@@ -86,9 +86,9 @@ def bootstrap_confidence_interval(data, num_samples=1000, confidence_level=0.95)
8686 mean = np .mean (means )
8787 return mean , lower_bound , upper_bound
8888
89- def calculate_confidence_interval (data ):
89+ def calculate_confidence_interval (data , min_is_best = True ):
9090 mean , lower , upper = bootstrap_confidence_interval (data )
91- min_value = np .min (data )
91+ min_value = np .min (data ) if min_is_best else np . max ( data )
9292 return mean , (upper - lower ) / 2 , min_value
9393
9494
@@ -117,7 +117,7 @@ def main():
117117
118118
119119 total_time , total_ci , total_best = calculate_confidence_interval (np .sum (all_times , axis = 1 ))
120- ops_per_sec , ops_per_sec_ci , ops_per_sec_best = calculate_confidence_interval (float (all_times .shape [1 ]) / np .sum (all_times / 1000 , axis = 1 ))
120+ ops_per_sec , ops_per_sec_ci , ops_per_sec_best = calculate_confidence_interval (float (all_times .shape [1 ]) / np .sum (all_times / 1000 , axis = 1 ), min_is_best = False )
121121 min_time , min_ci , _ = calculate_confidence_interval (np .min (all_times , axis = 1 ))
122122 mean_time , mean_ci , _ = calculate_confidence_interval (np .mean (all_times , axis = 1 ))
123123 median_time , median_ci , _ = calculate_confidence_interval (np .median (all_times , axis = 1 ))
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