Lista 03 ML

import pandas as pd
from Lista02 import FuncoesML as fun
from sklearn.cluster import KMeans
import numpy as np
from random import randint

def WCSS(distance):

    retorno = []
    for x in distance:
        soma = 0
        for z in x:
            soma += z**2
        retorno.append(soma)

    return retorno

wine = pd.read_csv('C:/Users/Auricelia/Desktop/DataSetsML/wine.csv')
wine2 = pd.read_csv('C:/Users/Auricelia/Desktop/DataSetsML/wine.csv')

del wine2['Class']
del wine2['delete']

wine2 = np.array(wine2)

wine = fun.normalizar(wine)

del wine[0]
del wine[13]
print(wine)

wine = np.array(wine)

dictionary = {}
dictionary2 = {}

for y2 in range(0,50):

    distance2 = []
    h2 = randint(1,10000)
    rng2 = np.random.RandomState(h2)
    centers2, labels2, distance2 = fun.find_clusters(wine2, 3, rng2)
    retorno2 = WCSS(distance2)

    num2 = retorno2[len(retorno2) - 1]
    dictionary2[h2] = num2

for y in range(50):

    distance = []
    h = randint(1,10000)
    rng = np.random.RandomState(h)

    for algumacoisa in range(20):
        centers, labels, distance = fun.find_clusters(wine, 3, rng)
        retorno = WCSS(distance)
        print("AQUI " + str(y) , retorno[len(retorno) - 1])

    num = retorno[len(retorno) - 1]
    dictionary[h] = num

#
# print("Distancias" ,distance)
print("Labels", labels)
# print("centers", centers)
# print("Quantas vezes rodou", len(distance))

wine = pd.DataFrame(wine)

wine['Class'] = labels

print(wine)


dictionary = sorted(dictionary.items(), key=lambda x: x[1])
dictionary2 = sorted(dictionary2.items(), key=lambda x: x[1])

# print("\n\n\n\n",dictionary)
# print("\n\n\n\n",dictionary2)