错误: X.shape[1] =1应等于9，即训练时的要素数

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# 假设 X 和 y 是你的数据
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([1, 2, 3])

# 检查数据形状
print("原始数据形状:", X.shape)

# 分割数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 检查分割后的数据形状
print("训练数据形状:", X_train.shape)
print("测试数据形状:", X_test.shape)

# 确保特征数量一致
assert X_train.shape[1] == X_test.shape[1], "训练和测试数据的特征数量不一致"

# 训练模型
model = LinearRegression()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)