Solution for complete knapsack problem.

backpack.py

costs = [3,5,9]
value = [5,9,16]

volume = 1303

# solutions
opts = set()
opts.add(tuple([0]))

# calc total value
cost_val = dict(zip(costs, value))

def total_value(opt):
    return sum([cost_val.get(cost, 0) for cost in opt])

def possible_solutions():
    solutions = set()
    for opt in opts:
        for cost in costs:
            if cost + sum(opt) > volume:
                continue
            cnt = (volume - sum(opt)) // cost
            for _ in range(cnt + 1):
                sol = tuple(list(opt) + [cost] * _)
                solutions.add(sol)
    return solutions

def optimize_max_return(opts):
    cur = list(opts)[0]
    for sol in opts:
        if total_value(sol) > total_value(cur):
            cur = sol
    return cur

while sum(optimize_max_return(opts)) <= volume - min(costs):
    opts = opts.union(possible_solutions())

print(optimize_max_return(opts), total_value(optimize_max_return(opts)))