feat(algorithms, two-pointers): three number sum#188
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🧹 Nitpick comments (3)
algorithms/two_pointers/three_sum/__init__.py (2)
54-56: Add a docstring for consistency withthree_sum.The existing
three_sumfunction has a comprehensive docstring documenting complexity analysis and parameters. Adding similar documentation tothree_number_sumwould improve maintainability and API discoverability.📝 Proposed docstring
def three_number_sum(array: List[int], target_sum: int) -> List[List[int]]: + """ + Finds all unique triplets in the array that sum up to the target sum. + + Complexity Analysis: + Time Complexity: O(n²) where n is the length of the input array. + Space Complexity: O(n) for the sorted copy, plus O(n²) for storing results. + + Args: + array (list): input list of distinct integers + target_sum (int): target sum for the triplets + Returns: + list: list of triplets, each ordered ascending, with triplets ordered ascending + """ if not array: return []🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@algorithms/two_pointers/three_sum/__init__.py` around lines 54 - 56, Add a comprehensive docstring to the three_number_sum function matching the style of three_sum: describe the function purpose, parameters (array: List[int], target_sum: int), return value (List[List[int]]), and include time/space complexity notes and any ordering or uniqueness guarantees; update the top of the three_number_sum function to include this docstring so it matches the existing documentation format used by three_sum.
65-67: Consider adding duplicate skip for the outer loop for robustness.The
three_sumfunction (line 28) skips duplicate values in the outer loop to avoid duplicate triplets. Whilst the problem statement specifies "distinct integers", adding a similar check here would make the function more robust and consistent withthree_sum.♻️ Proposed fix to handle duplicate outer values
for idx, num in enumerate(nums): + # Skip duplicate values to avoid duplicate triplets + if idx > 0 and num == nums[idx - 1]: + continue + left = idx + 1 right = n - 1🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@algorithms/two_pointers/three_sum/__init__.py` around lines 65 - 67, The outer loop in the related function should skip duplicate starting values to avoid producing duplicate triplets: inside the for idx, num in enumerate(nums) loop in three_sum, add a guard like "if idx > 0 and nums[idx] == nums[idx - 1]: continue" (use the same sorted nums input as the inner logic expects) so repeated outer values are skipped; update the loop that defines left = idx + 1 and right = n - 1 to include this duplicate-skip check.algorithms/two_pointers/three_sum/test_three_sum.py (1)
17-19: Consider adding more test cases for better coverage.Currently there's only one test case for
three_number_sum. Additional edge cases would strengthen confidence in the implementation:
- Empty array:
([], 0, [])- Array with fewer than 3 elements:
([1, 2], 3, [])- No matching triplets:
([1, 2, 3, 4], 100, [])- Negative target sum:
([1, -2, 3, -4, 5], -3, [[-4, -2, 3]])🧪 Proposed additional test cases
THREE_NUMBER_SUM_TEST_CASES = [ ([12, 3, 1, 2, -6, 5, -8, 6], 0, [[-8, 2, 6], [-8, 3, 5], [-6, 1, 5]]), + ([], 0, []), + ([1, 2], 3, []), + ([1, 2, 3, 4], 100, []), + ([1, -2, 3, -4, 5], -3, [[-4, -2, 3]]), ]🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@algorithms/two_pointers/three_sum/test_three_sum.py` around lines 17 - 19, Add a broader set of unit cases to THREE_NUMBER_SUM_TEST_CASES to improve coverage for the three_number_sum implementation: include an empty array case ([], 0, []), a short-array case with fewer than 3 elements ([1, 2], 3, []), a no-match case ([1, 2, 3, 4], 100, []), and a negative-target case ([1, -2, 3, -4, 5], -3, [[-4, -2, 3]]); ensure these new tuples are appended to the existing THREE_NUMBER_SUM_TEST_CASES list so the test harness that calls three_number_sum exercises these edge scenarios.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Nitpick comments:
In `@algorithms/two_pointers/three_sum/__init__.py`:
- Around line 54-56: Add a comprehensive docstring to the three_number_sum
function matching the style of three_sum: describe the function purpose,
parameters (array: List[int], target_sum: int), return value (List[List[int]]),
and include time/space complexity notes and any ordering or uniqueness
guarantees; update the top of the three_number_sum function to include this
docstring so it matches the existing documentation format used by three_sum.
- Around line 65-67: The outer loop in the related function should skip
duplicate starting values to avoid producing duplicate triplets: inside the for
idx, num in enumerate(nums) loop in three_sum, add a guard like "if idx > 0 and
nums[idx] == nums[idx - 1]: continue" (use the same sorted nums input as the
inner logic expects) so repeated outer values are skipped; update the loop that
defines left = idx + 1 and right = n - 1 to include this duplicate-skip check.
In `@algorithms/two_pointers/three_sum/test_three_sum.py`:
- Around line 17-19: Add a broader set of unit cases to
THREE_NUMBER_SUM_TEST_CASES to improve coverage for the three_number_sum
implementation: include an empty array case ([], 0, []), a short-array case with
fewer than 3 elements ([1, 2], 3, []), a no-match case ([1, 2, 3, 4], 100, []),
and a negative-target case ([1, -2, 3, -4, 5], -3, [[-4, -2, 3]]); ensure these
new tuples are appended to the existing THREE_NUMBER_SUM_TEST_CASES list so the
test harness that calls three_number_sum exercises these edge scenarios.
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📒 Files selected for processing (5)
algorithms/arrays/remove_duplicates/README.mdalgorithms/arrays/remove_duplicates/__init__.pyalgorithms/two_pointers/three_sum/README.mdalgorithms/two_pointers/three_sum/__init__.pyalgorithms/two_pointers/three_sum/test_three_sum.py
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Describe your change:
Three number sum algorithm
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Fixes: #{$ISSUE_NO}.Summary by CodeRabbit
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