def test_get_maximum_value(self):
item1, item2, item3 = Item(60, 10), Item(100, 20), Item(120, 30)
self.assertEqual(220, get_maximum_value([item1, item2, item3], 50))
item1, item2, item3, item4 = Item(60,
5), Item(50,
3), Item(70,
4), Item(30, 2)
self.assertEqual(80, get_maximum_value([item1, item2, item3, item4],
5))
def test_get_maximum_value(self):
item1, item2, item3 = Item(60, 10), Item(100, 20), Item(120, 30)
self.assertEqual(220, get_maximum_value([item1, item2, item3], 50))
item1, item2, item3, item4 = Item(60, 5), Item(50, 3), Item(70, 4), Item(30, 2)
self.assertEqual(80, get_maximum_value([item1, item2, item3, item4], 5))
""" Given the capacity of the knapsack and items specified by weights and values, return the maximum summarized value of the items that can be fit in the knapsack. Example: capacity = 5, items(value, weight) = [(60, 5), (50, 3), (70, 4), (30, 2)] result = 80 (items valued 50 and 30 can both be fit in the knapsack) The time complexity is O(n * m) and the space complexity is O(m), where n is the total number of items and m is the knapsack's capacity. """ from algorithms.dp import get_maximum_value,Item capacity = 5 #items = [[60, 5], [50, 3], [70, 4], [30, 2]] #(value, weight) items=[Item(60,5),Item(50,3),Item(70,4),Item(30,2)] print(get_maximum_value(items,capacity))