1))
# 迭代
for i in range(K):
# 计算回归系数
w = np.dot(X.T,Y)/np.dot(X.T,X)
# 标准化w
w = w/np.linalg.norm(w)
# 计算t和p
t = np.dot(X,w)
p = np.dot(X.T,t)/np.dot(t.T,t)
# 计算q
q = np.dot(Y.T,t)/np.dot(t.T,t)
# 计算b
b = q/(np.dot(p.T,q))
# 更新X和Y
X = X - np.dot(t.reshape(-1,1),p.reshape(1,-1))
Y = Y - np.dot(t,b)
# 存储结果
T[:,i] = t
P[:,i] = p
W[:,i] = w
Q[0,i] = q
B[i,0] = b
return T,P,W,Q,B,Y_mean,X_mean
# 示例
X = np.array([[1,2,3],[4,5,6],[7,8,9]])
Y = np.array([1,2,3])
K = 2
T,P,W,Q,B,Y_mean,X_mean = pls(X,Y,K)
print("T:\n",T)
print("P:\n",P)
print("W:\n",W)
print("Q:\n",Q)
print("B:\n",B)
print("Y_mean:\n",Y_mean)
print("X_mean:\n",X_mean)
```
希望这次能够正确运行。