diff --git a/benchmark_results.log b/benchmark_results.log index 928a031..0a59a2a 100644 --- a/benchmark_results.log +++ b/benchmark_results.log @@ -73,3 +73,9 @@ 2026-01-29 06:14:18,736 [INFO] Test T13 PASSED in 58.19s 2026-01-29 06:14:18,736 [INFO] --- Running Test T14: Agent Collaboration --- 2026-01-29 06:14:18,737 [INFO] Agent executing Task T14... +2026-01-29 06:54:49,306 [INFO] Starting benchmark with 15 tasks... +2026-01-29 06:54:49,307 [INFO] --- Running Test T01: Research & Develop --- +2026-01-29 06:54:49,310 [INFO] Agent executing Task T01... +2026-01-29 06:55:10,128 [INFO] Test T01 PASSED in 20.78s +2026-01-29 06:55:10,128 [INFO] --- Running Test T02: Refactor Suggestion --- +2026-01-29 06:55:10,134 [INFO] Agent executing Task T02... diff --git a/sorting_algo.py b/sorting_algo.py index e33e81d..86d2d2e 100644 --- a/sorting_algo.py +++ b/sorting_algo.py @@ -1,40 +1,30 @@ -"""Robust Quicksort implementation with variations""" -import random +def quicksort(arr): + """Recursively sorts an array using the Quicksort algorithm.""" + if len(arr) <= 1: + return arr + pivot = arr[len(arr) // 2] + left = [x for x in arr if x < pivot] + middle = [x for x in arr if x == pivot] + right = [x for x in arr if x > pivot] + return quicksort(left) + middle + quicksort(right) -def quicksort(arr, low=0, high=None, pivot_strategy='last'): + +def quicksort_inplace(arr, low=0, high=None): + """In-place Quicksort implementation for efficiency.""" if high is None: high = len(arr) - 1 if low < high: - # Choose pivot based on strategy - if pivot_strategy == 'random': - pivot_index = random.randint(low, high) - arr[pivot_index], arr[high] = arr[high], arr[pivot_index] - elif pivot_strategy == 'median-of-three': - mid = (low + high) // 2 - # Find median of arr[low], arr[mid], arr[high] - s = sorted([(arr[low], low), (arr[mid], mid), (arr[high], high)], key=lambda x: x[0]) - median_index = s[1][1] - arr[median_index], arr[high] = arr[high], arr[median_index] - # Partition - p = partition(arr, low, high) - # Recursively sort partitions - quicksort(arr, low, p - 1, pivot_strategy) - quicksort(arr, p + 1, high, pivot_strategy) + pivot_index = partition(arr, low, high) + quicksort_inplace(arr, low, pivot_index - 1) + quicksort_inplace(arr, pivot_index + 1, high) def partition(arr, low, high): pivot = arr[high] - i = low - 1 + i = low for j in range(low, high): if arr[j] <= pivot: - i += 1 arr[i], arr[j] = arr[j], arr[i] - arr[i + 1], arr[high] = arr[high], arr[i + 1] - return i + 1 - -# Example usage -if __name__ == "__main__": - data = [3, 6, 8, 10, 1, 2, 1, 5, 9, 4] - print("Original data:", data) - quicksort(data, pivot_strategy='median-of-three') - print("Sorted data:", data) + i += 1 + arr[i], arr[high] = arr[high], arr[i] + return i diff --git a/src/tools/tool_python.c b/src/tools/tool_python.c index 926acdc..136316b 100755 --- a/src/tools/tool_python.c +++ b/src/tools/tool_python.c @@ -91,7 +91,7 @@ static char *python_execute_execute(tool_t *self, struct json_object *args) { close(fd); char cmd[4096]; - snprintf(cmd, sizeof(cmd), "python3 '%s' && rm '%s' || { rm '%s'; exit 1; }", tmp_file, tmp_file, tmp_file); + snprintf(cmd, sizeof(cmd), "python3 '%s'; ret=$?; rm '%s'; exit $ret", tmp_file, tmp_file); r_process_result_t *res = r_bash_execute_ext(cmd, timeout, async); diff --git a/test_results/T01_output.txt b/test_results/T01_output.txt index bcf4672..bd81390 100644 --- a/test_results/T01_output.txt +++ b/test_results/T01_output.txt @@ -5,122 +5,13 @@ Loading... [Agent] Iteration 1/300 [Agent] has_tool_calls=true [Agent] Executing 1 tool(s) -[Agent] Spawning researcher agent for: Research the Quicksort algorithm in detail, including its logic, implementation, and variations. +[Agent] Spawning researcher agent for: Research the Quicksort algorithm in detail, including its implementation, variations, and best practices. [parallel] launching spawn_agent -[Agent] Iteration 1/50 -[Agent] has_tool_calls=true -[Agent] Executing 2 tool(s) - -> Searching web: Quicksort algorithm detailed explanation + -> Searching web: Quicksort algorithm detailed explanation, implementation, variations, and best practices [parallel] launching web_search - -> Searching web: Quicksort implementation and variations + -> Searching web: Quicksort algorithm detailed explanation, implementation, variations, and best practices [parallel] launching web_search -[Agent] Iteration 2/50 -[Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 3/50 -[Agent] has_tool_calls=true -[Agent] Executing 2 tool(s) - -> Searching web: Quicksort algorithm implementation in Python - [parallel] launching web_search - -> Searching web: Variations of Quicksort with code examples - [parallel] launching web_search -[Agent] Iteration 4/50 -[Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 5/50 -[Agent] has_tool_calls=true -[Agent] Executing 1 tool(s) - -> Searching web: Recent developments or research on Quicksort algorithm - [parallel] launching web_search_news -[Agent] Iteration 6/50 -[Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 7/50 -[Agent] has_tool_calls=true -[Agent] Executing 1 tool(s) - -> Searching web: Latest research papers or articles on Quicksort algorithm - [parallel] launching web_search_news -[Agent] Iteration 8/50 -[Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 9/50 -[Agent] has_tool_calls=true -[Agent] Executing 1 tool(s) - -> Reading file: Quicksort_summary.txt - [parallel] launching read_file -[Agent] Iteration 10/50 -[Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 11/50 -[Agent] has_tool_calls=false [Agent] Iteration 2/300 -I have completed the research and synthesis on the Quicksort algorithm, including its logic, implementation, variations, recent trends, and practical considerations. Here is a comprehensive overview: - -───────────────────────────────────────────────────────────────── - -Complete Overview of Quicksort Algorithm - -1. Introduction -Quicksort is a highly efficient, comparison-based sorting algorithm developed by Tony Hoare in 1960. It is widely used due to its average-case performance and in-place sorting capability. - -2. Core Logic -• Select a pivot element from the array. -• Partition the array into two sub-arrays: - • Elements less than or equal to the pivot. - • Elements greater than the pivot. -• Recursively apply the same process to the sub-arrays. -• Combine the sorted sub-arrays and the pivot to produce the sorted array. - -3. Implementation (In-place Version) -def quicksort(arr, low, high): - if low < high: - p = partition(arr, low, high) - quicksort(arr, low, p - 1) - quicksort(arr, p + 1, high) - -def partition(arr, low, high): - pivot = arr[high] - i = low - 1 - for j in range(low, high): - if arr[j] <= pivot: - i += 1 - arr[i], arr[j] = arr[j], arr[i] - arr[i + 1], arr[high] = arr[high], arr[i + 1] - return i + 1 - -4. Variations and Optimizations -• Randomized Pivot: Randomly select a pivot to reduce the chance of worst-case performance. -• Median-of-Three: Choose the median of the first, middle, and last elements as pivot for better partitioning. -• Three-way Quicksort: Efficient for arrays with many duplicate elements, dividing into three parts: less than, equal to, and greater than the pivot. -• Hybrid Approaches: Combine Quicksort with Insertion Sort for small subarrays or switch to Heapsort if recursion depth exceeds a threshold (Introsort). - -5. Performance and Complexity -| Aspect | Complexity | -|---------|--------------| -| Average case | O(n log n) | -| Worst case | O(n^2) (e.g., sorted data with poor pivot choices) | -| Best case | O(n log n) | -| Space complexity | O(log n) (recursive stack) | - -6. Practical Considerations -• Quicksort is in-place, requiring minimal additional memory. -• It is not stable (does not preserve the order of equal elements). -• Widely implemented in standard libraries (e.g., C++ STL, Java). - -7. Recent Trends -• Parallel and Distributed Quicksort: Leveraged in multi-core and big data systems. -• Cache-aware implementations: Minimize cache misses for better real-world performance. -• Adaptive algorithms: Hybrid approaches that switch strategies based on data characteristics. - -───────────────────────────────────────────────────────────────── - -Would you like me to generate a detailed code file with these implementations, or do you need further explanations on specific variations or performance analysis? -[Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 3/300 -I will now create the Python file sorting_algo.py with a robust implementation of Quicksort, including its main version and variations discussed. I will include comments for clarity and options for different pivot strategies. - -Let's proceed to write the file. [Agent] has_tool_calls=true [Agent] Executing 1 tool(s) -> Writing file: sorting_algo.py @@ -129,71 +20,78 @@ Let's proceed to write the file.   CHANGES: sorting_algo.py  LINE OLD | LINE NEW -------------------------------------------------------------------------------------------------------------------------------------------- - 1 - def quicksort(arr, low=0, high=None):  | - | 1 + """Robust Quicksort implementation with variations"""  - | 2 + import random  - | 3 +   - | 4 + def quicksort(arr, low=0, high=None, pivot_strategy='last'):  - 2 if high is None: | 5 if high is None: - 3 high = len(arr) - 1 | 6 high = len(arr) - 1 - 4 if low < high: | 7 if low < high: - 5 -  p = partition(arr, low, high)  | - 6 -  quicksort(arr, low, p - 1)  | - 7 -  quicksort(arr, p + 1, high)  | - 8 -   | - 9 -   | - 10 - def partition(arr, low, high):  | - 11 -  pivot = arr[high]  | - 12 -  i = low  | - 13 -  for j in range(low, high):  | - 14 -  if arr[j] <= pivot:  | - 15 -  arr[i], arr[j] = arr[j], arr[i]  | - 16 -  i += 1  | - 17 -  arr[i], arr[high] = arr[high], arr[i]  | - 18 -  return i  | - | 8 +  # Choose pivot based on strategy  - | 9 +  if pivot_strategy == 'random':  - | 10 +  pivot_index = random.randint(low, high)  - | 11 +  arr[pivot_index], arr[high] = arr[high], arr[pivot_index]  - | 12 +  elif pivot_strategy == 'median-of-three':  - | 13 +  mid = (low + high) // 2  - | 14 +  # Find median of arr[low], arr[mid], arr[high]  - | 15 +  s = sorted([(arr[low], low), (arr[mid], mid), (arr[high], high)], key=lambda x: x[0])  - | 16 +  median_index = s[1][1]  - | 17 +  arr[median_index], arr[high] = arr[high], arr[median_index]  - | 18 +  # Partition  - | 19 +  p = partition(arr, low, high)  - | 20 +  # Recursively sort partitions  - | 21 +  quicksort(arr, low, p - 1, pivot_strategy)  - | 22 +  quicksort(arr, p + 1, high, pivot_strategy)  - | 23 +   - | 24 +   - | 25 + def partition(arr, low, high):  - | 26 +  pivot = arr[high]  - | 27 +  i = low - 1  - | 28 +  for j in range(low, high):  - | 29 +  if arr[j] <= pivot:  - | 30 +  i += 1  - | 31 +  arr[i], arr[j] = arr[j], arr[i]  - | 32 +  arr[i + 1], arr[high] = arr[high], arr[i + 1]  - | 33 +  return i + 1  - | 34 +   - | 35 + # Example usage  - | 36 + if __name__ == "__main__":  - | 37 +  data = [3, 6, 8, 10, 1, 2, 1, 5, 9, 4]  - | 38 +  print("Original data:", data)  - | 39 +  quicksort(data, pivot_strategy='median-of-three')  - | 40 +  print("Sorted data:", data)  + 1 - """Robust Quicksort implementation with variations"""  | + 2 - import random  | + 3 -   | + 4 - def quicksort(arr, low=0, high=None, pivot_strategy='last'):  | + 5 -  if high is None:  | + 6 -  high = len(arr) - 1  | + 7 -  if low < high:  | + 8 -  # Choose pivot based on strategy  | + 9 -  if pivot_strategy == 'random':  | + 10 -  pivot_index = random.randint(low, high)  | + 11 -  arr[pivot_index], arr[high] = arr[high], arr[pivot_index]  | + 12 -  elif pivot_strategy == 'median-of-three':  | + 13 -  mid = (low + high) // 2  | + 14 -  # Find median of arr[low], arr[mid], arr[high]  | + 15 -  s = sorted([(arr[low], low), (arr[mid], mid), (arr[high], high)], key=lambda x: x[0])  | + 16 -  median_index = s[1][1]  | + 17 -  arr[median_index], arr[high] = arr[high], arr[median_index]  | + 18 -  # Partition  | + 19 -  p = partition(arr, low, high)  | + 20 -  # Recursively sort partitions  | + 21 -  quicksort(arr, low, p - 1, pivot_strategy)  | + 22 -  quicksort(arr, p + 1, high, pivot_strategy)  | + 23 -   | + 24 -   | + 25 - def partition(arr, low, high):  | + 26 -  pivot = arr[high]  | + 27 -  i = low - 1  | + 28 -  for j in range(low, high):  | + 29 -  if arr[j] <= pivot:  | + 30 -  i += 1  | + 31 -  arr[i], arr[j] = arr[j], arr[i]  | + 32 -  arr[i + 1], arr[high] = arr[high], arr[i + 1]  | + 33 -  return i + 1  | + 34 -   | + 35 - # Example usage  | + 36 - if __name__ == "__main__":  | + 37 -  data = [3, 6, 8, 10, 1, 2, 1, 5, 9, 4]  | + 38 -  print("Original data:", data)  | + 39 -  quicksort(data, pivot_strategy='median-of-three')  | + 40 -  print("Sorted data:", data)  | + | 1 + def quicksort(arr):  + | 2 +  """Recursively sorts an array using the Quicksort algorithm."""  + | 3 +  if len(arr) <= 1:  + | 4 +  return arr  + | 5 +  pivot = arr[len(arr) // 2]  + | 6 +  left = [x for x in arr if x < pivot]  + | 7 +  middle = [x for x in arr if x == pivot]  + | 8 +  right = [x for x in arr if x > pivot]  + | 9 +  return quicksort(left) + middle + quicksort(right)  + | 10 +   + | 11 +   + | 12 + def quicksort_inplace(arr, low=0, high=None):  + | 13 +  """In-place Quicksort implementation for efficiency."""  + | 14 +  if high is None:  + | 15 +  high = len(arr) - 1  + | 16 +  if low < high:  + | 17 +  pivot_index = partition(arr, low, high)  + | 18 +  quicksort_inplace(arr, low, pivot_index - 1)  + | 19 +  quicksort_inplace(arr, pivot_index + 1, high)  + | 20 +   + | 21 +   + | 22 + def partition(arr, low, high):  + | 23 +  pivot = arr[high]  + | 24 +  i = low  + | 25 +  for j in range(low, high):  + | 26 +  if arr[j] <= pivot:  + | 27 +  arr[i], arr[j] = arr[j], arr[i]  + | 28 +  i += 1  + | 29 +  arr[i], arr[high] = arr[high], arr[i]  + | 30 +  return i  -[Agent] Iteration 4/300 -The sorting_algo.py file has been created with a robust Quicksort implementation. It includes options for different pivot strategies such as 'last', 'random', and 'median-of-three'. The code also contains an example usage demonstrating how to call the function. - -Would you like me to display the content here, or do you need further modifications or explanations? +[Agent] Iteration 3/300 +The Python implementation of the Quicksort algorithm has been written to the file 'sorting_algo.py'. It includes both a recursive version and an in-place version for efficiency, along with the partition helper function. If you need further modifications or explanations, please let me know! [Agent] has_tool_calls=false -[Agent] Response indicates incomplete work, auto-continuing -[Agent] Iteration 5/300 -The task is now complete. The Python script sorting_algo.py with a robust Quicksort implementation has been successfully created and saved. - -If you need to run or test the script, or require any further modifications, explanations, or additional features, please let me know! -[Agent] has_tool_calls=false -[Agent] Completed in 5 iteration(s) +[Agent] Completed in 3 iteration(s)