File "D:/pycharm/projects/Pythoneeee/projects/d.py", line 65, in <module> coef_mat = np.column_stack((prob_theta, logit_fit.coef_[0], linear_fit.coef_[0])) File "<__array_function__ internals>", line 200, in column_stack File "D:\pycharm\projects\venv\lib\site-packages\numpy\lib\shape_base.py", line 656, in column_stack return _nx.concatenate(arrays, 1) File "<__array_function__ internals>", line 200, in concatenate ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 2 and the array at index 1 has size 10

这个错误提示是因为`prob_theta`和`logit_fit.coef_[0]`的维度不一致导致的。可以将`prob_theta`转换成`(n,1)`的矩阵，然后再进行列合并，这样就能保证每个数组在第0个维度上的长度一致了。具体代码如下：

prob_theta = prob_theta.reshape(-1, 1)
coef_mat = np.column_stack((prob_theta, logit_fit.coef_[0], linear_fit.coef_[0]))

这里的`reshape`方法将`prob_theta`转换成`(n,1)`的矩阵，其中`-1`表示自动计算维度大小，因为我们只需要将行数确定为`n`即可。

相关问题

Traceback (most recent call last): File "D:/pycharm/projects/Pythoneeee/projects/d.py", line 65, in <module> coef_mat = np.column_stack((prob_fit.theta_.T, logit_fit.coef_, linear_fit.coef_)) # File "<__array_function__ internals>", line 200, in column_stack File "D:\pycharm\projects\venv\lib\site-packages\numpy\lib\shape_base.py", line 656, in column_stack return _nx.concatenate(arrays, 1) File "<__array_function__ internals>", line 200, in concatenate ValueError: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 10 and the array at index 1 has size 1

这个错误提示是因为`prob_fit.theta_`的维度是`(1, n)`，而`logit_fit.coef_`和`linear_fit.coef_`的维度是`(1,)`和`(1, m)`，所以无法直接进行列合并。可以使用`np.squeeze`方法将`prob_fit.theta_`的维度压缩成`(n,)`，然后再进行列合并。具体代码如下：

prob_theta = np.squeeze(prob_fit.theta_)
coef_mat = np.column_stack((prob_theta, logit_fit.coef_[0], linear_fit.coef_[0]))

这里的`np.squeeze`方法可以将维度为1的维度压缩掉，即将`(1, n)`的矩阵转换成`(n,)`的向量。

Traceback (most recent call last): File "D:/pycharm/projects/Pythoneeee/projects/最优化期末老师帮.py", line 40, in <module> prop_mat = np.column_stack((prob_fit / logit_fit, TypeError: unsupported operand type(s) for /: 'GaussianNB' and 'LogisticRegression'

这个错误提示是因为在进行列合并时，prob_fit和logit_fit的类型分别为GaussianNB和LogisticRegression，而这两个类型无法进行除法操作。因此，需要先将它们转换为可以进行除法操作的类型。

在scikit-learn库中，GaussianNB和LogisticRegression模型的系数矩阵可以通过调用相应模型对象的`theta_`和`coef_`属性来获取。因此，可以按照如下方式修改代码：

import numpy as np
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression

# 创建基于概率的模型
prob_model = GaussianNB()
prob_model.fit(X_train, y_train)
prob_fit = prob_model.theta_.reshape((-1, 1))

# 创建基于逻辑回归的模型
logit_model = LogisticRegression()
logit_model.fit(X_train, y_train)
logit_fit = logit_model.coef_.T

# 创建基于线性回归的模型
linear_model = LinearRegression()
linear_model.fit(X_train, y_train)
linear_fit = linear_model.coef_

# 将三个模型的系数矩阵按列合并
prop_mat = np.column_stack((prob_fit / logit_fit, linear_fit))

在这里，`theta_`属性返回GaussianNB模型的每个特征的均值向量，需要将其reshape为列向量。`coef_`属性返回LogisticRegression模型的系数矩阵，需要进行转置后才能合并。最后，将三个模型的系数矩阵按列合并即可。