用python实现光谱拆分，利用最小二乘法

好的，针对您的问题，以下是用python实现光谱拆分，利用最小二乘法的代码：

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((m,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,np.tile(p,(1,n)).T)
        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)

希望这能够帮到您！