EXTRA 5 Scaler & Reverse (Διαίρεση με το 100 στα δεδομένα). (EXTRA 5 Scaler (Division by 100 on the data)).

 

Θα διαιρέσουμε τα δεδομένα με το 100 για να τα προετοιμάσουμε για την πρόβλεψη. We will divide the data by 100 to prepare it for prediction.

data = [

    [22, 25, 31, 8, 10],
    [9, 16, 14, 27, 34],
    [12, 19, 23, 27, 13],
    [4, 21, 18, 25, 31],
    [23, 10, 35, 13, 32],
    [8, 20, 15, 26, 10],
    [19, 24, 17, 8, 2],
    [17, 30, 21, 3, 9],
    [24, 17, 31, 21, 13],
    [27, 11, 31, 5, 14],
    [27, 6, 16, 33, 22],
    [18, 16, 28, 34, 17],
    [13, 23, 8, 31, 12],
    [28, 6, 17, 1, 18],
    [30, 33, 19, 13, 20],
    [7, 6, 26, 11, 34],
    [2, 19, 1, 12, 10],
    [8, 21, 15, 19, 9],
    [22, 4, 29, 8, 24],
    [16, 1, 20, 7, 31],
    [6, 27, 3, 30, 10],
    [10, 34, 26, 24, 23],
    [20, 18, 34, 11, 2],
    [3, 10, 26, 35, 7],
    [24, 14, 35, 1, 21],
    [19, 17, 26, 30, 24],
    [29, 18, 26, 14, 2],
    [13, 22, 14, 21, 7],
    [5, 16, 1, 18, 28],
    [34, 18, 31, 28, 27],
    [20, 8, 4, 21, 15],
    [25, 24, 6, 5, 15],
    [35, 29, 16, 13, 27],
    [22, 27, 9, 10, 17],
    [16, 17, 34, 32, 2],
    [7, 22, 8, 3, 29],
    [21, 1, 19, 6, 18],
    [9, 14, 26, 18, 3],
    [1, 23, 33, 34, 3],
    [2, 24, 30, 5, 12],
    [2, 8, 9, 17, 19],
    [9, 17, 15, 27, 23],
    [5, 4, 35, 27, 20],
    [15, 19, 26, 4, 16],
    [23, 4, 32, 29, 18],
    [29, 27, 10, 13, 22],
    [11, 25, 22, 1, 6],
    [33, 2, 8, 1, 22],
    [19, 6, 5, 18, 7],
    [2, 26, 14, 30, 15],
    [22, 17, 14, 33, 11],
    [30, 24, 8, 12, 33],
    [2, 19, 12, 4, 1],
    [3, 9, 17, 2, 4],
    [35, 3, 11, 18, 20],
    [7, 14, 9, 21, 18],
    [7, 4, 32, 12, 35],
    [25, 13, 7, 18, 31],
    [20, 34, 29, 13, 11],
    [22, 23, 5, 13, 15],
    [30, 32, 9, 26, 20],
    [19, 22, 9, 35, 12],
    [31, 27, 2, 29, 7],
    [34, 26, 2, 12, 6],
    [13, 3, 18, 16, 10],
    [11, 9, 5, 4, 13],
    [12, 29, 11, 34, 6],
    [23, 15, 35, 12, 24],
    [25, 27, 29, 35, 2],
    [25, 4, 31, 35, 23],

]

# Αντιστροφή της λίστας
data.reverse()

# Εκτύπωση των ταξινομημένων λιστών με scaler / 100
for sublist in data:
    print('[', end='')
    print(*[x / 100 for x in sublist], sep=', ', end='],\n')

ΑΠΟΤΕΛΕΣΜΑΤΑ

[0.15, 0.21, 0.13, 0.17, 0.14],
[0.26, 0.07, 0.1, 0.33, 0.04],
[0.35, 0.04, 0.02, 0.1, 0.05],
[0.11, 0.25, 0.07, 0.03, 0.05],
[0.33, 0.03, 0.24, 0.2, 0.29],
[0.09, 0.18, 0.15, 0.33, 0.29],
[0.16, 0.21, 0.05, 0.07, 0.19],
[0.35, 0.22, 0.28, 0.01, 0.29],
[0.13, 0.02, 0.04, 0.22, 0.09],
[0.08, 0.1, 0.28, 0.07, 0.22],
[0.15, 0.02, 0.08, 0.06, 0.13],
[0.34, 0.25, 0.04, 0.22, 0.26],
[0.3, 0.27, 0.16, 0.29, 0.24],
[0.1, 0.28, 0.32, 0.23, 0.31],
[0.24, 0.01, 0.32, 0.16, 0.35],
[0.19, 0.09, 0.31, 0.06, 0.01],
[0.04, 0.22, 0.02, 0.26, 0.06],
[0.2, 0.05, 0.02, 0.01, 0.33],
[0.13, 0.01, 0.14, 0.07, 0.22],
[0.24, 0.23, 0.12, 0.27, 0.33],
[0.26, 0.07, 0.33, 0.23, 0.03],
[0.19, 0.06, 0.05, 0.18, 0.07],
[0.33, 0.02, 0.08, 0.01, 0.22],
[0.11, 0.25, 0.22, 0.01, 0.06],
[0.29, 0.27, 0.1, 0.13, 0.22],
[0.23, 0.04, 0.32, 0.29, 0.18],
[0.15, 0.19, 0.26, 0.04, 0.16],
[0.05, 0.04, 0.35, 0.27, 0.2],
[0.09, 0.17, 0.15, 0.27, 0.23],
[0.02, 0.08, 0.09, 0.17, 0.19],
[0.02, 0.24, 0.3, 0.05, 0.12],
[0.01, 0.23, 0.33, 0.34, 0.03],
[0.09, 0.14, 0.26, 0.18, 0.03],
[0.21, 0.01, 0.19, 0.06, 0.18],
[0.07, 0.22, 0.08, 0.03, 0.29],
[0.16, 0.17, 0.34, 0.32, 0.02],
[0.22, 0.27, 0.09, 0.1, 0.17],
[0.35, 0.29, 0.16, 0.13, 0.27],
[0.25, 0.24, 0.06, 0.05, 0.15],
[0.2, 0.08, 0.04, 0.21, 0.15],
[0.34, 0.18, 0.31, 0.28, 0.27],
[0.05, 0.16, 0.01, 0.18, 0.28],
[0.13, 0.22, 0.14, 0.21, 0.07],
[0.29, 0.18, 0.26, 0.14, 0.02],
[0.19, 0.17, 0.26, 0.3, 0.24],
[0.24, 0.14, 0.35, 0.01, 0.21],
[0.03, 0.1, 0.26, 0.35, 0.07],
[0.2, 0.18, 0.34, 0.11, 0.02],
[0.1, 0.34, 0.26, 0.24, 0.23],
[0.06, 0.27, 0.03, 0.3, 0.1],
[0.16, 0.01, 0.2, 0.07, 0.31],
[0.22, 0.04, 0.29, 0.08, 0.24],
[0.08, 0.21, 0.15, 0.19, 0.09],
[0.02, 0.19, 0.01, 0.12, 0.1],
[0.07, 0.06, 0.26, 0.11, 0.34],
[0.3, 0.33, 0.19, 0.13, 0.2],
[0.28, 0.06, 0.17, 0.01, 0.18],
[0.13, 0.23, 0.08, 0.31, 0.12],
[0.18, 0.16, 0.28, 0.34, 0.17],
[0.06, 0.21, 0.3, 0.05, 0.17],
[0.12, 0.16, 0.19, 0.28, 0.29],
[0.35, 0.02, 0.04, 0.29, 0.07],
[0.07, 0.11, 0.22, 0.27, 0.24],
[0.18, 0.35, 0.26, 0.25, 0.11],
[0.31, 0.3, 0.13, 0.04, 0.33],
[0.12, 0.29, 0.02, 0.18, 0.03],
[0.32, 0.2, 0.14, 0.11, 0.05],
[0.17, 0.05, 0.22, 0.28, 0.01],
[0.34, 0.26, 0.3, 0.19, 0.22],
[0.27, 0.06, 0.16, 0.33, 0.22],
[0.27, 0.11, 0.31, 0.05, 0.14],
[0.24, 0.17, 0.31, 0.21, 0.13],
[0.17, 0.3, 0.21, 0.03, 0.09],
[0.19, 0.24, 0.17, 0.08, 0.02],
[0.08, 0.2, 0.15, 0.26, 0.1],
[0.23, 0.1, 0.35, 0.13, 0.32],
[0.04, 0.21, 0.18, 0.25, 0.31],
[0.12, 0.19, 0.23, 0.27, 0.13],
[0.09, 0.16, 0.14, 0.27, 0.34],
[0.22, 0.25, 0.31, 0.08, 0.1],

Έχω διαίρεσει τα δεδομένα του EXTRA 5 με το 100, προετοιμάζοντάς τα για πιθανή χρήση σε μοντέλα πρόβλεψης ή άλλες αναλυτικές διαδικασίες. 
Αυτή η μετατροπή βοηθάει στην κανονικοποίηση των δεδομένων, κάνοντας τις τιμές να κυμαίνονται μεταξύ 0 και 1, πράγμα που είναι συχνά ωφέλιμο στην επεξεργασία δεδομένων και την εφαρμογή στατιστικών και μαθηματικών μοντέλων.