A-M baby

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

@@ -0,0 +1,57 @@
+items = (
+	("map",		9,	150,	1),
+	("compass",	13,	35,	1),
+	("water",	153,	200,	3),
+	("sandwich",	50,	60,	2),
+	("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),
+)
+
+#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 = items[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)
+
+    cache[k] = [best_v, best_list]
+    return best_v, best_list
+
+
+v, lst = choose_item(400, len(items) - 1, {})
+w = 0
+for i, cnt in enumerate(lst):
+    if cnt > 0:
+        print cnt, items[i][0]
+        w = w + items[i][1] * cnt
+
+print "Total weight:", w, "Value:", v