Normalization VS. numpy way to normalize?

Question

I'm supposed to normalize an array. I've read about normalization and come across a formula:

I wrote the following function for it:

def normalize_list(list):
    max_value = max(list)
    min_value = min(list)
    for i in range(0, len(list)):
        list[i] = (list[i] - min_value) / (max_value - min_value)

That is supposed to normalize an array of elements.

Then I have come across this: https://stackoverflow.com/a/21031303/6209399 Which says you can normalize an array by simply doing this:

def normalize_list_numpy(list):
    normalized_list = list / np.linalg.norm(list)
    return normalized_list

---

If I normalize this test array test_array = [1, 2, 3, 4, 5, 6, 7, 8, 9] with my own function and with the numpy method, I get these answers:

My own function: [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]
The numpy way: [0.059234887775909233, 0.11846977555181847, 0.17770466332772769, 0.23693955110363693, 0.29617443887954614, 0.35540932665545538, 0.41464421443136462, 0.47387910220727386, 0.5331139899831830

---

Why do the functions give different answers? Is there others way to normalize an array of data? What does numpy.linalg.norm(list) do? What do I get wrong?

Answer 1

There are different types of normalization. You are using min-max normalization. The min-max normalization from scikit learn is as follows.

import numpy as np
from sklearn.preprocessing import minmax_scale

# your function
def normalize_list(list_normal):
    max_value = max(list_normal)
    min_value = min(list_normal)
    for i in range(len(list_normal)):
        list_normal[i] = (list_normal[i] - min_value) / (max_value - min_value)
    return list_normal

#Scikit learn version 
def normalize_list_numpy(list_numpy):
    normalized_list = minmax_scale(list_numpy)
    return normalized_list

test_array = [1, 2, 3, 4, 5, 6, 7, 8, 9]
test_array_numpy = np.array(test_array)

print(normalize_list(test_array))
print(normalize_list_numpy(test_array_numpy))

Output:

[0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]    
[0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]

MinMaxscaler uses exactly your formula for normalization/scaling: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html

@OuuGiii: NOTE: It is not a good idea to use Python built-in function names as varibale names. list() is a Python builtin function so its use as a variable should be avoided.