Code Best Practices Checker | Python

Python Code Refactoring for Fibonacci Sequence with Memoization

Refactor Python code to optimize Fibonacci calculation using memoization, eliminating redundant recalculations for improved performance.


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Prompt

def fibonacci(n):
    if n <= 1:
        return n
    else:
        return fibonacci(n-1) + fibonacci(n-2)

Answer

Language/Technology Identification:

The provided code snippet is written in Python.

Code Refactoring:

  1. Avoiding Recalculation:
    • Issue: The current implementation recalculates Fibonacci values multiple times.
    • Refactoring: Utilize memoization to store already calculated values to improve performance.

Documentation of Changes:

  1. Avoiding Recalculation:
    • Issue: The original code recalculates Fibonacci values, leading to inefficiency.
    • Refactoring Reasoning: Implement memoization to store computed Fibonacci values, reducing redundant calculations and optimizing performance.

Refactored Code (Python):

def fibonacci(n, memo={}):
    if n in memo:
        return memo[n]
    if n <= 1:
        return n
    else:
        result = fibonacci(n-1, memo) + fibonacci(n-2, memo)
        memo[n] = result
        return result

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Description

Refactor Python code to optimize Fibonacci calculation using memoization, eliminating redundant recalculations for improved performance.

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