Benchmark: Triangular RV - MeasureThat.net

Script Preparation code:

function triangular(a, b, c) {
  const f = (c - a)/(b - a);
  const rand = Math.random();
  let x;
  if (rand < f) {
    x = a + Math.sqrt(rand*(b - a)*(c - a))
  } else {
    x = b - Math.sqrt((1 - rand)*(b - a)*(b - c))
  }
  return x;
}

function linear(mean, radius) {
  let result = undefined;
  while (!result) {
    //const rand1 = Math.random()*2*radius - radius + mean;
    const rand1 = Math.random()*2 - 1;
    const x = rand1*radius + mean
    const rand2 = Math.random();
    if (Math.random() > Math.abs(rand1)) {
      result = x;
    }
  }
  return result;
}

Tests:

Rejection
linear(0, 1)
Formula
triangular(-1, 1, 0)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Rejection
Formula

Fastest: N/A

Slowest: N/A

Latest run results:

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

LLMs can make mistakes. Check important info.

I'll break down the provided JSON data for you, explaining what's being tested and the pros and cons of different approaches.

**Benchmark Definition**

The benchmark definition is represented by two JavaScript functions: `triangular(a, b, c)` and `linear(mean, radius)`. These functions generate random numbers using different methods:

1. `triangular(a, b, c)`: This function generates a random number from a triangular distribution with three parameters (a, b, and c). The distribution is defined by the ratio of `(c - a)/(b - a)` to determine which branch of the function to take based on the generated random number (`Math.random()`).
2. `linear(mean, radius)`: This function generates a random number from a linear distribution with two parameters (mean and radius). However, there's an additional check using `Math.random() > Math.abs(rand1)` to determine whether the generated value is accepted or rejected.

**Options Compared**

The benchmark compares different execution strategies for these two functions:

* Rejection: This option uses the `linear(mean, radius)` function with the rejection mechanism.
* Formula: This option uses the `triangular(a, b, c)` function without any modifications.

**Pros and Cons of Different Approaches**

1. **Rejection (Linear)**:
	* Pros:
		+ Can be faster for small ranges due to early rejection.
		+ Easier to optimize using techniques like interval arithmetic.
	* Cons:
		+ May lead to slower performance for larger ranges due to repeated calculations.
		+ Requires careful consideration of the acceptance probability to maintain a reasonable balance between speed and accuracy.
2. **Formula (Triangular)**:
	* Pros:
		+ Typically faster than rejection methods, especially for large ranges.
		+ Often preferred in mathematical simulations where exactness is important.
	* Cons:
		+ May require more complex calculations, which can be slower for smaller ranges.

**Library and Purpose**

There is no explicit library mentioned in the benchmark definition. However, the `Math.random()` function is a built-in JavaScript method used to generate pseudorandom numbers.

**Special JS Feature or Syntax**

The benchmark uses some advanced JavaScript features:

* Arrow functions (`=>`) in the `linear` function.
* Template literals (`\r\n` and `\r`) for multiline strings.

If you're not familiar with these features, don't worry! They're widely supported in modern JavaScript environments.

**Other Alternatives**

For similar benchmarking purposes, consider using:

1. `Math.random()`-based Monte Carlo simulations (e.g., generating random points within a rectangle).
2. Custom libraries or frameworks for numerical computations (e.g., NumJS, Fast.js).
3. Benchmarking tools and frameworks specifically designed for JavaScript performance measurement (e.g., jsperf, benchmark.js).

Keep in mind that the choice of alternative depends on your specific use case, target environment, and requirements.

I hope this explanation helps!

Related benchmarks:

Triangular RV (version: 0)

Comparing performance of: Rejection vs Formula

Created: 4 years ago by: Guest

Jump to the latest result

Rejection

Formula

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):