Mon Nov 18 17:44:44 2024

knapsack_random_test():
  python version: 3.10.12
  numpy version:  1.26.4
  Test knapsack_random()

subset_random_test():
  Test subset_random()

  Subsets will be of size n =  5

6 : [0 0 1 1 0]
19 : [1 0 0 1 1]
7 : [0 0 1 1 1]
20 : [1 0 1 0 0]
5 : [0 0 1 0 1]
18 : [1 0 0 1 0]
18 : [1 0 0 1 0]
20 : [1 0 1 0 0]
13 : [0 1 1 0 1]
23 : [1 0 1 1 1]

knapsack_random_test01():
  knapsack_random() random selects a subset of n items
  and considers it as a solution to a knapsack problem.
  Maximize profit without exceeding weight limit.

  Weight limit is  26
  Number of items is  5
  Value array  [24 13 23 15 16]
  Weight array  [12  7 11  8  9]

  Selected items: [1 1 0 0 0]
  Weight  19
  Value  37
  Ratio  1.9473684210526316

  Selected items: [0 1 1 1 0]
  Weight  26
  Value  51
  Ratio  1.9615384615384615

  Selected items: [1 1 1 0 1]
  Weight  39  exceeds weight limit  26

  Selected items: [1 1 0 1 1]
  Weight  36  exceeds weight limit  26

  Selected items: [0 0 1 0 0]
  Weight  11
  Value  23
  Ratio  2.090909090909091

  Selected items: [1 0 1 1 0]
  Weight  31  exceeds weight limit  26

  Selected items: [1 1 0 0 0]
  Weight  19
  Value  37
  Ratio  1.9473684210526316

  Selected items: [0 1 1 1 0]
  Weight  26
  Value  51
  Ratio  1.9615384615384615

  Selected items: [0 0 0 1 1]
  Weight  17
  Value  31
  Ratio  1.8235294117647058

  Selected items: [0 0 0 1 0]
  Weight  8
  Value  15
  Ratio  1.875

knapsack_random_test():
  Normal end of execution.
Mon Nov 18 17:44:44 2024