input: Write a function to find the maximum value that can be obtained by selecting items with given values and weights for a knapsack with a specified capacity using dynamic programming. The function should take the capacity of the knapsack, a list of item values, and a list of item weights as input, and return the maximum value.
  
  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(10, [5, 4, 6, 3], [10, 40, 30, 50], 4) == 90
  assert knapSack(0, [10, 20], [40, 100], 2) == 0
  assert knapSack(15, [10, 20], [60, 100], 2) == 60
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]
  