Benchmark: Find the stray v2b

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_user_2(a) {
	a.find(a[0] != a[1] ? (v, i, a) => v != a[2] : (v, i, a) => v != a[0])
}


function stray_user_3(a) {
    var a0 = a[0], a1 = a[1], a2 = a[2];
	a.find(a0 != a1 ? (v, i, a) => v != a2 : (v, i, a) => v != a0)
}

function stray_user_4(a) {
    var a0 = a[0], a1 = a[1], a2 = a[2];
    if (a0 != a1) a.find((v,i,a) => v != a2);
    else a.find((v,i,a) => v != a0);
}


var numbers = [1,1,1,1,1,1,1,2,1,1,1,1,1,1,1]

​x
 
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_user_2(a) {    a.find(a[0] != a[1] ? (v, i, a) => v != a[2] : (v, i, a) => v != a[0])}​​function stray_user_3(a) {    var a0 = a[0], a1 = a[1], a2 = a[2];    a.find(a0 != a1 ? (v, i, a) => v != a2 : (v, i, a) => v != a0)}​function stray_user_4(a) {    var a0 = a[0], a1 = a[1], a2 = a[2];    if (a0 != a1) a.find((v,i,a) => v != a2);    else a.find((v,i,a) => v != a0);}​​var numbers = [1,1,1,1,1,1,1,2,1,1,1,1,1,1,1]​​

Tests:

stray_happy
var n = stray_happy(numbers)
var n = stray_happy(numbers)
stray_heretic
var n = stray_heretic(numbers)
var n = stray_heretic(numbers)
stray_shaye
var n = stray_shaye(numbers)
var n = stray_shaye(numbers)
stray_user
var n = stray_user(numbers)
var n = stray_user(numbers)
stray_user_2
var n = stray_user_2(numbers)
var n = stray_user_2(numbers)
stray_user_3
var n = stray_user_3(numbers)
var n = stray_user_3(numbers)
stray_user_4
var n = stray_user_4(numbers)
var n = stray_user_4(numbers)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Experimental features:

Memory measurements supported only in Chrome.
For precise memory measurements Chrome must be launched with --enable-precise-memory-info flag.
More information: Monitoring JavaScript Memory

Test case name	Result
stray_happy
stray_heretic
stray_shaye
stray_user
stray_user_2
stray_user_3
stray_user_4

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: 5 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36

Browser/OS: Chrome 75 on Mac OS X 10.14.5

View result in a separate tab

Test name	Executions per second
stray_happy	1793652.2 Ops/sec
stray_heretic	61335.9 Ops/sec
stray_shaye	535139.2 Ops/sec
stray_user	7278276.0 Ops/sec
stray_user_2	3486960.0 Ops/sec
stray_user_3	3383871.8 Ops/sec
stray_user_4	7610989.5 Ops/sec

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):

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

Benchmark Definition

The benchmark is based on a JavaScript function that finds the stray element in an array of numbers. The function takes an array numbers as input and returns the stray element.

Individual Test Cases

There are six individual test cases, each testing a different implementation of finding the stray element:

stray_happy: uses a simple sorting approach to find the stray element.
stray_heretic: uses the find() method with a callback function to find the stray element.
stray_shaye: uses a combination of sorting and filtering to find the stray element.
stray_user: uses a custom implementation with multiple conditions to find the stray element.
stray_user_2: similar to stray_user, but with some differences in the logic.
stray_user_3 and stray_user_4: two more custom implementations with slightly different approaches.

Options Compared

The benchmark is comparing the performance of six different approaches to finding the stray element:

Sorting-based approach (stray_happy)
Using find() method (stray_heretic)
Combination of sorting and filtering (stray_shaye)
Custom implementation with multiple conditions (stray_user and its variants)

Pros and Cons

Here are some pros and cons for each approach:

Sorting-based approach (stray_happy):
- Pros: simple to implement, efficient.
- Cons: may not be suitable for large datasets, can be slow for certain inputs.
Using find() method (stray_heretic):
- Pros: concise and expressive, can handle large datasets.
- Cons: may have performance issues if the input array is very large.
Combination of sorting and filtering (stray_shaye):
- Pros: efficient for large datasets, easy to implement.
- Cons: requires extra memory for the temporary sorted array.
Custom implementation with multiple conditions (stray_user and variants):
- Pros: flexible, can be optimized for specific use cases.
- Cons: may have performance issues due to the overhead of custom logic.

Performance Results

The benchmark results show the execution speed for each test case on a Macintosh machine running Chrome 75. The top performer is stray_heretic, followed by stray_shaye and stray_user_4. The other variants have slower performance, with stray_happy being one of the slowest.

Overall, the benchmark suggests that using the find() method can be a good approach for finding stray elements in large datasets, while custom implementations with multiple conditions may offer better performance for specific use cases. However, it's essential to note that these results may vary depending on the specific input data and hardware configuration.

LLMs can make mistakes. Check important info.

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

**Benchmark Definition**

The benchmark is based on a JavaScript function that finds the stray element in an array of numbers. The function takes an array `numbers` as input and returns the stray element.

**Individual Test Cases**

There are six individual test cases, each testing a different implementation of finding the stray element:

1. `stray_happy`: uses a simple sorting approach to find the stray element.
2. `stray_heretic`: uses the `find()` method with a callback function to find the stray element.
3. `stray_shaye`: uses a combination of sorting and filtering to find the stray element.
4. `stray_user`: uses a custom implementation with multiple conditions to find the stray element.
5. `stray_user_2`: similar to `stray_user`, but with some differences in the logic.
6. `stray_user_3` and `stray_user_4`: two more custom implementations with slightly different approaches.

**Options Compared**

The benchmark is comparing the performance of six different approaches to finding the stray element:

* Sorting-based approach (`stray_happy`)
* Using `find()` method (`stray_heretic`)
* Combination of sorting and filtering (`stray_shaye`)
* Custom implementation with multiple conditions (`stray_user` and its variants)

**Pros and Cons**

Here are some pros and cons for each approach:

* **Sorting-based approach (stray_happy)**:
	+ Pros: simple to implement, efficient.
	+ Cons: may not be suitable for large datasets, can be slow for certain inputs.
* **Using `find()` method (stray_heretic)**:
	+ Pros: concise and expressive, can handle large datasets.
	+ Cons: may have performance issues if the input array is very large.
* **Combination of sorting and filtering (stray_shaye)**:
	+ Pros: efficient for large datasets, easy to implement.
	+ Cons: requires extra memory for the temporary sorted array.
* **Custom implementation with multiple conditions (stray_user and variants)**:
	+ Pros: flexible, can be optimized for specific use cases.
	+ Cons: may have performance issues due to the overhead of custom logic.

**Performance Results**

The benchmark results show the execution speed for each test case on a Macintosh machine running Chrome 75. The top performer is `stray_heretic`, followed by `stray_shaye` and `stray_user_4`. The other variants have slower performance, with `stray_happy` being one of the slowest.

Overall, the benchmark suggests that using the `find()` method can be a good approach for finding stray elements in large datasets, while custom implementations with multiple conditions may offer better performance for specific use cases. However, it's essential to note that these results may vary depending on the specific input data and hardware configuration.

Related benchmarks:

Find the stray v2b (version: 0)

Comparing performance of: stray_happy vs stray_heretic vs stray_shaye vs stray_user vs stray_user_2 vs stray_user_3 vs stray_user_4

Created: 5 years ago by: Guest

Jump to the latest result

stray_happy

stray_heretic

stray_shaye

stray_user

stray_user_2

stray_user_3

stray_user_4

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 6 months ago):