feat(algorithms, heaps): top k and kth largest #152
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BrianLusina
added
enhancement
Algorithm
📝 WalkthroughWalkthroughAdded new heap algorithm modules (Top K Largest, K Closest to Origin), a Sudoku validator, corresponding READMEs and unit tests; normalized several README image paths; renamed a local variable in Longest Happy String and updated its test import; DIRECTORY.md updated to reflect these changes. Changes
Sequence Diagram(s)(omitted — changes are library/algorithm additions and docs without multi-component sequential control flow requiring visualization) Estimated code review effort🎯 3 (Moderate) | ⏱️ ~30 minutes Possibly related PRs
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Actionable comments posted: 2
🤖 Fix all issues with AI agents
In `@algorithms/heap/topklargest/__init__.py`:
- Around line 72-76: The in-place call nums.sort(reverse=True) mutates the
caller's list; change it to operate on a non-mutating sorted sequence (e.g., use
sorted(nums, reverse=True) or sort a shallow copy) and then return the k-th
largest from that result (replace the nums.sort and return nums[k - 1] usage
with a non-mutating sorted result variable and return that_result[k - 1]);
ensure references to nums.sort(reverse=True) and the subsequent return are
updated accordingly.
- Around line 5-27: k_largest lacks input validation and can IndexError on empty
inputs or invalid k; add guards at the start of k_largest to validate params
(nums and k) consistent with the other variants: if not nums or k <= 0 or k >
len(nums) raise a ValueError with a clear message (e.g., "k must be between 1
and len(nums) and nums must be non-empty"); perform this validation before
building min_heap and iterating.
🧹 Nitpick comments (2)
algorithms/heap/topklargest/__init__.py (1)
30-56: Implementation is correct; consider exception vs sentinel for invalid input.The heap-based algorithm is correctly implemented with O(n log k) time complexity. One consideration: returning
-1for invalid input could be ambiguous if-1is a valid element in the array. An alternative would be to raise aValueError, which is more Pythonic for invalid arguments. However, this is consistent withkth_largest_sorting, so the current approach is acceptable if documented.algorithms/heap/topklargest/test_top_k_largest_elements.py (1)
6-27: Consider adding edge case tests for invalid inputs.The current test cases cover happy paths well, but consider adding tests for:
- Empty list:
([], 1, -1)forkth_largest/kth_largest_sortingk=0:([1,2,3], 0, -1)k > len(nums):([1,2], 5, -1)This would validate the input validation logic and document expected behavior.
📝 Suggested additional test cases
KTH_LARGEST_EDGE_CASES = [ ([], 1, -1), # empty list ([1, 2, 3], 0, -1), # k = 0 ([1, 2], 5, -1), # k > len(nums) ]
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algorithms/heap/topklargest/test_top_k_largest_elements.py (1)
algorithms/heap/topklargest/__init__.py (3)
k_largest(5-27)kth_largest(30-56)kth_largest_sorting(59-76)
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algorithms/heap/topklargest/README.md
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🔇 Additional comments (4)
algorithms/heap/topklargest/README.md (2)
1-46: Documentation is well-structured and accurate.The explanation of the min-heap approach for Top-K elements is clear, with appropriate complexity analysis. The note about variable k complexity is helpful for understanding the general case.
49-106: LGTM!The documentation for the Kth Largest Element problem is comprehensive, covering both sorting and min-heap approaches with accurate complexity analysis.
algorithms/heap/topklargest/test_top_k_largest_elements.py (1)
12-16: LGTM!Good use of
sorted()comparison fork_largestsince the heap doesn't guarantee order within the result set.DIRECTORY.md (1)
143-144: LGTM!The new entry for
Topklargestfollows the existing indentation pattern in the file. The static analysis warning about indentation can be safely ignored as the style is consistent with other entries in the Heap section (e.g., Schedule Tasks on lines 141-142).
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Actionable comments posted: 3
🤖 Fix all issues with AI agents
In `@algorithms/heap/topkclosesttoorigin/__init__.py`:
- Around line 31-36: k_closest_to_origin_sorting currently mutates the input by
calling points.sort(); change it to avoid side effects by sorting a copy (use
sorted(points, key=...)) or by copying first (points_copy = list(points);
points_copy.sort(...)) and then return the first k from the sorted copy so the
original input remains unchanged, matching the non-mutating behavior of
k_closest_to_origin.
In `@algorithms/heap/topkclosesttoorigin/README.md`:
- Around line 6-8: The distance formula in the README is garbled; replace the
incorrect expression with correct mathematical notation: for distance from
origin use "sqrt(x² + y²)" (or "√(x² + y²)"), and for distance between two
points use "sqrt((x₁ - x₂)² + (y₁ - y₂)²)" (or "√((x₁ - x₂)² + (y₁ - y₂)²)"),
ensuring variable names match those used elsewhere in the doc (e.g., x,y and a,b
or x₁,y₁ and x₂,y₂) and remove the malformed `√(x1 - a2)2 + (y1 - b2)2` text.
In `@algorithms/heap/topkclosesttoorigin/test_top_k_closest_to_origin.py`:
- Around line 23-25: The test sorts results only by x-coordinate which can be
flaky when x values tie; update the sorting key in
test_top_k_closest_to_origin.py to use both coordinates (e.g., key that returns
a tuple of (x, y)) for sorted_expected and sorted_actual in the block using
sorted_expected/sorted_actual and apply the same change in
test_top_k_closest_to_origin_sorting so comparisons are deterministic.
🧹 Nitpick comments (1)
algorithms/heap/topkclosesttoorigin/__init__.py (1)
5-28: Consider adding edge case validation.The heap-based implementation is correct and efficient. However, there's no validation for edge cases:
k <= 0would return an empty list (acceptable but undocumented)k > len(points)would return all points (acceptable)- Empty
pointslist would return empty (acceptable)These behaviors may be intentional, but documenting them or adding explicit validation would improve robustness.
🔧 Optional: Add input validation
def k_closest_to_origin(points: List[List[int]], k: int) -> List[List[int]]: + if not points or k <= 0: + return [] + k = min(k, len(points)) + # Max heap will store the top k points closest to the origin in the form (-distance, idx). The distance is negated
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algorithms/heap/topkclosesttoorigin/test_top_k_closest_to_origin.py (1)
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DIRECTORY.md (1)
143-146: LGTM!The new directory entries for
TopkclosesttooriginandTopklargestare correctly added and follow the existing indentation pattern used throughout the file. The static analysis hints about indentation (MD007) are false positives since the file consistently uses 4-space and 6-space indentation for nested list items.algorithms/heap/topkclosesttoorigin/README.md (1)
56-104: LGTM!The algorithm explanation and complexity analysis are well-written and accurate. The cross-reference to the related
topklargestimplementation provides helpful context.algorithms/heap/topkclosesttoorigin/test_top_k_closest_to_origin.py (1)
9-13: Test coverage is adequate for core functionality.The test cases cover the basic scenarios. Consider adding edge cases in future iterations:
k = 0k > len(points)- Points with identical distances to origin
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Actionable comments posted: 1
🤖 Fix all issues with AI agents
In `@algorithms/matrix/isvalidsudoku/__init__.py`:
- Around line 15-20: The code only checks len(board) == 9 but not that each row
has 9 columns; update the validation in the isValidSudoku / is_valid_sudoku
implementation to iterate over board rows and return False if any len(row) != 9
before computing number_of_columns or indexing board[0]; this prevents malformed
inputs (e.g., short rows) from causing incorrect behavior or index errors.
🧹 Nitpick comments (3)
algorithms/matrix/isvalidsudoku/README.md (2)
1-26: Good documentation structure.The problem statement is clear, constraints are well-defined, and examples are properly referenced.
Minor formatting issue on line 12: missing space after "Note:".
📝 Suggested fix
-> Note:A partially filled Sudoku board can be valid even if it is not necessarily solvable. You only need to verify that +> Note: A partially filled Sudoku board can be valid even if it is not necessarily solvable. You only need to verify that
44-59: Minor inconsistency in variable naming.The algorithm description uses
row_sets,col_sets,box_sets(lines 44-46), but later references them asrow_set,col_set,box_set(lines 56-58). Consider using consistent naming throughout.📝 Suggested fix for consistency
- - If no duplicate is found, we insert `cell` into: - - `row_set[r]`, - - `col_set[c]`, - - `box_set[box_idx]`. + - If no duplicate is found, we insert `cell` into: + - `row_sets[r]`, + - `col_sets[c]`, + - `box_sets[box_idx]`.algorithms/matrix/isvalidsudoku/test_is_valid_sudoku.py (1)
6-77: Good test coverage for basic scenarios.The test cases cover valid/invalid boards with row and column duplicates effectively. The parameterized approach is clean and maintainable.
Consider adding test cases for:
- 3x3 box duplicates (a digit repeated within the same sub-box but not in same row/column)
- Invalid board dimensions (e.g., 8x9 or empty board) to test the edge case validation in the implementation
- Edge case with
k > len(nums)scenario
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algorithms/matrix/isvalidsudoku/__init__.py (1)
is_valid_sudoku(4-51)
algorithms/heap/longest_happy_string/test_longest_happy_string.py (1)
algorithms/heap/longest_happy_string/__init__.py (1)
longest_diverse_string(5-52)
algorithms/heap/topklargest/__init__.py (1)
algorithms/sorting/heapsort/__init__.py (1)
heapify(60-63)
🪛 LanguageTool
algorithms/heap/longest_happy_string/README.md
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algorithms/heap/total_cost_hire_k_workers/README.md
[style] ~68-~68: This phrase might be redundant. Consider either removing or replacing the adjective ‘additional’.
Context: ...ire a worker from a section, we need to add an additional candidate to this section. Therefore, we need two...
(ADD_AN_ADDITIONAL)
[locale-violation] ~76-~76: In American English, ‘afterward’ is the preferred variant. ‘Afterwards’ is more commonly used in British English and other dialects.
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algorithms/matrix/isvalidsudoku/README.md
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Context: ...s valid. A board is considered valid if all of the following conditions hold (considering ...
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[style] ~8-~8: Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
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🪛 markdownlint-cli2 (0.18.1)
DIRECTORY.md
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🔇 Additional comments (11)
algorithms/heap/total_cost_hire_k_workers/README.md (1)
61-86: LGTM!The image path updates normalize the references consistently. The formatting changes look good.
algorithms/heap/longest_happy_string/README.md (1)
25-27: LGTM!Image path normalization is consistent with other README files in this PR. The changes improve path consistency across the documentation.
Also applies to: 58-72
algorithms/heap/longest_happy_string/__init__.py (1)
23-24: Good refactor for clarity.Renaming
counttonegative_countand explicitly converting withcount = -negative_countmakes the code more self-documenting. The variable name now clearly conveys that heap values are stored as negatives for max-heap simulation.algorithms/heap/longest_happy_string/test_longest_happy_string.py (1)
3-3: LGTM!Import path correctly updated to reflect the module's new location under
algorithms.heap. The test cases provide good coverage including edge cases with zero counts and unbalanced character frequencies.algorithms/matrix/isvalidsudoku/README.md (1)
74-99: LGTM!The complexity analysis is correct. For a fixed 9×9 board, both time and space are O(1) since the number of operations and storage are bounded by constants.
DIRECTORY.md (1)
141-158: LGTM!The new directory entries for the Heap algorithms (Longest Happy String, Maximal Score After K Operations, Topkclosesttoorigin, Topklargest, etc.) and the Matrix/Isvalidsudoku entry follow the existing file structure and indentation conventions consistently.
Note: The static analysis indentation warnings (MD007) are false positives—the indentation used here matches the established convention throughout the rest of the file.
Also applies to: 192-194
algorithms/matrix/isvalidsudoku/test_is_valid_sudoku.py (1)
80-88: LGTM!The test class structure is clean with proper use of
parameterized.expandfor data-driven testing and appropriate type hints.algorithms/matrix/isvalidsudoku/__init__.py (1)
25-51: LGTM!The core validation logic is correct and efficient. The single-pass approach with hash sets for tracking duplicates in rows, columns, and 3x3 boxes is the standard O(1) space/time solution for this problem. The box index formula
(row // 3) * 3 + (col // 3)correctly maps each cell to its sub-box.algorithms/heap/topklargest/__init__.py (3)
5-34: LGTM! Input validation and edge case handling now in place.The
k_largestfunction correctly validates inputs, capskto the array length, and uses a min-heap approach efficiently. The previous concerns about missing validation have been addressed.
37-63: LGTM!The
kth_largestfunction correctly maintains a min-heap of size k and returns the heap root as the kth largest element. Input validation is properly implemented.
66-83: LGTM! In-place mutation issue resolved.The function now uses
sorted()to create a new list instead of mutating the input. This addresses the previous concern about unexpected side effects on the caller's list.
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