Implement dynamic programming solution for Knapsack.

knapsack/solver.py

@@ -1,42 +1,17 @@
 #!/usr/bin/python
 # -*- coding: utf-8 -*-
 
-from collections import namedtuple
-Item = namedtuple("Item", ['index', 'value', 'weight'])
+import knapsack
+
 
 def solve_it(input_data):
     # Modify this code to run your optimization algorithm
-
-    # parse the input
-    lines = input_data.split('\n')
-
-    firstLine = lines[0].split()
-    item_count = int(firstLine[0])
-    capacity = int(firstLine[1])
-
-    items = []
-
-    for i in range(1, item_count+1):
-        line = lines[i]
-        parts = line.split()
-        items.append(Item(i-1, int(parts[0]), int(parts[1])))
-
-    # a trivial greedy algorithm for filling the knapsack
-    # it takes items in-order until the knapsack is full
-    value = 0
-    weight = 0
-    taken = [0]*len(items)
-
-    for item in items:
-        if weight + item.weight <= capacity:
-            taken[item.index] = 1
-            value += item.value
-            weight += item.weight
-    
-    # prepare the solution in the specified output format
-    output_data = str(value) + ' ' + str(0) + '\n'
-    output_data += ' '.join(map(str, taken))
-    return output_data
+    k = knapsack.input_data_to_knapsack(input_data)
+    if k.count * k.capacity < 50000000:
+        r = knapsack.solve_knapsack_dynamic(k)
+    else:
+        r = knapsack.solve_knapsack_greedy(k)
+    return knapsack.result_to_output_data(r)
 
 
 if __name__ == '__main__':
@@ -47,5 +22,7 @@ if __name__ == '__main__':
             input_data = input_data_file.read()
         print(solve_it(input_data))
     else:
-        print('This test requires an input file.  Please select one from the data directory. (i.e. python solver.py ./data/ks_4_0)')
+        print("This test requires an input file.  "
+              "Please select one from the data directory. "
+              "(i.e. python solver.py ./data/ks_4_0)")