def scoreMeetup(places):
if len(places) == 0:
return 99999999
center = centroid([(p["bounds"][0], p["bounds"][1]) for p in places])
distances = [distanceBetweenLatLongs(center, (p["bounds"][0], p["bounds"][1])) for p in places]
distance_score = reduce(lambda d1, d2: d1 + d2, distances, 0)
average_distance = distance_score / len(places)
variance_score = math.sqrt(reduce(lambda s, d: s + (d - average_distance)**2, distances, 0))
return distance_score + variance_score, center
def updateMeetupScore(total_distance, total_variance, center, numUsers, place):
place_point = randomPointInRegion(place["bounds"])
center_sum = (center[0] * numUsers, center[1] * numUsers)
print "%s %s %s"%(place["key"],center, place_point)
new_center = ((center_sum[0] + place_point[0]) / (numUsers + 1), (center_sum[1] + place_point[1]) / (numUsers + 1))
distance = distanceBetweenLatLongs(new_center, place_point)
new_distance = total_distance + distance
average_distance = new_distance / (numUsers + 1)
new_variance = math.sqrt(total_variance**2 + (distance - average_distance)**2)
return new_distance, new_variance, new_center
def updateMeetupScore(total_distance, total_variance, center, numUsers, place):
place_point = randomPointInRegion(place["bounds"])
center_sum = (center[0] * numUsers, center[1] * numUsers)
print "%s %s %s" % (place["key"], center, place_point)
new_center = ((center_sum[0] + place_point[0]) / (numUsers + 1),
(center_sum[1] + place_point[1]) / (numUsers + 1))
distance = distanceBetweenLatLongs(new_center, place_point)
new_distance = total_distance + distance
average_distance = new_distance / (numUsers + 1)
new_variance = math.sqrt(total_variance**2 +
(distance - average_distance)**2)
return new_distance, new_variance, new_center
def scoreMeetup(places):
if len(places) == 0:
return 99999999
center = centroid([(p["bounds"][0], p["bounds"][1]) for p in places])
distances = [
distanceBetweenLatLongs(center, (p["bounds"][0], p["bounds"][1]))
for p in places
]
distance_score = reduce(lambda d1, d2: d1 + d2, distances, 0)
average_distance = distance_score / len(places)
variance_score = math.sqrt(
reduce(lambda s, d: s + (d - average_distance)**2, distances, 0))
return distance_score + variance_score, center
