def find_match():
side = int(f.request.form['side'])
assert side in [0, 1]
_id = f.request.form['_id']
first = DEST if side else ORIGIN
second = ORIGIN if side else DEST
query, res_ids, answers, dsts, among = cn.find_closest(_id, first, second)
_ = cn.interpret(first['features'][query, :],
second['features'][answers[0], :])
query_info, first_answer, feature_order = _
answers_info = [first_answer]
answers_info.extend([
cn.interpret(first['features'][query, :],
second['features'][answer, :], feature_order)[1]
for answer in answers[1:]
])
sendf = lambda x, p: ('{:.' + str(p) + 'f}').format(float(x))
res = {
'query': query_info,
'answers_id': list(res_ids),
'distances': [sendf(d, 5) for d in dsts],
'explanations': answers_info,
'among': among
}
return f.jsonify(r=res)
def find_match():
side = int(f.request.form['side'])
assert side in [0, 1]
_id = f.request.form['_id']
first = DEST if side else ORIGIN
second = ORIGIN if side else DEST
query, res_ids, answers, dsts, among = cn.find_closest(_id, first, second)
_ = cn.interpret(first['features'][query, :],
second['features'][answers[0], :])
query_info, first_answer, feature_order = _
answers_info = [first_answer]
answers_info.extend([cn.interpret(first['features'][query, :],
second['features'][answer, :],
feature_order)[1]
for answer in answers[1:]])
sendf = lambda x, p: ('{:.'+str(p)+'f}').format(float(x))
res = {'query': query_info, 'answers_id': list(res_ids),
'distances': [sendf(d, 5) for d in dsts],
'explanations': answers_info, 'among': among}
return f.jsonify(r=res)
def get_knn_candidates(vids, left_knn, right_knn, at_least, at_most=None):
"""Return between `at_least` and `at_most` venue in right that are close (in
the sense of euclidean distance) of the `vids` in left. Namely, it return
their row number and their ids."""
import heapq
candidates = []
candidates_id = []
knn = right_knn['knn']
at_most = int(at_most) or 50000
nb_venues = min(at_most, max(len(vids)*knn, at_least))
for idx, vid in enumerate(vids):
_, rid, ridx, dst, _ = cn.find_closest(vid, left_knn, right_knn)
for dst_, rid_, ridx_, idx_ in zip(dst, rid, ridx, range(knn)):
if rid_ not in candidates_id:
candidates_id.append(rid_)
heapq.heappush(candidates, (dst_, idx*knn+idx_,
(rid_, ridx_)))
nb_venues = min(len(candidates), int(nb_venues))
closest = heapq.nsmallest(nb_venues, candidates)
mask = np.array([v[2][1] for v in closest])
r_vids = np.array([v[2][0] for v in closest])
return mask, r_vids
def get_knn_candidates(vids, left_knn, right_knn, at_least, at_most=None):
"""Return between `at_least` and `at_most` venue in right that are close (in
the sense of euclidean distance) of the `vids` in left. Namely, it return
their row number and their ids."""
import heapq
candidates = []
candidates_id = []
knn = right_knn['knn']
at_most = int(at_most) or 50000
nb_venues = min(at_most, max(len(vids) * knn, at_least))
for idx, vid in enumerate(vids):
_, rid, ridx, dst, _ = cn.find_closest(vid, left_knn, right_knn)
for dst_, rid_, ridx_, idx_ in zip(dst, rid, ridx, range(knn)):
if rid_ not in candidates_id:
candidates_id.append(rid_)
heapq.heappush(candidates,
(dst_, idx * knn + idx_, (rid_, ridx_)))
nb_venues = min(len(candidates), int(nb_venues))
closest = heapq.nsmallest(nb_venues, candidates)
mask = np.array([v[2][1] for v in closest])
r_vids = np.array([v[2][0] for v in closest])
return mask, r_vids
