def stop_by(curLati, curLong, desLati, desLong, keywords, maxLen):
result = []
for s in data:
isAllContain = True
for keyword in keywords:
if not keyword in s['keywords']:
isAllContain = False
break
if isAllContain and (ctp17hw1.dist(curLati, curLong, s['latitude'], s['longitude']) + \
ctp17hw1.dist(s['latitude'], s['longitude'], desLati, desLong)) < maxLen:
result.append(s['name'])
return result
# print(stop_by(37.477155, 126.963423, 37.459250, 126.950786, ["Matjip", "Honbap"], 3500))
def nearest(curLati, curLong, keyword):
name = ""
for s in data:
if keyword in s['keywords']:
minDist = ctp17hw1.dist(curLati, curLong, s['latitude'], s['longitude'])
name = s['name']
break
for s in data:
if keyword in s['keywords']:
dist = ctp17hw1.dist(curLati, curLong, s['latitude'], s['longitude'])
print(dist, s['name'])
if dist < minDist :
minDist = dist
name = s['name']
return name
def count_near(loc_lst, k):
cur = loc_lst[0]
visit = [0] * len(loc_lst)
visit[0] = 1
for i in range(len(loc_lst) - 1):
if ctp17hw1.dist(cur[0], cur[1], loc_lst[i + 1][0],
loc_lst[i + 1][1]) <= k and visit[i + 1] == 0:
visit[i + 1] = 1
visit = count_helper(loc_lst, i + 1, visit, k)
return sum(visit) - 1
def shortest_route(loc_lst, my_loc):
visit = [0]*len(loc_lst)
result = -1
for i in range(len(loc_lst)):
l = dfs(visit, i, loc_lst, my_loc) + ctp17hw1.dist(loc_lst[i][0],loc_lst[i][1],my_loc[0],my_loc[1])
if result == -1:
result = l
else:
result = min(l, result)
return result
def dfs(visit, start, loc_lst, my_loc):
result = -1
visit[start] = 1
if sum(visit) == len(visit):
visit[start] = 0
return ctp17hw1.dist(loc_lst[start][0],loc_lst[start][1],my_loc[0],my_loc[1])
for i in range(len(loc_lst)):
if visit[i] == 0:
l = ctp17hw1.dist(loc_lst[start][0],loc_lst[start][1],loc_lst[i][0],loc_lst[i][1])+\
dfs(visit, i, loc_lst, my_loc)
if result == -1:
result = l
else:
result = min(l, result)
visit[start] = 0
return result
def suggest_similar(lst, loc_lst, d, k):
const = 1
result = []
str_list = list(map(lambda x: x[2], lst))
filtered_list = filtering(str_list, loc_lst)
for name in str_list:
if str_list.count(name) == 1:
cur_loc = find_loc(name, loc_lst)
for loc in filtered_list:
print(name, loc[2])
print(
ctp17hw1.dist(cur_loc[0] / const, cur_loc[1] / const,
loc[0] / const, loc[1] / const))
print(similarity(cur_loc, loc))
if ctp17hw1.dist(
cur_loc[0] / const, cur_loc[1] / const, loc[0] / const,
loc[1] / const) < d and similarity(cur_loc, loc) >= k:
if not loc[2] in result:
result.append(loc[2])
return result
def count_helper(loc_lst, cur, visit, k):
for i in range(len(loc_lst) - 1):
if ctp17hw1.dist(loc_lst[cur][0], loc_lst[cur][1], loc_lst[i + 1][0],
loc_lst[i + 1][1]) <= k and visit[i + 1] == 0:
visit[i + 1] = 1
visit = count_helper(loc_lst, i + 1, visit, k)
return visit
# print(ctp17hw1.dist(0.4,0.5,0.4,0.6))
#
# print(count_near([(0.3,0.2),(0.4,0.),(0.3,0.3),(0.4,0.6)],25000))5
def total_dist(lst):
distant = 0
for i in range(len(lst) - 1):
distant += ctp17hw1.dist(lst[i]['latitude'], lst[i]['longitude'], lst[i + 1]['latitude'], lst[i + 1]['longitude'])
return distant
# test example; should be erased before submitting.
# loc1 = {'latitude':1.2, 'longitude':3.4, 'timestamp':100}
# loc3 = {'latitude':9.1, 'longitude':2.3, 'timestamp':200}
# loc2 = {'latitude':5.6, 'longitude':7.8, 'timestamp':300}
# print(total_dist([loc1, loc3, loc2]))
# print(ctp17hw1.dist(loc1['latitude'], \
# loc1['longitude'], \
# loc2['latitude'], \
# loc2['longitude']))
# print(union)
intersection = []
keywords1 = loc1[3][:]
keywords2 = loc2[3][:]
for i in keywords1:
if i in keywords2:
a = keywords1.count(i)
b = keywords2.count(i)
for _ in range(b):
keywords2.remove(i)
intersection += [i] * min(a, b)
# print(intersection)
print(intersection, union)
print(len(intersection) / len(union))
return len(intersection) / len(union)
print(1 / 2)
print(ctp17hw1.dist(1.1, 1.1, 1.7, 1.7))
loc_list = [(1.1, 5.0 , "seoul", ["agd","ab"]), \
(1.7, 5.0, "newyork", ["agd", "ab"]),
(90.0, 5.0, "cafe", ["agd", "ab"]),
(90.0, 5.0, "home", ["agd", "ab"]),
(90.0, 5.0, "bus", ["agd", "ab"]),
(1.6, 5.0, "lib", ["agd", "ab"]),
(1.4, 5.0, "usa", ["agd","ab"]),
(1.7, 5.0 , "europe", ["agd"])]
print(suggest_similar([(1,5,"home"),(7,9,"home"), (11,17,"school"),(20, 25, "school"),(26,40,"cafe"),(41,57,"lib"),(58,59,"bus"),\
(59,60,"seoul"),(61,62,"seoul"),(63,64,"seoul")],loc_list,40000,0.2))