def main():
print("Testing KD Tree...")
test_times = 100
run_time_1 = run_time_2 = 0
for _ in range(test_times):
# 随机生成数据
low = 0
high = 100
n_rows = 1000
n_cols = 2
X = gen_data(low, high, n_rows, n_cols)
y = gen_data(low, high, n_rows)
Xi = gen_data(low, high, n_cols)
# 创建Kd树
tree = KDTree()
tree.build_tree(X, y)
# Kd树查找
start = time()
nd = tree.nearest_neighbour_search(Xi)
run_time_1 += time() - start
ret1 = get_eu_dist(Xi, nd.split[0])
# 普通线性查找
start = time()
row = exhausted_search(X, Xi)
run_time_2 += time() - start
ret2 = get_eu_dist(Xi, row)
# 比较结果
assert ret1 == ret2, "target:%s\nrestult1:%s\nrestult2:%s\ntree:\n%s" % (
Xi, nd, row, tree)
print("%d tests passed!" % test_times)
print("KD Tree Search %.2f s" % run_time_1)
print("Exhausted search %.2f s" % run_time_2)
# [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])
from data import Data
from kd_tree import KDTree
kd = KDTree(2)
d = Data()
d.extract('example.dat')
print d.data
kd.build_tree(d.data.keys()[:7])
print kd
print kd.nearest((7,7), kd.root)
#print kd
