KNN_michel_sacha/py_main.py

import numpy as np

def lecture(name):
    lst_date = []
    lst_conso = []
    lst_t_moy = []
    lst_t_ref = []
    with open(name) as fic:
        fic.readline()
        for ligne in fic: 
            date,conso, t_moy, t_ref = ligne.split(';')
            lst_date.append(date)
            lst_conso.append(float(conso))
            lst_t_moy.append(float(t_moy))
            lst_t_ref.append(float(t_ref))
    return(np.array(lst_date), np.array(lst_conso), np.array(lst_t_moy), np.array(lst_t_ref))
           
def numero_jour (date):
    
    return int(date.split("-")[2])

def distance (pos1, pos2):
    x1, y1, z1 = pos1
    x2, y2, z2 = pos2
    return np.sqrt((x1-x2)**2)+((y1-y2)**2)+((z1-z2)**2)
    
def kPlusProches(echantillon, donnees, k):
    voisins = []  
    for i in range(len(donnees)):
        date, conso, t_moy = donnees[i]
        d = distance(echantillon, (date, conso, t_moy))
        print (d)
        voisins.append((d, i))
    return [voisins[i][1] for i in range(k)]  

num_jour = []
    
dates, consos, t_moys, t_refs = lecture("pic-journalier-consommation.csv")
for date in dates:
    num_jour.append(numero_jour(date))
coords = list(zip(num_jour, t_moys, t_refs))
print(kPlusProches((31, 0, 0), coords, 4))