# [3,4,6,1],
# [3,6,6,1],
# [3,5,9,2],
# [3,5,12,2],
# [3,5,13,2],]
X = [[2, 3, 1], [5, 4, 1], [9, 6, 1], [8.5, 6, 1], [4, 7, 1], [8, 1, 1],
[7, 2, 1]]
X = np.array(X)
# data_train = pd.read_csv('./data_set/iris_1.csv', header=0)
# train_data = np.array(data_train)
# X = train_data[:, :-1]
# y = train_data[:, -1]
# X_train, X_test, y_train, y_true = train_test_split(X, y,test_size=1 / 3., random_state=6)
#
# train_set = np.column_stack((X_train, y_train))
kd = KDTree()
kd.build_tree(X)
x = [[7, 6, 1], [3, 4.5, 1]]
test_x = np.array(x)
# print(test_x[:,:-1])
nearest = kd.search_neighbour(test_x)
for i in range(len(test_x)):
print(test_x[i], '--->', nearest[i])
