def knapsack_with_solutions(n, g, weights, values): dp = [[0] * (g + 1) for _ in range(n + 1)] count = [[0] * (g + 1) for _ in range(n + 1)] for i in range(n + 1): count[i][0] = 1 for i in range(1, n + 1): for j in range(g + 1): dp[i][j] = dp[i - 1][j] count[i][j] = count[i - 1][j] if j >= weights[i - 1]: if dp[i][j] < dp[i - 1][j - weights[i - 1]] + values[i - 1]: dp[i][j] = dp[i - 1][j - w

This implementation of the knapsack problem uses dynamic programming to find the maximum value that can be obtained by selecting items from a given array of weights and values, subject to a maximum weight constraint. The dp table stores the maximum value that can be obtained for each weight limit up to g, considering the first i items. The count table stores the number of ways to achieve the maximum value for each weight limit up to g, considering the first i items. The algorithm first initializes the count table by setting count[i][0] = 1 for all i, as there is always one way to achieve a weight limit of 0. Then, for each item i and weight limit j, the algorithm checks if adding item i to the knapsack would result in a higher value than not adding it. If so, it updates dp[i][j] and count[i][j] with the maximum value and number of ways, respectively. Finally, the algorithm returns the maximum value and the number of ways to achieve it for the weight limit g. Note: The implementation is incomplete as it is cut off at the line "dp[i][j] = dp[i - 1][j - w". The code seems to have been cut off accidentally. [2024-06-09 18:21:03 | AI写代码神器 | 760点数解答]