Mon Apr 14 09:59:24 2025

knapsack_brute_test():
  python version: 3.10.12
  numpy version:  1.26.4
  Test knapsack_brute()

subset_next_test():
  Test subset_next()

  Generate in order subsets of size  5

[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 1.]
[0. 0. 0. 1. 0.]
[0. 0. 0. 1. 1.]
[0. 0. 1. 0. 0.]
[0. 0. 1. 0. 1.]
[0. 0. 1. 1. 0.]
[0. 0. 1. 1. 1.]
[0. 1. 0. 0. 0.]
[0. 1. 0. 0. 1.]
[0. 1. 0. 1. 0.]
[0. 1. 0. 1. 1.]
[0. 1. 1. 0. 0.]
[0. 1. 1. 0. 1.]
[0. 1. 1. 1. 0.]
[0. 1. 1. 1. 1.]
[1. 0. 0. 0. 0.]
[1. 0. 0. 0. 1.]
[1. 0. 0. 1. 0.]
[1. 0. 0. 1. 1.]
[1. 0. 1. 0. 0.]
[1. 0. 1. 0. 1.]
[1. 0. 1. 1. 0.]
[1. 0. 1. 1. 1.]
[1. 1. 0. 0. 0.]
[1. 1. 0. 0. 1.]
[1. 1. 0. 1. 0.]
[1. 1. 0. 1. 1.]
[1. 1. 1. 0. 0.]
[1. 1. 1. 0. 1.]
[1. 1. 1. 1. 0.]
[1. 1. 1. 1. 1.]

knapsack_brute_test01():
  knapsack_brute() uses a brute force approach.
  Maximize profit without exceeding weight limit.

  Problem  6
  Weight limit is  170
  Number of subsets is  128
   Item 0/1  Value  Weight  Value/Weight

      0   0    442     41    10.78
      1   1    525     50    10.50
      2   0    511     49    10.43
      3   1    593     59    10.05
      4   0    546     55     9.93
      5   0    564     57     9.89
      6   1    617     60    10.28

  Taken   3   1735    169    10.27

knapsack_brute_test():
  Normal end of execution.
Mon Apr 14 09:59:24 2025