Solution Trees 2

[shakthinandana]

No description provided.

[super30admin]

Construct Binary Tree from Inorder and Postorder Traversal (build_with_postorder_inorder.py)

Strengths:

• The code correctly implements the recursive approach to build the tree by leveraging the postorder array to determine the root and the inorder array to determine the left and right subtrees.
• The use of a hashmap (dictionary) for storing inorder indices is efficient and reduces the time complexity from O(n^2) to O(n) for each node lookup.
• The code is well-structured with a helper function that handles the recursive construction.

Areas for Improvement:

1. Variable Naming: The variable names in_st and in_end could be more descriptive. Consider using start and end or in_start and in_end for clarity.
2. Class Variables: The use of self.rootindex is appropriate but note that it is a mutable state. Since the solution is recursive and relies on decrementing this index, it is correct. However, it's important to ensure that this variable is properly initialized and managed. In this case, it is initialized in the buildTree method and then used in the helper function. This is acceptable, but be cautious as using instance variables in recursive methods can sometimes lead to issues if multiple instances are used concurrently (though not a concern in this context).
3. Recursion Base Case: The base case if (in_st > in_end) is correct and handles the termination condition properly.
4. Order of Recursion: The code correctly processes the right subtree first and then the left subtree because in postorder, the root is at the end, and when we traverse backwards, the right subtree comes before the left. This is accurately reflected.
5. Code Readability: Adding comments to explain the purpose of the helper function and the key steps (like why we decrement rootindex and why we call right before left) would make the code more understandable.

Minor Suggestions:

• The helper function could be named more descriptively, such as buildTreeHelper.
• The code could include a check for empty input arrays at the beginning, though the base case in the helper already handles the case when start > end.

Overall, the solution is efficient and correct. The code quality is good, but minor improvements in naming and comments would enhance readability.

VERDICT: PASS

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Root to Leaf Sum (sum_root_to_leaf.py)

Strengths:

• The solution correctly implements the recursive DFS approach to traverse the tree and accumulate the root-to-leaf numbers.
• The time and space complexity are optimal and match the reference solution.
• The code is concise and readable.

Areas for improvement:

• The helper function does not have a return statement for base cases, but it uses return to exit when the node is null or when it reaches a leaf. However, the recursive calls are made without returning any value, which is acceptable in Python since the function modifies the class variable self.res.
• The base case check if root == None: return is correct, but it could be written more idiomatically as if not root: return.
• The variable names cur could be more descriptive, such as current_sum or current_num, to improve readability.
• The solution uses a class variable self.res to store the result. While this works, it might be slightly more efficient to avoid using a class variable and instead return the sum from the helper function. However, the current approach is acceptable.

Minor suggestions:

• Consider adding a docstring to the helper function to explain its purpose and parameters.
• The code could be made more efficient by avoiding the class variable and using a return value from the helper function, but the current method is straightforward.

Overall, the solution is correct and efficient.

VERDICT: PASS