使用for循环的简单线性回归仅适用于Python

Question

I am now doing a linear regression analysis. The input variable is Size. The output variable is Price. I store the set of data in 2D array. I know using NumPy is easy to conduct analysis but my professors told me to use for loop only to perform iterations. Formula of interation is shown as the picture in the hyperlink . Therefore, I decided the following code to perform the calculation:

#Structure of array (Stored in float), with structure like this [Room, Price]
array = [[4.0, 399.9], [5.0, 329.9], [6.0, 369.0]]

#Set initial value
theta_price = 0
theta_room = 0
stepsize = 0.01
item = 3

#Perform iterations
for looping in range(0, 50): #Loop 50 times
    for j in array[0]: #Call the value stored in array[0]
        for k in array[1]: #Call the value stored in array[1]
             theta_price_1 = theta_price - stepsize * (1 / item) * (sum((theta_price + theta_room * int(j) - int(k)))#Perform iterations of theta 0
             theta_room_1 = theta_room - stepsize * (1 / item) * (sum((theta_price + t + theta_room * int(j) - int(k))*int(j)))#Perform iterations of theta 1
             #Bring the new theta value to the next loop
             theta_price = theta_price_1
             theta_room = theta_room_1
             print(theta_price,theta_room)#Print the result for every loop

The above code was not function with error message at line 10 that:

'int' object is not iterable

But if I remove the sum function, it works with incorrect calculation results. Therefore, I know it has some problems with the sum function and array but I don't know how to solve it?

Answer 1

As I metioned in the comment, sum should be applied over all elements in each iteration, that's what Batch Gradient Descent does. So the code should be:

theta_price = 0
theta_room = 0
stepsize = 0.1
item = 5
#Perform iterations
array = [
            [0,1,2,3,4],
            [5,6,7,8,9],
        ]

for looping in range(0, 500): #Loop 50 times
         theta_price = theta_price - stepsize * (1 / item) * (sum([theta_price + theta_room * int(j) - int(k) for j, k in zip(array[0], array[1])]))#Perform iterations of theta 0
         theta_room = theta_room - stepsize * (1 / item) * (sum([(theta_price + theta_room * int(j) - int(k)) * int(j) for j, k in zip(array[0], array[1])]))#Perform iterations of theta 1
         print(theta_price,theta_room)#Print the result for every loop

after 500 iterations with the 5 test data, I can get the result:

4.999999614653767 1.0000001313279816

which are expected.