fake_world

ml2

Dec 3rd, 2020
418
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  1.  
  2. import numpy as np
  3. import pandas as pd
  4. data = pd.DataFrame(data=pd.read_csv('enjoysport.csv'))
  5. concepts = np.array(data.iloc[:,0:-1])
  6. print(concepts)
  7. target = np.array(data.iloc[:,-1])
  8. print(target)
  9.  
  10.  
  11. def learn(concepts, target):
  12.     specific_h = concepts[0].copy()
  13.     print("initialization of specific_h and general_h")
  14.     print(specific_h)
  15.     general_h = [["?" for i in range(len(specific_h))] for i in range(len(specific_h))]
  16.     print(general_h)
  17.     for i, h in enumerate(concepts):
  18.         if target[i] == "yes":
  19.             for x in range(len(specific_h)):
  20.                 if h[x]!= specific_h[x]:
  21.                     specific_h[x] ='?'
  22.                     general_h[x][x] ='?'
  23.                 print(specific_h)
  24.         print(specific_h)
  25.         if target[i] == "no":
  26.             for x in range(len(specific_h)):
  27.                 if h[x]!= specific_h[x]:
  28.                     general_h[x][x] = specific_h[x]
  29.                 else:
  30.                     general_h[x][x] = '?'
  31.         print(" steps of Candidate Elimination Algorithm",i+1)
  32.         print(specific_h)
  33.         print(general_h)
  34.     indices = [i for i, val in enumerate(general_h) if val == ['?', '?', '?', '?', '?', '?']]
  35.     for i in indices:
  36.         general_h.remove(['?', '?', '?', '?', '?', '?'])
  37.     return specific_h, general_h
  38. s_final, g_final = learn(concepts, target)
  39. print("Final Specific_h:", s_final, sep="\n")
  40. print("Final General_h:", g_final, sep="\n")
  41.  
  42.  
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