def main():
probes, links = read_data()
link_points, belong = flatten_uniq(links)
print "%d probe points loaded" % len(probes)
print "%d link points loaded" % len(link_points)
# probes = np.array(random.sample(probes, 10000))
# print probes
probes = np.array(probes)
link_points = np.array(link_points)
probes_rad = np.deg2rad(probes)
link_points_rad = np.deg2rad(link_points)
knns_kd = nearest_kdtree(probes_rad,
link_points_rad,
n=100,
is_filter=False)
link_candidates = [
set([
bel for lat, lon in link_points_[knns] for bel in belong[lat, lon]
]) for knns in knns_kd
]
with open("data/link_candidates.pkl", "wb") as fout:
cPickle.dump(link_candidates, fout, 2)
def test():
probes, links = read_data()
link_points, belong = flatten_uniq(links)
print "%d probe points loaded" % len(probes)
print "%d link points loaded" % len(link_points)
probes = np.array(random.sample(probes, 100))
# print probes
link_points = np.array(link_points)
probes_rad = np.deg2rad(probes)
link_points_rad = np.deg2rad(link_points)
knns_kd = evaluate(
lambda: nearest_kdtree(
probes_rad, link_points_rad, n=30, is_filter=True), "kdtree")
knns_force = evaluate(
lambda: nearest_force(probes_rad, link_points_rad, n=30),
"brute force")
for idx, (k1, k2) in enumerate(izip(knns_kd, knns_force)):
diff = np.setdiff1d(k2, k1)
if diff.size > 0:
print diff
print probes[idx]
print link_points[diff]
print haversine_np(probes_rad[idx], link_points_rad[diff])
def main():
probes, links = read_data()
link_points, belong = flatten_uniq(links)
print "%d probe points loaded" % len(probes)
print "%d link points loaded" % len(link_points)
# probes = np.array(random.sample(probes, 10000))
# print probes
probes = np.array(probes)
link_points = np.array(link_points)
probes_rad = np.deg2rad(probes)
link_points_rad = np.deg2rad(link_points)
knns_kd = nearest_kdtree(probes_rad, link_points_rad, n=100, is_filter=False)
link_candidates = [set([bel for lat, lon in link_points_[knns] for bel in belong[lat, lon]]) for knns in knns_kd]
with open("data/link_candidates.pkl", "wb") as fout:
cPickle.dump(link_candidates, fout, 2)
def test():
probes, links = read_data()
link_points, belong = flatten_uniq(links)
print "%d probe points loaded" % len(probes)
print "%d link points loaded" % len(link_points)
probes = np.array(random.sample(probes, 100))
# print probes
link_points = np.array(link_points)
probes_rad = np.deg2rad(probes)
link_points_rad = np.deg2rad(link_points)
knns_kd = evaluate(lambda: nearest_kdtree(probes_rad, link_points_rad, n=30, is_filter=True), "kdtree")
knns_force = evaluate(lambda: nearest_force(probes_rad, link_points_rad, n=30), "brute force")
for idx, (k1, k2) in enumerate(izip(knns_kd, knns_force)):
diff = np.setdiff1d(k2, k1)
if diff.size > 0:
print diff
print probes[idx]
print link_points[diff]
print haversine_np(probes_rad[idx], link_points_rad[diff])
