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kristery May 22nd, 2019 80 Never
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  1. import os
  2. import warnings
  3. import numpy as np
  4. from scipy import stats
  5.  
  6.  
  7. significant_level = 0.05 # five percent significant level for one-sided t-test
  8.  
  9. def hypothesis_test(*results):
  10.     signif = [False] * len(results)
  11.  
  12.     # choose the index with the maximum mean
  13.     i_best = np.argmax([result.mean() for result in results])
  14.  
  15.     for i in range(len((results))):
  16.         if i == i_best:
  17.             signif[i] = True
  18.         else:
  19.             signif[i] = [0]*len(results[i])
  20.             if not np.array_equal(results[i], results[i_best]):
  21.                 t_stat, p_ = stats.ttest_ind(results[i], results[i_best])
  22.                 p = 0.5 * p_  # two-sided p-value -> one-sided p-value
  23.                 signif[i] = p > significant_level
  24.  
  25.     return signif
  26.  
  27. # score of each method with 5 trials
  28. method_1_scores = np.array([10, 11, 12, 14, 16])
  29. method_2_scores = np.array([14, 21, 20, 17, 12])
  30. method_3_scores = np.array([14, 15, 11, 14, 12])
  31.  
  32. results = np.array([method_1_scores, method_2_scores, method_3_scores])
  33. print(results)
  34. print(hypothesis_test(*results))
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