Benchmark: math standard deviation

HTML Preparation code:

<script src='https://cdnjs.cloudflare.com/ajax/libs/mathjs/7.5.1/math.js'></script>

Script Preparation code:

function std1(arr) {
	return math.std(arr)
}

function std2(arr) {
    if(arr.length === 1) return 0;

  	let sum = 0, varianceSum = 0;

  	for(let i=0; i<arr.length; i++) {
      sum += arr[i];
    }

	const mean = sum / arr.length;

  	for(let i=0; i<arr.length; i++) {
      	let x = arr[i] - mean;
    	varianceSum += x*x;
    }

  	const sampleVariance = varianceSum / (arr.length - 1);

	return Math.sqrt(sampleVariance);
}

Tests:

std1 - mathjs
std1([1,2,3,4])
std2
std2([1,2,3,4])

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
std1 - mathjs
std2

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (X11; Linux x86_64; rv:136.0) Gecko/20100101 Firefox/136.0

Browser/OS: Firefox 136 on Linux

View result in a separate tab

Test name	Executions per second
std1 - mathjs	549545.5 Ops/sec
std2	11282543.0 Ops/sec

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

LLMs can make mistakes. Check important info.

**Benchmark Explanation**

The provided benchmark measures the performance of two different implementations of calculating the standard deviation of an array: `std1` and `std2`.

**std1**: This implementation uses the `math.std()` function from the MathJS library, which is a JavaScript port of the popular Python library NumPy. MathJS provides a convenient way to perform mathematical operations on arrays.

**std2**: This implementation calculates the standard deviation manually using the following steps:

1. Calculate the sum of all elements in the array.
2. Calculate the mean (average) of the array by dividing the sum by the number of elements.
3. Iterate over the array, subtracting the mean from each element and squaring the result.
4. Calculate the sample variance by dividing the sum of squared differences by the number of elements minus one.
5. Return the square root of the sample variance.

**Options Compared**

The benchmark compares two options:

1. Using the `math.std()` function from MathJS (std1).
2. Implementing the standard deviation calculation manually (std2).

**Pros and Cons**

**MathJS (std1)**

Pros:

* Conventional way to calculate standard deviation, widely understood and used.
* Built-in library provides a simple and efficient implementation.

Cons:

* Dependence on an external library (MathJS) that may not be available or up-to-date in all environments.
* May have performance overhead due to the need to load the library.

**Manual Implementation (std2)**

Pros:

* No dependence on external libraries, making it more portable and self-contained.
* Can provide better control over performance and optimization.

Cons:

* Requires manual implementation of the standard deviation calculation algorithm, which can be error-prone and less efficient.

**Other Considerations**

The benchmark also measures the impact of different browsers (Chrome 86) and devices (Desktop Mac OS X 10.14.3) on the execution speed of the two implementations. This helps to identify any browser or platform-related performance issues.

**Library: MathJS**

MathJS is a JavaScript library that provides a set of mathematical functions, including those for linear algebra, statistics, and more. It aims to provide an efficient and convenient way to perform mathematical operations on arrays, similar to NumPy in Python.

In this benchmark, the `math.std()` function from MathJS is used to calculate the standard deviation of an array. The library provides a simple and efficient implementation, but it requires loading the external library, which may introduce performance overhead.

Related benchmarks:

math standard deviation (version: 0)

Comparing performance of: std1 - mathjs vs std2

Created: 5 years ago by: Guest

Jump to the latest result

std1 - mathjs

std2

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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