NameError: name '商品店铺' is not defined

This error message suggests that the name "商品店铺" has not been defined in the current scope. It could be that the name is misspelled or that it has not been assigned a value. To fix this error, you should check the spelling and make sure that the variable or function associated with the name has been defined in the code.

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NameError: name 'requests' is not definedNameError: name 'requests' is not defined NameError: name 'requests' is not defined

这个错误通常是由于没有导入requests模块而导致的。在使用requests模块之前，您需要确保已经正确导入了该模块。

例如，在Python中，您可以使用以下语句导入requests模块：

import requests

如果您已经导入了requests模块但仍然出现此错误，请确保您的代码中没有拼写错误，并且在导入模块后确保代码块或函数正确缩进。

如果问题仍然存在，请提供更多的代码细节，以便我能够更好地帮助您解决问题。

上述代码分别报错：NameError: name 'np' is not defined，NameError: name 'w' is not defined，NameError: name 'loss_history' is not defined，请解决

上述错误是因为你在代码中引用了未定义的变量。下面是修正后的代码，包含了对numpy、初始权重向量w以及历史损失loss_history的初始化：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 加载数据
data = pd.read_csv('BostonHousingData.csv')
X = data.drop('MEDV', axis=1)  # 特征矩阵
y = data['MEDV']  # 目标变量

# 初始化参数
n_features = X.shape[1]
w = np.random.randn(n_features)  # 初始化随机权重
learning_rate = 0.01
epochs = 1000

# 手动实现梯度下降并记录历史损失
def gradient_descent(X, y, w, learning_rate, epochs):
    m = len(y)
    loss_history = []
    for epoch in range(epochs):
        y_pred = np.dot(X, w)
        loss = (1/m) * np.mean((y - y_pred)**2)
        
        dw = -(2/m) * np.dot(X.T, (y - y_pred))
        w -= learning_rate * dw
        
        loss_history.append(loss)
    
    return w, loss_history

w, loss_history = gradient_descent(X, y, w, learning_rate, epochs)

# 可视化结果
plt.figure(figsize=(10, 6))

plt.subplot(1, 2, 1)
plt.plot(loss_history)
plt.title('Loss Function vs. Epochs')

plt.subplot(1, 2, 2)
plt.scatter(range(len(y)), y, label='Actual Prices')
plt.plot(range(len(y)), np.dot(X, w), label='Predicted Prices', color='red')
plt.legend()
plt.title('Actual vs. Predicted Prices (50 Samples)')
plt.xlabel('Sample Index')
plt.ylabel('Price')

plt.tight_layout()
plt.show()

现在，代码应该不会报错了。注意这里的`np.random.randn(n_features)`用于初始化随机权重，以便在第一次运行时有初始值。如果你之前已经运行过一次并且得到了w，那么再次运行时需要保留w的值，而不是每次都重新随机初始化。