input: Write a function to solve a knapsack problem using dynamic programming. The function should take the capacity of the knapsack, the list of item weights, and the list of item values as inputs, and return the maximum value that can be obtained.
  
  def knapSack(W, wt, val, n):
      if n == 0 or W == 0:
          return 0
      if wt[n-1] > W:
          return knapSack(W, wt, val, n-1)
      else:
          return max(val[n-1] + knapSack(W - wt[n-1], wt, val, n-1), knapSack(W, wt, val, n-1))
  
  assert knapSack(50, [10, 20, 30], [60, 100, 120], 3) == 220
  assert knapSack(30, [10, 20, 30], [60, 100, 120], 3) == 160
  assert knapSack(10, [5, 4, 6, 3], [10, 40, 30, 50], 4) == 90
  assert knapSack(0, [10, 20, 30], [60, 100, 120], 3) == 0
  assert knapSack(50, [], [], 0) == 0
output: def knapSack(W, wt, val, n):
      dp = [[0 for _ in range(W + 1)] for _ in range(n + 1)]
      for i in range(n + 1):
          for w in range(W + 1):
              if i == 0 or w == 0:
                  dp[i][w] = 0
              elif wt[i-1] <= w:
                  dp[i][w] = max(val[i-1] + dp[i-1][w-wt[i-1]], dp[i-1][w])
              else:
                  dp[i][w] = dp[i-1][w]
      return dp[n][W]