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Jan 2nd, 2020
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  1. import pandas as pd
  2. from random import randint, sample
  3.  
  4. # Generate data
  5. list_list = []
  6. for i in range(20):
  7. for j in range(20):
  8. if j == i:
  9. continue
  10. temp_list = [20] * 20
  11. temp_list[i] = 19
  12. temp_list[j] = 21
  13. list_list = [temp_list] + list_list
  14.  
  15. df_dict = {}
  16. for i in range(20):
  17. df_dict['col_' + str(i)] = [temp_list[i] for temp_list in list_list]
  18.  
  19. df = pd.DataFrame(df_dict)
  20.  
  21. # Loop through data finding solutions
  22. rand_rounds = 50
  23. max_on_one_side = 5
  24. min_on_one_side = 2
  25. index_sample = ['col_' + str(i) for i in list(range(20))]
  26. junk = ['left_sum', 'right_sum', 'result']
  27. one_best = df.shape[0]
  28.  
  29. def split_list(a_list):
  30. """returns first half and second half of a list"""
  31. half = len(a_list)//2
  32. return a_list[:half], a_list[half:]
  33.  
  34. def get_score(solutions_df, index_left, index_right):
  35. """Lower scores are better. Score returned is the maximum solutions remaining
  36. of the three outcomes of a particular weighing."""
  37. solutions_df['left_sum'] = solutions_df[index_left].sum(axis=1)
  38. solutions_df['right_sum'] = solutions_df[index_right].sum(axis=1)
  39. solutions_df['result'] = '=='
  40. solutions_df.loc[solutions_df['left_sum'] > solutions_df['right_sum'], 'result'] = '>'
  41. solutions_df.loc[solutions_df['left_sum'] < solutions_df['right_sum'], 'result'] = '<'
  42. return solutions_df.result.value_counts().max()
  43.  
  44. # Notes
  45. # After round 1 we should have 125-130
  46. # After round 2 we should have < 44
  47. # After round 3 we should have < 15
  48. # After round 4 we should have < 7
  49. # After round 5 we should have < 3
  50. # After round 6 we should be done
  51.  
  52. result = {
  53. 'step_one_left':[],
  54. 'step_one_right':[],
  55. 'step_one_path':[],
  56. 'step_one_count':[],
  57. 'step_two_left':[],
  58. 'step_two_right':[],
  59. 'step_two_path':[],
  60. 'step_two_count':[],
  61. 'step_three_left':[],
  62. 'step_three_right':[],
  63. 'step_three_path':[],
  64. 'step_three_count':[],
  65. 'step_four_left':[],
  66. 'step_four_right':[],
  67. 'step_four_path':[],
  68. 'step_four_count':[],
  69. 'step_five_left':[],
  70. 'step_five_right':[],
  71. 'step_five_path':[],
  72. 'step_five_count':[],
  73. 'step_six_left':[],
  74. 'step_six_right':[],
  75. 'step_six_path':[],
  76. 'step_six_count':[],
  77. }
  78. results_df = pd.DataFrame(result)
  79.  
  80. for rand in range(rand_rounds):
  81. n = randint(min_on_one_side, max_on_one_side)
  82. index_left, index_right = split_list(sample(index_sample, n*2))
  83. new_score = get_score(df, index_left, index_right)
  84. if new_score < one_best:
  85. one_left, one_right = index_left, index_right
  86. print("Round 1: "+str(one_best)+" to "+str(new_score)+" after "+str(rand)+" rounds.")
  87. one_best = new_score
  88. one_df_final = df.copy()
  89. for i in ['==','<','>']:
  90. print("Beginning round two optimization for case: Left " + i + " Right.")
  91. two_df = one_df_final.copy()
  92. two_df = two_df.loc[two_df.result==i].reset_index(drop=True)
  93. two_best = two_df.shape[0]
  94. for rand in range(rand_rounds):
  95. if i != '==':
  96. break
  97. n = randint(min_on_one_side, max_on_one_side)
  98. index_left, index_right = split_list(sample(index_sample, n*2))
  99. new_score = get_score(two_df, index_left, index_right)
  100. if new_score < two_best:
  101. two_left, two_right = index_left, index_right
  102. print("Round 2: "+str(two_best)+" to "+str(new_score)+" after "+str(rand)+" rounds.")
  103. two_best = new_score
  104. two_df_final = two_df.copy()
  105. for j in ['==','<','>']:
  106. print("Beginning round three optimization for case: Left " + j + " Right.")
  107. three_df = two_df_final.copy()
  108. three_df = three_df.loc[three_df.result==j].reset_index(drop=True)
  109. three_best = three_df.shape[0]
  110. for rand in range(rand_rounds):
  111. if j != '==':
  112. break
  113. n = randint(min_on_one_side, max_on_one_side)
  114. index_left, index_right = split_list(sample(index_sample, n*2))
  115. new_score = get_score(three_df, index_left, index_right)
  116. if new_score < three_best:
  117. three_left, three_right = index_left, index_right
  118. print("Round 3: "+str(three_best)+" to "+str(new_score)+" after "+str(rand)+" rounds.")
  119. three_best = new_score
  120. three_df_final = three_df.copy()
  121. for k in ['==','<','>']:
  122. print("Beginning round four optimization for case: Left " + k + " Right.")
  123. four_df = three_df_final.copy()
  124. four_df = four_df.loc[four_df.result==k].reset_index(drop=True)
  125. four_best = four_df.shape[0]
  126. for rand in range(rand_rounds):
  127. if k != '==':
  128. break
  129. n = randint(min_on_one_side, max_on_one_side)
  130. index_left, index_right = split_list(sample(index_sample, n*2))
  131. new_score = get_score(four_df, index_left, index_right)
  132. if new_score < four_best:
  133. four_left, four_right = index_left, index_right
  134. print("Round 4: "+str(four_best)+" to "+str(new_score)+" after "+str(rand)+" rounds.")
  135. four_best = new_score
  136. four_df_final = four_df.copy()
  137. for l in ['==','<','>']:
  138. print("Beginning round five optimization for case: Left " + l + " Right.")
  139. five_df = four_df_final.copy()
  140. five_df = five_df.loc[five_df.result==l].reset_index(drop=True)
  141. five_best = five_df.shape[0]
  142. for rand in range(rand_rounds):
  143. if l != '==':
  144. break
  145. n = randint(min_on_one_side, max_on_one_side)
  146. index_left, index_right = split_list(sample(index_sample, n*2))
  147. new_score = get_score(five_df, index_left, index_right)
  148. if new_score < five_best:
  149. five_left, five_right = index_left, index_right
  150. print("Round 5: "+str(five_best)+" to "+str(new_score)+" after "+str(rand)+" rounds.")
  151. five_best = new_score
  152. five_df_final = five_df.copy()
  153. for m in ['==','<','>']:
  154. print("Beginning round six optimization for case: Left " + m + " Right.")
  155. six_df = five_df_final.copy()
  156. six_df = six_df.loc[six_df.result==m].reset_index(drop=True)
  157. six_best = six_df.shape[0]
  158. for rand in range(rand_rounds):
  159. if m != '==':
  160. break
  161. n = randint(min_on_one_side, max_on_one_side)
  162. index_left, index_right = split_list(sample(index_sample, n*2))
  163. new_score = get_score(six_df, index_left, index_right)
  164. if new_score < six_best:
  165. six_left, six_right = index_left, index_right
  166. print("Round 6: "+str(six_best)+" to "+str(new_score)+" after "+str(rand)+" rounds.")
  167. six_best = new_score
  168. six_df_final = six_df.copy()
  169. for n in ['==','<','>']:
  170. final_df = six_df_final.copy()
  171. final_df = final_df.loc[final_df.result==n].reset_index(drop=True)
  172. result = {
  173. 'step_one_left':one_left,
  174. 'step_one_right':one_right,
  175. 'step_one_path':"Left " + i + " Right",
  176. 'step_one_count':two_df.shape[0],
  177. 'step_two_left':two_left,
  178. 'step_two_right':two_right,
  179. 'step_two_path':"Left " + j + " Right",
  180. 'step_two_count':three_df.shape[0],
  181. 'step_three_left':three_left,
  182. 'step_three_right':three_right,
  183. 'step_three_path':"Left " + k + " Right",
  184. 'step_three_count':four_df.shape[0],
  185. 'step_four_left':four_left,
  186. 'step_four_right':four_right,
  187. 'step_four_path':"Left " + l + " Right",
  188. 'step_four_count':five_df.shape[0],
  189. 'step_five_left':five_left,
  190. 'step_five_right':five_right,
  191. 'step_five_path':"Left " + m + " Right",
  192. 'step_five_count':six_df.shape[0],
  193. 'step_six_left':six_left,
  194. 'step_six_right':six_right,
  195. 'step_six_path':"Left " + n + " Right",
  196. 'step_six_count':final_df.shape[0],
  197. }
  198. results_df = results_df.append(result, ignore_index=True)
  199.  
  200. results_df.to_csv('brute_force_results.csv', index=False)
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