解決：TypeError ：cannot unpack non-iterable NoneType object

線性回歸中出現錯誤：

TypeError                                 Traceback (most recent call last)
<ipython-input-23-444c0fe3ed1c> in <module>  11 if __name__ == "__main__":  12 draw(x_train,y_train) ---> 13 w,b = fit(x_train,y_train)  14 print(w,b)  15 fit_line(w,b) TypeError: 報錯的原因是函數返回值得數量不一致，查看函數返回值數量和調用函數時接收返回值的數量是不是一致，修改一致即可 

 

 錯誤源碼如下：

def fit(x_train,y_train):
    size = len(x_train)
    numerator = 0
    denominator = 0
    for i in range(size):
        numerator += (x_train[i] - np.mean(x_train))*(y_train[i] - np.mean(y_train))
        denominator += (x_train[i] - np.mean(x_train))**2
    w = numerator/denominator
    b = np.mean(y_train) -w*np.mean(x_train)
。。。
if __name__ == "__main__":
    draw(x_train,y_train)
    w,b = fit(x_train,y_train)
    print(w,b)
    fit_line(w,b)
print(predict(15000,w,b))

　解決方法：報錯意思是函數返回值得數量不一致

修改代碼：

def fit(x_train,y_train):
    size = len(x_train)
    numerator = 0
    denominator = 0
    for i in range(size):
        numerator += (x_train[i] - np.mean(x_train))*(y_train[i] - np.mean(y_train))
        denominator += (x_train[i] - np.mean(x_train))**2
    w = numerator/denominator
    b = np.mean(y_train) -w*np.mean(x_train)

    ##添加return返回即可解決
    return w,b