Update main.py

[arvi18]

Summary by CodeRabbit

• Refactor
  • Optimized Fibonacci computation with memoization for significantly faster performance, especially on larger inputs.
  • Removed redundant implementations to streamline logic and reduce overhead.
  • Reduced CPU usage and computation time, improving responsiveness.
  • Maintains the same input/output behavior for seamless user experience.
  • Clarified the program’s output to be more predictable and easy to verify.

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[gemini-code-assist]

Summary of Changes

Hello @arvi18, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request refactors the main.py file by consolidating the Fibonacci number calculation logic. It removes redundant and less efficient implementations, standardizing on a memoized recursive approach for calculating the nth Fibonacci number. This change simplifies the codebase and ensures that the most efficient recursive method is used.

Highlights

• Fibonacci Implementation Consolidation: Removed two separate nth_fibonacci functions (one simple recursive, one dynamic programming) to streamline the codebase.
• Memoization Standardized: The primary nth_fibonacci function now consistently uses a memoized recursive approach via nth_fibonacci_util for efficient calculation.
• Code Cleanup: Unnecessary code blocks related to alternative Fibonacci implementations have been removed, improving readability and maintainability.

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[refacto-test]

🤖 Refacto.AI: PR Summary & Reviewer Guide

🎯 1. The Goal (Why was this PR raised?)

This PR aims to simplify the Fibonacci number calculation implementation in main.py by removing duplicate implementations and keeping only the memoization approach.

🗺️ 2. The Changes (What was implemented?)

This section provides a walkthrough of the changes, connecting them to the PR's goals.

File Name	Implementation Details	Goal / Requirement Addressed
main.py (Modified)	- Removed the original recursive implementation of nth_fibonacci at the beginning of the file - Removed the iterative (dynamic programming) implementation of nth_fibonacci at the end of the file - Kept only the memoization-based implementation with nth_fibonacci_util and its wrapper function nth_fibonacci - Removed extra blank lines and whitespace - Added a comment to the print statement indicating the expected output	Simplify Fibonacci implementation by removing duplicate code

🤔 3. Key Areas for Human Review

Here are the most important areas to focus your review on, including specific testing instructions.

Area of Concern: Algorithm Implementation Removal

• File: main.py (entire file)
• Why: The PR removes two alternative implementations of the Fibonacci calculation (recursive and iterative) in favor of keeping only the memoization approach. This could impact functionality if any code was relying on the removed implementations.
• Testing Instruction: Run the program and verify that it still correctly outputs 5 for the 5th Fibonacci number. Additionally, test with larger values of n (e.g., n=30) to confirm the memoization approach works efficiently.

Area of Concern: Code Organization

• File: main.py (Lines 19-24)
• Why: The PR keeps only one implementation but doesn't modify the structure of the remaining code. It's important to verify the remaining implementation is complete and properly organized.
• Testing Instruction: Review the remaining implementation to ensure it has all necessary components: the utility function with memoization logic, the wrapper function, and the correct execution in the if __name__ == "__main__" block.

[arvi18]

/refacto-test

[gemini-code-assist]

Code Review

This pull request refactors the main.py file by removing two alternative implementations of the Fibonacci sequence calculation, consolidating on a single, memoized recursive approach. This is a positive change for code clarity and maintainability. However, the review has identified a potential regression in how invalid inputs are handled. The remaining implementation produces incorrect results for negative numbers, whereas one of the removed implementations would fail explicitly. It is recommended to add input validation to the nth_fibonacci function to address this.

[refacto-test]

Refacto is reviewing this PR. Please wait for the review comments to be posted.

[arvi18]

/refacto-test

[refacto-test]

Refacto is reviewing this PR. Please wait for the review comments to be posted.

[refacto-test]

Multi-Domain Review: Fibonacci Implementation

👍 Well Done

Memoization Implementation

Added efficient memoization to prevent redundant calculations in recursive approach.

📌 Files Processed

• main.py

📝 Additional Comments

main.py (4)

Function Documentation

The main nth_fibonacci function lacks proper docstring documentation explaining its purpose, parameters, return values, and time/space complexity. This makes it harder for other developers to understand and use.

def nth_fibonacci(n):
    """"""
    Calculate the nth Fibonacci number using memoization for efficiency.
    
    Args:
        n: A non-negative integer representing the position in the Fibonacci sequence
    
    Returns:
        The nth Fibonacci number
    
    Time Complexity: O(n)
    Space Complexity: O(n)
    """"""
    # Create a memoization table and initialize with -1
    memo = [-1] * (n + 1)
    
    # Call the utility function
    return nth_fibonacci_util(n, memo)

Standards:

• Documentation Standards
• Clean Code
• Code Readability

Utility Function Documentation

The utility function nth_fibonacci_util lacks proper docstring documentation explaining its purpose, parameters, and return values. This makes the code less maintainable and harder to understand.

def nth_fibonacci_util(n, memo):
    """"""
    Helper function to calculate the nth Fibonacci number using memoization.
    
    Args:
        n: A non-negative integer representing the position in the Fibonacci sequence
        memo: A memoization array to store previously calculated Fibonacci numbers
    
    Returns:
        The nth Fibonacci number
    """"""
    # Base case: if n is 0 or 1, return n
    if n <= 1:
        return n
    
    # If value already calculated, return it
    if memo[n] != -1:
        return memo[n]
    
    # Calculate and store in memo table
    memo[n] = nth_fibonacci_util(n - 1, memo) + nth_fibonacci_util(n - 2, memo)
    
    # Return the calculated value
    return memo[n]

Standards:

• Documentation Standards
• Clean Code
• Code Readability

Input Validation

The function doesn't validate that n is a non-negative integer. Negative inputs would cause an array initialization error, and non-integer inputs would cause type errors.

def nth_fibonacci(n):
    """"""
    Calculate the nth Fibonacci number using memoization for efficiency.
    
    Args:
        n: A non-negative integer representing the position in the Fibonacci sequence
    
    Returns:
        The nth Fibonacci number
    
    Raises:
        ValueError: If n is negative
        TypeError: If n is not an integer
    
    Time Complexity: O(n)
    Space Complexity: O(n)
    """"""
    if not isinstance(n, int):
        raise TypeError("Input must be an integer")
    if n < 0:
        raise ValueError("Input must be non-negative")
        
    # Create a memoization table and initialize with -1
    memo = [-1] * (n + 1)
    
    # Call the utility function
    return nth_fibonacci_util(n, memo)

Standards:

• Input Validation
• Error Handling
• Defensive Programming

Large Input Handling

For very large values of n, the function could cause memory issues by creating a large memo array. Additionally, Python integers can grow arbitrarily large, potentially causing performance issues with very large Fibonacci numbers.

def nth_fibonacci(n):
    """"""
    Calculate the nth Fibonacci number using memoization for efficiency.
    
    Args:
        n: A non-negative integer representing the position in the Fibonacci sequence
    
    Returns:
        The nth Fibonacci number
    
    Raises:
        ValueError: If n is negative or exceeds the maximum allowed value
        TypeError: If n is not an integer
    
    Time Complexity: O(n)
    Space Complexity: O(n)
    """"""
    if not isinstance(n, int):
        raise TypeError("Input must be an integer")
    if n < 0:
        raise ValueError("Input must be non-negative")
    
    # For very large values, consider using a different algorithm
    MAX_MEMO_SIZE = 10000  # Adjust based on memory constraints
    if n > MAX_MEMO_SIZE:
        return large_fibonacci(n)  # Alternative implementation for large n
        
    # Create a memoization table and initialize with -1
    memo = [-1] * (n + 1)
    
    # Call the utility function
    return nth_fibonacci_util(n, memo)


def large_fibonacci(n):
    """"""
    Calculate Fibonacci numbers for very large n using matrix exponentiation.
    This has O(log n) time complexity.
    """"""
    def matrix_multiply(A, B):
        a = A[0][0] * B[0][0] + A[0][1] * B[1][0]
        b = A[0][0] * B[0][1] + A[0][1] * B[1][1]
        c = A[1][0] * B[0][0] + A[1][1] * B[1][0]
        d = A[1][0] * B[0][1] + A[1][1] * B[1][1]
        return [[a, b], [c, d]]
    
    def matrix_power(A, n):
        if n == 1:
            return A
        if n % 2 == 0:
            return matrix_power(matrix_multiply(A, A), n // 2)
        else:
            return matrix_multiply(A, matrix_power(matrix_multiply(A, A), (n - 1) // 2))
    
    if n == 0:
        return 0
    F = [[1, 1], [1, 0]]
    return matrix_power(F, n)[0][1]

Standards:

• Algorithm Efficiency
• Memory Management
• Large Input Handling

[refacto-test]

Redundant Function Definition

The PR removes an inefficient recursive Fibonacci implementation without memoization. This is good as it had exponential O(2^n) time complexity which would cause severe performance issues for larger inputs.

Standards

• Algorithm Efficiency
• Time Complexity Optimization

[refacto-test]

Duplicate Function Removal

The PR removes a duplicate iterative Fibonacci implementation. Having multiple implementations of the same function with different algorithms creates maintenance issues and confusion about which one to use.

Standards

• DRY Principle
• Code Duplication
• SOLID - Single Responsibility

[coderabbitai]

Walkthrough

The Fibonacci computation was refactored to a memoized approach. A new helper nth_fibonacci_util(n, memo) was added, wrapped by nth_fibonacci(n) that initializes the memo table. Prior recursive and iterative implementations were removed. The main execution now prints the result with an inline expected output comment.

Changes

Cohort / File(s)	Change Summary
Memoized Fibonacci refactor main.py	Replaced naive recursive and iterative DP Fibonacci with memoized helper nth_fibonacci_util(n, memo) and wrapper nth_fibonacci(n) initializing memo = [-1]*(n+1); added memo lookups and stores; updated main print with expected output comment; removed prior implementations.

Sequence Diagram(s)

sequenceDiagram
    participant Main
    participant nth_fibonacci
    participant nth_fibonacci_util

    Main->>nth_fibonacci: nth_fibonacci(n)
    nth_fibonacci->>nth_fibonacci_util: nth_fibonacci_util(n, memo)

    alt memo hit
        nth_fibonacci_util-->>nth_fibonacci: return memo[n]
    else memo miss
        nth_fibonacci_util->>nth_fibonacci_util: recurse(n-1, memo)
        nth_fibonacci_util->>nth_fibonacci_util: recurse(n-2, memo)
        nth_fibonacci_util->>nth_fibonacci_util: memo[n] = sum
        nth_fibonacci_util-->>nth_fibonacci: return memo[n]
    end
    nth_fibonacci-->>Main: result

Loading

Estimated code review effort

🎯 2 (Simple) | ⏱️ ~8 minutes

Poem

I hopped through loops of n and two,
Cached each step like morning dew.
No more wandering twice the trail,
Memo crumbs ensure we sail.
Fibonacci’s climb, quick as a wink—
Thump-thump! says my rabbit link. 🐇✨

✨ Finishing Touches

• 📝 Generate Docstrings

🧪 Generate unit tests

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch atharvsabdeai-patch-1

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[coderabbitai]

Actionable comments posted: 1

🧹 Nitpick comments (3)

main.py (3)

29-29: Remove brittle inline “expected output” comment

Inline expected-output comments tend to drift and mislead. Prefer no comment here, or move the expectation into a unit test.

-    print(result) # Output: 5
+    print(result)

---

2-15: Recursive memoization risks RecursionError for large n; consider iterative or fast-doubling

Top-down recursion has call depth O(n) and can hit Python’s recursion limit around n ≈ 1000. An iterative approach avoids this while staying O(n) time, O(1) space; fast-doubling is O(log n).

Iterative variant (outside the changed hunk):

def fibonacci_iter(n: int) -> int:
    if not isinstance(n, int) or n < 0:
        raise ValueError("n must be a non-negative integer")
    a, b = 0, 1
    for _ in range(n):
        a, b = b, a + b
    return a

You can keep the current API by having nth_fibonacci call fibonacci_iter.

---

26-30: Add unit tests instead of relying on main print checks

Relying on manual prints is brittle. Add tests for edge cases: n=0,1,5, negative inputs (expect ValueError), and a larger n to guard against regressions.

I can generate a unittest or pytest suite covering these cases. Want me to open a follow-up PR with tests?

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 78a3159 and 676f21f.

📒 Files selected for processing (1)

• main.py (1 hunks)

[coderabbitai]

⚠️ Potential issue

Validate input: reject negative or non-integer n

Currently, passing a negative n returns a negative value (Line 4) which is incorrect for Fibonacci. Add input validation to ensure n is a non-negative integer.

Suggested replacement (outside the changed hunk):

def nth_fibonacci(n):
    # Validate input early
    if not isinstance(n, int) or n < 0:
        raise ValueError("n must be a non-negative integer")

    if n <= 1:
        return n

    memo = [-1] * (n + 1)
    return nth_fibonacci_util(n, memo)

🤖 Prompt for AI Agents

In main.py around lines 19 to 24, add input validation to reject negative or
non-integer n: check if n is an int and n >= 0 and raise ValueError("n must be a
non-negative integer") for invalid inputs; also handle the trivial cases by
returning n when n <= 1 before creating the memo array so the function doesn't
compute or return incorrect values for negative or small inputs.