RosettaCodeData/Task/Knapsack-problem-Bounded/Python/knapsack-problem-bounded-3.py

60 lines
1.4 KiB
Python

items = {
"sandwich": (50, 60, 2),
"map": (9, 150, 1),
"compass": (13, 35, 1),
"water": (153, 200, 3),
"glucose": (15, 60, 2),
"tin": (68, 45, 3),
"banana": (27, 60, 3),
"apple": (39, 40, 3),
"cheese": (23, 30, 1),
"beer": (52, 10, 3),
"suntan cream": (11, 70, 1),
"camera": (32, 30, 1),
"t-shirt": (24, 15, 2),
"trousers": (48, 10, 2),
"umbrella": (73, 40, 1),
"w-trousers": (42, 70, 1),
"w-overcoat": (43, 75, 1),
"note-case": (22, 80, 1),
"sunglasses": (7, 20, 1),
"towel": (18, 12, 2),
"socks": (4, 50, 1),
"book": (30, 10, 2),
}
item_keys = list(items.keys())
#cache: could just use memoize module, but explicit caching is clearer
def choose_item(weight, idx, cache):
if idx < 0: return 0, []
k = (weight, idx)
if k in cache: return cache[k]
name, w, v, qty = item_keys[idx], *items[item_keys[idx]]
best_v, best_list = 0, []
for i in range(0, qty + 1):
wlim = weight - i * w
if wlim < 0: break
val, taken = choose_item(wlim, idx - 1, cache)
if val + i * v > best_v:
best_v = val + i * v
best_list = taken[:]
best_list.append((i, name))
cache[k] = [best_v, best_list]
return best_v, best_list
v, lst = choose_item(400, len(items) - 1, {})
w = 0
for cnt, name in lst:
if cnt > 0:
print(cnt, name)
w = w + items[name][0] * cnt
print("Total weight:", w, "Value:", v)