def test():
X = [[1, 2], [3, 4], [5, 10], [15, 20]]
y = [1, 1, 0, 0]
Xi = [0, 0]
k = 2
ret1 = exhausted_search(X, Xi, k)
print(ret1)
model = KNeighborsBase()
model.fit(X, y, k_neighbors=k)
ret2 = model._knn_search(Xi)
for x in ret2.items:
print(x)
def main():
print("Testing K nearest search...")
test_times = 100
run_time_1 = run_time_2 = 0
for _ in range(test_times):
# Generate dataset randomly
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)
# Build KNN
k = 2
model = KNeighborsBase()
model.fit(X, y, k_neighbors=k)
# KD Tree Search
start = time()
heap = model._knn_search(Xi)
run_time_1 += time() - start
ret1 = [get_euclidean_distance(Xi, nd.split[0]) for nd in heap.items]
ret1.sort()
# Exhausted search
start = time()
ret2 = exhausted_search(X, Xi, k)
run_time_2 += time() - start
ret2 = [get_euclidean_distance(Xi, row) for row in ret2]
ret2.sort()
# Compare result
assert ret1 == ret2, "target:%s\nrestult1:%s\nrestult2:%s\ntree:\n%s" \
% (Xi, ret1, ret2, model.tree)
print("%d tests passed!" % test_times)
print("KNN Search %.2f s" % run_time_1)
print("Exhausted search %.2f s" % run_time_2)