Benchmark: min/max coords - MeasureThat.net

Script Preparation code:

var xys = Array.from({length: 5000}, () => Math.random() * 500 | 0)

Tests:

with Math
const xs = xys.filter((v,i) => i%2 === 0) const ys = xys.filter((v,i) => i%2 === 1) const dim = [Math.min.apply(null,xs), Math.min.apply(null,ys), Math.max.apply(null,xs), Math.max.apply(null,ys)]
with for
const dim = [0, 0, Infinity, Infinity] for(let i = 0; i < xys.length; i+=2){ const x = xys[i], y = xys[i+1] x < dim[0] && (dim[0] = x) y < dim[1] && (dim[1] = y) x > dim[2] && (dim[2] = x) y > dim[3] && (dim[3] = y) }

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
with Math
with for

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 its test cases.

**Benchmark Overview**

MeasureThat.net is a website that allows users to create and run JavaScript microbenchmarks. The provided benchmark measures the performance of finding the minimum and maximum coordinates in an array of random x-y values.

**Script Preparation Code**

The script preparation code generates an array of 5000 random x-y values using `Math.random() * 500 | 0`. This creates a large dataset for testing.

**Html Preparation Code**

There is no HTML preparation code provided, which means that the benchmark only runs in a JavaScript environment and does not involve any DOM manipulation or rendering.

**Test Cases**

There are two test cases:

1. **"with Math"`**
This test case uses the built-in `Math` functions to find the minimum and maximum coordinates. The script first filters the array into even-indexed values (`xs`) and odd-indexed values (`ys`). Then, it uses `Math.min()` and `Math.max()` to find the minimum and maximum values in each of these arrays. Finally, it combines the results to form an array `[dim]` containing the minimum and maximum coordinates.

Pros:

* Simple and concise code
* Uses built-in functions that are likely to be optimized

Cons:

* May not perform well for very large datasets due to function call overhead
* May not be suitable for certain edge cases or corner cases

2. **"with for"`**
This test case uses a traditional `for` loop to iterate over the array and update the minimum and maximum coordinates.

Pros:

* May perform better for very large datasets due to loop optimization
* Can handle edge cases and corner cases more effectively than the `Math` approach

Cons:

* More verbose code compared to the `with Math` approach
* May introduce additional overhead from loop control logic

**Library Used**

There is no library explicitly mentioned in the benchmark, but the use of `Array.from()` suggests that it may be using a modern JavaScript standard feature. However, since `Array.from()` was introduced in ECMAScript 2015 (ES6), this benchmark likely uses ES6 or later syntax.

**Special JS Feature/Syntax**

Since the benchmark does not explicitly mention any special JS features or syntax beyond ES6 syntax for `Array.from()`, we can assume that it is using standard JavaScript syntax. If it were to use advanced features like async/await, Promises, or Web Workers, those would be mentioned in the code.

**Other Alternatives**

Some alternative approaches to finding minimum and maximum coordinates could include:

* Using a custom sorting function with `Array.prototype.sort()`
* Using a library like Lodash for its `minBy()` and `maxBy()` functions
* Using a specialized data structure, such as a heap or priority queue

However, the "with Math" and "with for" approaches mentioned in the benchmark are likely to be among the most efficient and straightforward solutions.

Related benchmarks:

min/max coords (version: 0)

Comparing performance of: with Math vs with for

Created: 3 years ago by: Guest

Jump to the latest result

with Math

with for

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):