def solve_antonio_sampling(p: Problem, input):
s = Solution(p)
myset = set(input)
tail = myset.pop()
head = myset.pop()
s.add_any(head[0])
s.add_any(tail[0])
while (len(myset) > 0):
tmp_max_tail = -1
selected_item_tail = None
tmp_max_head = -1
selected_item_head = None
testset = random.sample(myset, min(iteration, len(myset)))
for val in testset:
tmp_tail = computescore(tail, val)
tmp_head = computescore(head, val)
if (tmp_tail >= tmp_max_head):
tmp_max_tail = tmp_tail
selected_item_tail = val
if (tmp_head >= tmp_max_head):
tmp_max_head = tmp_head
selected_item_head = val
if tmp_max_tail > tmp_max_head:
tail = selected_item_tail
s.add_any(selected_item_tail[0])
myset.remove(selected_item_tail)
else:
head = selected_item_head
s.add_any_head(selected_item_head[0])
myset.remove(selected_item_head)
return s
def solve_antonio_sequential(p: Problem, input):
s = Solution(p)
myset = set(input)
current = myset.pop()
s.add_any(current[0])
while (len(myset) > 0):
tmp_max = -1
selected_item = None
testset = random.sample(myset, min(iteration, len(myset)))
for i, val in enumerate(myset):
if (i > 50):
break
tmp = computescore(current, val)
if (tmp >= tmp_max):
tmp_max = tmp
selected_item = val
current = selected_item
s.add_any(selected_item[0])
myset.remove(selected_item)
return s
def solve_random(p):
random.shuffle(p.result_images)
s = Solution(p)
for idx, tags in p.result_images:
s.add_any(idx)
return s