Benchmark: toFixed vs toPrecision vs Math.round() Equality Comparisons

Script Preparation code:

var someFloat = 0.123456789;

Tests:

toFixed(4)
someFloat.toFixed(4) === someFloat.toFixed(4);
toPrecision(4).toString()
someFloat.toPrecision(4) === someFloat.toPrecision(4)
(Math.round(*10000)/10000)
(Math.round(someFloat*10000)/10000) === (Math.round(someFloat*10000)/10000)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
toFixed(4)
toPrecision(4).toString()
(Math.round(*10000)/10000)

Fastest: N/A

Slowest: N/A

Latest run results:

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

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/142.0.0.0 Safari/537.36

Browser/OS: Chrome 142 on Windows

View result in a separate tab

Test name	Executions per second
toFixed(4)	9131023.0 Ops/sec
toPrecision(4).toString()	10455257.0 Ops/sec
(Math.round(*10000)/10000)	180134960.0 Ops/sec

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

LLMs can make mistakes. Check important info.

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

**Benchmark Overview**

The benchmark is designed to compare the performance of three different approaches for equality comparisons on floating-point numbers:

1. `toFixed()`
2. `toPrecision()`
3. `(Math.round() * 10000) / 10000`

These approaches are compared in terms of their execution time, measured by the number of executions per second.

**Options Compared**

The three options being compared are:

* `toFixed(4)`: Uses the `toFixed()` method to round the float to a specific precision (in this case, 4 decimal places).
* `toPrecision(4).toString()`: First uses `toPrecision(4)` to get a string representation of the float with a specific number of significant figures, and then converts it back to a string using `toString()`.
* `(Math.round(*10000)/10000)`: Uses the `Math.round()` function to round the float to a certain decimal place (in this case, 4 decimal places), scaled by multiplying and dividing by 10000.

**Pros and Cons of Each Approach**

1. **toFixed(4)**:
	* Pros: Simple and concise syntax.
	* Cons: May not work correctly for very large or small numbers due to floating-point precision issues.
2. **toPrecision(4).toString()**:
	* Pros: Provides a more precise control over the number of significant figures.
	* Cons: Adds an extra step of converting back to a string, which can be slower.
3. **(Math.round(*10000)/10000)**:
	* Pros: Avoids potential issues with floating-point precision by scaling and rounding separately.
	* Cons: Has a more complex syntax and may not be as readable.

**Library Used**

None are explicitly mentioned in the benchmark definition, but it's worth noting that `toFixed()` and `toPrecision()` are built-in methods in JavaScript.

**Special JS Feature or Syntax**

The only special syntax used is the multiplication and division operator (`*`) with a large number (10000) to scale the result of `Math.round()`. This is not typically used in everyday JavaScript code, but it's a valid way to achieve a certain level of precision.

**Other Alternatives**

If you need to compare floating-point numbers for equality, other alternatives could include:

* Using a library like [FastEqual](https://github.com/valdik/fast-equal) or [equalize](https://github.com/mathiasforsberg/equalize), which are designed specifically for comparing floats.
* Implementing your own custom comparison function using bitwise operations or other mathematical techniques.

It's worth noting that the choice of approach will depend on the specific use case and requirements of the project.

Related benchmarks:

toFixed vs toPrecision vs Math.round() Equality Comparisons (version: 0)

Comparing performance of: toFixed(4) vs toPrecision(4).toString() vs (Math.round(*10000)/10000)

Created: 2 years ago by: Guest

Jump to the latest result

toFixed(4)

toPrecision(4).toString()

(Math.round(*10000)/10000)

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

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