Benchmark: Find the stray_eric

Script Preparation code:

function stray_happy(numbers) {
  numbers = numbers.sort((a, b) => a - b);
  if (numbers[0] !== numbers[1]) {
    return numbers[0];
  }
  else {
    return numbers[numbers.length - 1];
  }
}


function stray_heretic(numbers) {
  return numbers.find(i => numbers.filter(j => j === i).length === 1);
}


function stray_shaye(numbers) {
  return +numbers.sort((a, b) => a - b)
  	.filter((n,i,a) => (i === 0 && a[0] !==a[1]) || (i === a.length-1 && a[a.length-1] !== a[a.length-2]));
}

function stray_user(a) {
  return a.find(v => a[0] != a[1] ? v != a[2] : v != a[0])
}

function stray_eric(numbers) {
  if (numbers[0] != numbers[1]){
    if (numbers[1] != numbers[2]){
      return numbers[1];
    } else {
      return numbers[0];
    }
  }
  return numbers.find(i => i !== numbers[0]);
}

N = 100
var numbers = Array(N).fill(1);
numbers[Math.floor(Math.random()*N)] = 2;

Tests:

stray_happy
var n = stray_happy(numbers)
stray_heretic
var n = stray_heretic(numbers)
stray_shaye
var n = stray_shaye(numbers)
stray_user
var n = stray_user(numbers)
stray_eric
var n = stray_eric(numbers)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
stray_happy
stray_heretic
stray_shaye
stray_user
stray_eric

Fastest: N/A

Slowest: N/A

Latest run results:

No previous run results

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and explain what's being tested.

**Benchmark Definition**

The benchmark definition is a JSON object that defines the script to be executed. It includes four different functions:

1. `stray_happy`: This function sorts the input array in ascending order using the `sort()` method with a custom comparison function, and then checks if the first two elements are equal. If they're not, it returns the first element.
2. `stray_heretic`: This function uses the `find()` method to find the only element in the array that appears only once. It does this by filtering the array to get an array of indices where each element appears alone.
3. `stray_shaye`: This function also sorts the input array and then filters it to find the two elements that are not equal. The filter uses a custom callback function that checks for these conditions.
4. `stray_user`: This function is similar to `stray_heretic`, but it uses a different approach to find the single element in the array.

**Options Compared**

The benchmark compares the performance of each function under different conditions:

* Sorting and filtering approaches
* Use of custom comparison functions (in `stray_happy` and `stray_shaye`)
* Use of regular expressions (none)

**Pros and Cons**

Here are some pros and cons for each approach:

1. **Sorting and filtering**: This approach is straightforward, but it can be inefficient if the input array is large, since sorting has a time complexity of O(n log n). However, filtering can also have a high overhead.
2. **Custom comparison functions**: These can provide better performance for specific use cases, but they require more code and may not generalize well to other situations.
3. **Regular expressions**: None of the functions used regular expressions.

**Other Considerations**

* The input array is always filled with 1s, except for a single element that's randomly set to 2.
* The sorting order in `stray_happy` is ascending.
* The filter condition in `stray_shaye` checks for two specific conditions: the first element is not equal to the second element, or the last element is not equal to the second-to-last element.

**Library and Special JS Features**

None of the functions use any libraries or special JavaScript features. They only rely on standard ECMAScript features.

**Alternatives**

If you wanted to rewrite this benchmark with different approaches, here are some alternatives:

1. **Use a more efficient sorting algorithm**: Instead of using `sort()` and filtering, you could use a sorting algorithm like quicksort or mergesort, which have better performance characteristics for large datasets.
2. **Use a specialized library**: If the problem requires advanced mathematical operations (e.g., finding the single element in an array), you might consider using a specialized library that provides optimized implementations of these operations.
3. **Use parallel processing**: You could rewrite the benchmark to use multiple threads or processes to sort and filter the data simultaneously, which could provide significant performance gains for large datasets.

Overall, this benchmark tests different approaches to finding a single element in an array, including sorting, filtering, and custom comparison functions.

Related benchmarks:

Find the stray_eric (version: 0)

Comparing performance of: stray_happy vs stray_heretic vs stray_shaye vs stray_user vs stray_eric

Created: 6 years ago by: Guest

Jump to the latest result

stray_happy

stray_heretic

stray_shaye

stray_user

stray_eric

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):