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August 28, 2019 06:36
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def brute_force_knapsack(x_weights, x_prices, x_capacity): | |
item_count = x_weights.shape[0] | |
picks_space = 2 ** item_count | |
best_price = -1 | |
best_picks = np.zeros(item_count) | |
for p in range(picks_space): | |
picks = [int(c) for c in f"{p:0{item_count}b}"] | |
price = np.dot(x_prices, picks) | |
weight = np.dot(x_weights, picks) | |
if weight <= x_capacity and price > best_price: | |
best_price = price | |
best_picks = picks | |
return best_picks | |
def create_knapsack(item_count=5): | |
x_weights = np.random.randint(1, 45, item_count) | |
x_prices = np.random.randint(1, 99, item_count) | |
x_capacity = np.random.randint(1, 99) | |
y = brute_force_knapsack(x_weights, x_prices, x_capacity) | |
return x_weights, x_prices, x_capacity, y |
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