@@ -86,9 +86,9 @@ def bootstrap_confidence_interval(data, num_samples=1000, confidence_level=0.95)
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mean = np .mean (means )
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return mean , lower_bound , upper_bound
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- def calculate_confidence_interval (data ):
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+ def calculate_confidence_interval (data , min_is_best = True ):
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mean , lower , upper = bootstrap_confidence_interval (data )
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- min_value = np .min (data )
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+ min_value = np .min (data ) if min_is_best else np . max ( data )
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return mean , (upper - lower ) / 2 , min_value
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@@ -117,7 +117,7 @@ def main():
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total_time , total_ci , total_best = calculate_confidence_interval (np .sum (all_times , axis = 1 ))
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- 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 ))
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+ 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 )
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min_time , min_ci , _ = calculate_confidence_interval (np .min (all_times , axis = 1 ))
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mean_time , mean_ci , _ = calculate_confidence_interval (np .mean (all_times , axis = 1 ))
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median_time , median_ci , _ = calculate_confidence_interval (np .median (all_times , axis = 1 ))
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