some pep8 cleanup too

Author: maxwell-aladago

dynamic_programming/knapsack.py

@@ -1,21 +1,25 @@
 """
-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.
+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.
 
-Note that only the integer weights 0-1 knapsack problem is solvable using dynamic programming.
+Note that only the integer weights 0-1 knapsack problem is solvable
+ using dynamic programming.
 """
 
-def MF_knapsack(i,wt,val,j):
+
+def MF_knapsack(i, wt, val, j):
     '''
     This code involves the concept of memory functions. Here we solve the subproblems which are needed
     unlike the below example
     F is a 2D array with -1s filled up
     '''
     global F  # a global dp table for knapsack
     if F[i][j] < 0:
-        if j < wt[i - 1]:
-            val = MF_knapsack(i - 1,wt,val,j)
+        if j < wt[i-1]:
+            val = MF_knapsack(i-1, wt, val, j)
         else:
-            val = max(MF_knapsack(i - 1,wt,val,j),MF_knapsack(i - 1,wt,val,j - wt[i - 1]) + val[i - 1])
+            val = max(MF_knapsack(i-1, wt, val, j),
+                      MF_knapsack(i-1, wt, val, j - wt[i-1]) + val[i-1])
         F[i][j] = val
     return F[i][j]
 
@@ -24,9 +28,9 @@ def knapsack(W, wt, val, n):
     dp = [[0 for i in range(W+1)]for j in range(n+1)]
 
     for i in range(1,n+1):
-        for w in range(1,W+1):
-            if(wt[i-1]<=w):
-                dp[i][w] = max(val[i-1]+dp[i-1][w-wt[i-1]],dp[i-1][w])
+        for w in range(1, W+1):
+            if 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]