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try to give better results by preselecting spaces

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Laura Klünder 2017-12-26 15:05:46 +01:00
parent ea9e5d56ef
commit d271062c86
1 changed files with 21 additions and 5 deletions
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26
src/c3nav/routing/locator.py
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@ -69,12 +69,24 @@ class Locator:
scan_values = LocatorPoint.convert_scan(scan, self.stations, create=False)
station_ids = frozenset(scan_values.keys())
if not scan_values:
return None
# get visible spaces
spaces = tuple((pk, space, station_ids & space.stations_set)
for pk, space in self.spaces.items()
if pk not in restrictions.spaces)
spaces = tuple((pk, space, station_ids) for pk, space, station_ids in spaces if station_ids)
# get relevant spaces
best_station_id = min(scan_values.items(), key=operator.itemgetter(1))[0]
spaces = tuple((pk, space, station_ids)
for pk, space, station_ids in spaces
if station_ids and best_station_id in station_ids)
if not spaces:
return None
# get good spaces
good_spaces = tuple((pk, space, station_ids) for pk, space, station_ids in spaces if len(station_ids) >= 3)
if not good_spaces:
for station_id in station_ids:
@ -85,7 +97,7 @@ class Locator:
best_score = float('inf')
for pk, space, station_ids in good_spaces:
point, score = space.get_best_point(scan_values, station_ids)
point, score = space.get_best_point(scan_values, station_ids, needed_station_id=best_station_id)
if score < best_score:
location = CustomLocation(router.spaces[pk].level, point.x, point.y,
permissions=permissions, icon='my_location')
@ -125,11 +137,15 @@ class LocatorSpace:
for station_id, value in point.values.items():
self.levels[i][self.stations_lookup[station_id]] = value**3
def get_best_point(self, scan_values, station_ids):
def get_best_point(self, scan_values, station_ids, needed_station_id=None):
stations = tuple(self.stations_lookup[station_id] for station_id in station_ids)
values = np.array(tuple(scan_values[station_id]**3 for station_id in station_ids), dtype=np.int64)
scores = np.sum((self.levels[:, stations]-values)**2, axis=1) / len(stations)
best_point = np.argmin(scores).ravel()[0]
acceptable_point_ids = tuple(
np.argwhere(self.levels[:, self.stations_lookup[needed_station_id]] > -90).ravel()
)
scores = np.sum((self.levels[acceptable_point_ids, stations]-values)**2, axis=1) / len(stations)
best_point = acceptable_point_ids[np.argmin(scores).ravel()[0]]
return self.points[best_point], scores[best_point]