print(f"findBestKWithCrossValidation: {findBestKWithCrossValidation}")
print("\n")
print('-' * 175)
print(f"Iris dataset classification: \n")
startTime = time.time()
# Entrainement sur l'ensemble de données Iris
iris_train, iris_train_labels, iris_test, iris_test_labels = load_datasets.load_iris_dataset(
knn_train_ratio)
iris_knn = Knn(distance_func=distanceFunc)
iris_knn.train(iris_train,
iris_train_labels,
findBestKWithCrossValidation=findBestKWithCrossValidation)
cm, _, _, _ = iris_knn.test(iris_test, iris_test_labels)
ConfusionMatrixListKnn.append(cm)
print(
f"\n --- Elapse time: {1_000*(time.time() - startTime):.2f} ms --- \n")
print('-' * 175)
print(f"Congressional dataset classification: \n")
startTime = time.time()
# Entrainement sur l'ensemble de données Congressional
cong_train, cong_train_labels, cong_test, cong_test_labels = load_datasets.load_congressional_dataset(
knn_train_ratio)
cong_knn = Knn(distance_func=distanceFunc)
cong_knn.train(cong_train,
print('MONK 2')
knn_monks_2.train(monks_train_2, monks_train_labels_2)
print('')
print('MONK 3')
knn_monks_3.train(monks_train_3, monks_train_labels_3)
# Testez votre classifieur KNN
print('--------------------')
print('IRIS DATASET TEST')
print('--------------------')
k_optimal = knn_iris.get_optimal_k(kmin=1, kmax=6)
#print('-------Performance générales sur les données de test---------')
knn_iris.set_nbNeighbors(k_optimal)
print('Running now on test data with k = ', k_optimal)
knn_iris.test(iris_test, iris_test_labels)
print('--------------------')
print('VOTE DATASET TEST')
print('--------------------')
k_optimal = knn_vote.get_optimal_k(kmin=1, kmax=6)
#print('-------Performance générales sur les données de test---------')
knn_vote.set_nbNeighbors(k_optimal)
print('Running now on test data with k = ', k_optimal)
knn_vote.test(congressional_test, congressional_test_labels)
print('--------------------')
print('MONKS DATASET TEST')
print('--------------------')
print('')
print('MONK 1')
