algorithm_recommender/test_results_explanation.txt at master · ivanho-git/algorithm_recommender · GitHub

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======================================================================
TEST 1: Small random array (n=10)
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Algorithm: insertion_sort
Has justification: True
Explanation (first 300 chars): Array is small array → Insertion Sort selected (efficient for small/sorted inputs).<br><br><strong>Why Insertion Sort despite higher theoretical operations?</strong><br>For n = 10, theoretical estimates are n log n ≈ 33 vs n² ≈ 100. However, asymptotic complexity (Big-O) only describes growth rate —

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TEST 2: Small nearly-sorted array (n=30)
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Algorithm: insertion_sort
Has justification: True

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TEST 3: Large random array (n=5000) - should NOT have justification
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Algorithm: merge_sort
Has justification: False
Explanation: Array is large array → Merge Sort selected (guaranteed O(n log n) performance).

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TEST 4: Reverse sorted small (n=30) - merge_sort, no justification
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Algorithm: merge_sort
Has justification: False
Explanation: Array is small array, reverse sorted → Merge Sort selected (guaranteed O(n log n) performance).

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TEST 5: _n_from_features round-trip
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  n=    5 -> size_log=0.194536 -> recovered=    5  OK
  n=   10 -> size_log=0.260345 -> recovered=   10  OK
  n=   20 -> size_log=0.330551 -> recovered=   20  OK
  n=   30 -> size_log=0.372836 -> recovered=   30  OK
  n=   40 -> size_log=0.403192 -> recovered=   40  OK
  n=   50 -> size_log=0.426888 -> recovered=   50  OK
  n=  100 -> size_log=0.501075 -> recovered=  100  OK
  n= 1000 -> size_log=0.750100 -> recovered= 1000  OK

ALL TESTS COMPLETE