How to repeat the steps

In my program I have total of 3 steps.

1. To find the mean
2. Split the given list using the mean value
3. If the threshold value > epsilon then update mean value and repeat the step 2 again with updated mean value, else finished.

I have achieved above two steps anyhow. Third step I have achieved only half and struggling in how to repeat the step 2 till the threshold value will be less than epsilon.

My code is below:

import numpy as np
store_list = [1,2,3,1,2,3,1]

# Step 1
mean_value = np.mean(store_list)
print("Mean Value : " + str(mean_value))

# Step 2
small_list = [i for i in store_list if i <= mean_value]
big_list = [i for i in store_list if i > mean_value]
print(small_list)
print(big_list)

toe_1 = np.mean(small_list)
toe_2 = np.mean(big_list)
print("toe value 1 : " + str(toe_1))
print("toe value 2 : " + str(toe_2))

toe_sum = np.sum([toe_1, toe_2])
toe_cap = np.divide(toe_sum, 2)
print("toe cap : " + str(toe_cap))

# Step 3
new_threshold = np.subtract(mean_value, toe_cap)
print(new_threshold)
epsilon_value = 0.1
if new_threshold > epsilon_value:
    mean_value = toe_cap
    print("new mean : " + str(mean_value))

My output of above is:

Mean Value : 1.8571428571428572
[1, 1, 1]
[2, 3, 2, 3]
toe value 1 : 1.0
toe value 2 : 2.5
toe cap : 1.75
new threshold : 0.1071428571428572
new mean : 1.75

In this case I have to repeat the step 2 again considering new mean. I am new to Python.

Answer

I’m not able to numpy but i can imagine that all you need is a recursive runner function, i don’t want to break the structure of your code, therefore i did not try to optimize the code. You can try the following code, i tried, it works:

import numpy as np


def step2(store_list, mean_value):
    small_list = [i for i in store_list if i <= mean_value]
    big_list = [i for i in store_list if i > mean_value]
    toe_1 = np.mean(small_list)
    toe_2 = np.mean(big_list)
    toe_sum = np.sum([toe_1, toe_2])
    toe_cap = np.divide(toe_sum, 2)
    return small_list, big_list, toe_1, toe_2, toe_cap


def step3(mean_value, toe_cap):
    new_threshold = np.subtract(mean_value, toe_cap)
    print(new_threshold)
    epsilon_value = 0.1
    if new_threshold > epsilon_value:
        return True, toe_cap
    else:
        return False, mean_value


def my_run(store_list, mean_value):
    small_list, big_list, toe_1, toe_2, toe_cap = step2(store_list, mean_value)
    print(small_list)
    print(big_list)
    print("toe value 1 : " + str(toe_1))
    print("toe value 2 : " + str(toe_2))
    print("toe cap : " + str(toe_cap))
    condition, mean_value = step3(mean_value, toe_cap)
    if condition:
        print("new mean : " + str(mean_value))
        my_run(store_list, mean_value)


def main():
    store_list = [1, 2, 3, 1, 2, 3, 1]
    mean_value = np.mean(store_list)
    print("Mean Value : " + str(mean_value))
    my_run(store_list, mean_value)


if __name__ == '__main__':
    main()