task-17-lct/backend
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add match

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ilia 2023-05-23 18:27:14 +03:00
parent 7d8f0578f5
commit 97df3fa982
1 changed files with 17 additions and 9 deletions
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26
passfinder/recomendations/service/service.py
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@ -191,9 +191,14 @@ def get_personal_movies_recommendation(user):
def dist_func(event1: Event, event2: Event): def dist_func(event1: Event, event2: Event):
cords1 = [event1.lat, event1.lon] # cords1 = [event1.lat, event1.lon]
cords2 = [event2.lat, event2.lon] # cords2 = [event2.lat, event2.lon]
return GD(cords1, cords2).km # try:
# dist = GD(cords1, cords2).km
# return dist
# except:
# return 1000000
return (event1.lon - event2.lon) ** 2 + (event1.lat - event2.lat) ** 2
def generate_nearest(): def generate_nearest():
@ -219,12 +224,15 @@ def generate_hotel_nearest():
if i % 100 == 0: if i % 100 == 0:
print(i) print(i)
def match_museums(): def match_points():
regions = list(Region.objects.all()) regions = list(City.objects.all())
for museum in Event.objects.filter(type='museum'): for i, point in enumerate(Event.objects.all()):
s_regions = list(sorted(regions.copy(), key=lambda x: dist_func(museum, x))) s_regions = list(sorted(regions.copy(), key=lambda x: dist_func(point, x)))
museum.region = s_regions[0] point.city = s_regions[0]
museum.save() point.save()
if i % 10 == 0:
print(i)
def calculate_mean_metric(favorite_events: Iterable[Event], target_event: Event, model: AnnoyIndex, rev_mapping): def calculate_mean_metric(favorite_events: Iterable[Event], target_event: Event, model: AnnoyIndex, rev_mapping):