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Compare Performance to find fibonacci 2
(version: 0)
Comparing performance of:
Using Go Bottom Up vs Using Memoization
Created:
2 years ago
by:
Guest
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Script Preparation code:
var n = 8;
Tests:
Using Go Bottom Up
function getNthNumber() { let prev = 0; let afterPrev = 1; let current = 1; for(let i = 1; i <= n; i++) { current = prev + afterPrev afterPrev = prev prev = current } return current } getNthNumber()
Using Memoization
function getNthNumber() { const memoize = []; const cb = (n) => { if(n <= 2 ) return 1; if(memoize[n]) return memoize[n]; memoize[n] = cb(n - 1) + cb(n - 2); return memoize[n]; } return cb(n) } getNthNumber()
Rendered benchmark preparation results:
Suite status:
<idle, ready to run>
Run tests (2)
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Test case name
Result
Using Go Bottom Up
Using Memoization
Fastest:
N/A
Slowest:
N/A
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Autogenerated LLM Summary
(model
llama3.2:3b
, generated one year ago):
**Benchmark Overview** The provided JSON represents a benchmark for comparing the performance of two approaches to calculate the nth Fibonacci number: "Go Bottom Up" and "Memoization". The benchmark is designed to test how efficiently each approach can compute Fibonacci numbers. **Options Compared** Two options are compared: 1. **Go Bottom Up**: This approach uses a bottom-up dynamic programming technique. It starts with the base cases (Fibonacci numbers 0 and 1) and iteratively calculates subsequent numbers by summing the previous two. 2. **Memoization**: This approach uses memoization, a caching mechanism that stores the results of expensive function calls to avoid redundant calculations. **Pros and Cons** * **Go Bottom Up**: + Pros: Simple to implement, efficient for large inputs. + Cons: May use excessive memory for large inputs due to the recursive nature of the calculation. * **Memoization**: + Pros: Can handle large inputs efficiently by caching results, reduces redundant calculations. + Cons: Requires additional memory for storing cache values. **Library/Functionality** In this benchmark, no external libraries or functions are used besides JavaScript. The `getNthNumber()` function is a custom implementation of the Fibonacci number calculation for each approach. **Special JS Features/Syntax** There are no special JavaScript features or syntax used in these benchmarks. They only utilize basic JavaScript constructs such as loops, variables, and functions. **Alternative Approaches** Other approaches to calculate the nth Fibonacci number include: * **Recursive Approach**: A top-down dynamic programming technique that uses recursion to calculate Fibonacci numbers. * **Iterative Approach with Lookahead**: An optimization of the bottom-up approach by calculating multiple subsequent numbers in each iteration. * **Matrix Exponentiation**: A mathematical technique for calculating Fibonacci numbers using matrix exponentiation, which has a time complexity of O(log n). These alternative approaches may have different performance characteristics and use cases compared to the "Go Bottom Up" and "Memoization" approaches tested in this benchmark.
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