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Update knapsack.py (#7271)

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* Update knapsack.py

* Update dynamic_programming/knapsack.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* Update knapsack.py

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Co-authored-by: Christian Clauss <cclauss@me.com>
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Nadirhan Şahin 2022-10-23 22:28:11 +03:00 committed by GitHub
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dynamic_programming/knapsack.py
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@ -1,9 +1,9 @@
"""
Given weights and values of n items, put these items in a knapsack of
capacity W to get the maximum total value in the knapsack.
capacity W to get the maximum total value in the knapsack.
Note that only the integer weights 0-1 knapsack problem is solvable
using dynamic programming.
using dynamic programming.
"""
@ -27,7 +27,7 @@ def mf_knapsack(i, wt, val, j):
def knapsack(w, wt, val, n):
dp = [[0 for i in range(w + 1)] for j in range(n + 1)]
dp = [[0] * (w + 1) for _ in range(n + 1)]
for i in range(1, n + 1):
for w_ in range(1, w + 1):
@ -108,7 +108,7 @@ def _construct_solution(dp: list, wt: list, i: int, j: int, optimal_set: set):
dp: list of list, the table of a solved integer weight dynamic programming problem
wt: list or tuple, the vector of weights of the items
i: int, the index of the item under consideration
i: int, the index of the item under consideration
j: int, the current possible maximum weight
optimal_set: set, the optimal subset so far. This gets modified by the function.
@ -136,7 +136,7 @@ if __name__ == "__main__":
wt = [4, 3, 2, 3]
n = 4
w = 6
f = [[0] * (w + 1)] + [[0] + [-1 for i in range(w + 1)] for j in range(n + 1)]
f = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
optimal_solution, _ = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w