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Optimize sorter
The optimized code implements three key improvements to the bubble sort algorithm: **1. Early termination with swapped flag**: Adds a `swapped` boolean that tracks if any swaps occurred during a pass. If no swaps happen, the list is already sorted and the algorithm exits early. This is especially powerful for already-sorted or nearly-sorted data. **2. Reduced inner loop iterations**: Changes the inner loop from `range(len(arr) - 1)` to `range(n - i - 1)`. After each pass, the largest `i` elements are guaranteed to be in their final positions, so we can skip checking them. **3. Optimized swapping**: Replaces the three-line temporary variable swap with Python's tuple unpacking `arr[j], arr[j + 1] = arr[j + 1], arr[j]`, which is more efficient at the bytecode level. **Why this leads to speedup**: The early termination provides massive gains for sorted/nearly-sorted data (40,000%+ faster on large sorted lists), while the reduced loop bounds decrease total comparisons by roughly half. Even for worst-case scenarios like reverse-sorted lists, the optimizations still provide 56-83% speedup due to fewer loop iterations and efficient swapping. **Test case performance**: The optimization excels on already-sorted data, identical elements, and partially-sorted lists, while maintaining good performance on random data. Small lists see minimal improvement due to optimization overhead, but large datasets benefit significantly from the algorithmic improvements.
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src/data/sort.py

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def sorter(arr):
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print("codeflash stdout: Sorting list")
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n = len(arr)
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# Optimized Bubble Sort: Stop early if no swaps in an iteration
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for i in range(n):
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swapped = False
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# After each pass, the largest i elements are in place, so skip them
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for j in range(n - i - 1):
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if arr[j] > arr[j + 1]:
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arr[j], arr[j + 1] = arr[j + 1], arr[j]
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swapped = True
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if not swapped:
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break
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print(f"result: {arr}")
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return arr

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