from sklearn import datasets
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.ensemble import RandomForestClassifier

def scores(list1, list2):
    errores = 0
    aciertos = 0
    if len(list1) != len(list2):
        print("ERROR. LENGTH MISMATCH")
    for i in range(len(list1)):
        if list1[i] == list2[i]:
            aciertos += 1
        else:
            errores += 1
    cocienteErrores = errores / len(list1)
    return [aciertos, errores, cocienteErrores]

iris = datasets.load_iris()

myMultinomialNB = MultinomialNB()
myBernoulliNB = BernoulliNB()

y_pred = myMultinomialNB.fit(iris.data, iris.target).predict(iris.data)

'''
for i in range(len(iris.target)):
    print(str(iris.target[i]) + "\t" + str(y_pred[i]) + "\t" + str(iris.data[i]))
'''

myRandomForest = RandomForestClassifier()
y_pred = myRandomForest.fit(iris.data, iris.target).predict(iris.data)
results = scores(iris.target, y_pred)
print("Errores: {}".format(results[1]))
print("Aciertos: {}".format(results[0]))
print("Cociente error: {}".format(results[2]))