Benchmark: Triangular RV Symmetric vs. Asymmetric

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 triangularSymmetric(mean, radius) {
  let x;
  const doubleRadiusSquared = 2 * radius * radius;
  const rand = Math.random();
  if (rand < 0.5) {
    x = mean - radius + Math.sqrt(rand*doubleRadiusSquared)
  } else {
    x = mean + radius - Math.sqrt((1 - rand)*doubleRadiusSquared)
  }
  return x;
}

Tests:

Triangular
triangular(-1, 1, 0)
Triangular Symmetric
triangularSymmetric(0, 1)

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
Triangular
Triangular Symmetric

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):

LLMs can make mistakes. Check important info.

I'd be happy to explain the benchmark and its options.

**Benchmark Overview**

The provided JSON represents two JavaScript microbenchmarks: `triangular` and `triangularSymmetric`. Both functions aim to generate random numbers from triangular distributions, which are commonly used in statistical analysis and simulation. The main difference between the two lies in their approach:

*   **Triangular Distribution**: This method generates a random number from a triangular distribution with parameters `a`, `b`, and `c`. It does so by calculating a scaling factor `f` based on the three parameters, then using `Math.random()` to determine whether to add or subtract the square root of a scaled value from the minimum parameter (`a`). If the random value is less than `f`, it adds; otherwise, it subtracts.
*   **Symmetric Triangular Distribution**: This method generates a random number from a symmetric triangular distribution centered around a mean value `mean` with a radius `radius`. The approach involves calculating the square of twice the radius (`doubleRadiusSquared`) and using this value to determine whether the random number should be added or subtracted from the mean.

**Options Compared**

The benchmark compares two approaches for generating random numbers from triangular distributions:

1.  **Triangular Distribution** (using `triangular` function)
2.  **Symmetric Triangular Distribution** (using `triangularSymmetric` function)

These two methods differ in their approach to generating the random values.

**Pros and Cons of Each Approach**

*   **Triangular Distribution:**
    *   Pros:
        +   Simpler implementation
        +   Can be more efficient for certain use cases due to fewer mathematical operations required.
    *   Cons:
        +   Less intuitive and harder to understand, especially for those without experience with triangular distributions.
        +   May have issues with precision or range due to the use of `Math.random()`.
*   **Symmetric Triangular Distribution:**
    *   Pros:
        +   More intuitive and easier to understand, as it's based on a known distribution.
        +   Provides more control over the shape and properties of the generated numbers.
    *   Cons:
        +   More complex implementation
        +   May require additional mathematical operations or calculations.

**Other Considerations**

When choosing between these approaches, consider factors like:

*   **Performance**: The `triangular` function might be faster due to fewer operations, but this can vary depending on the specific use case and hardware.
*   **Precision**: If you need precise values within a certain range, the `symmetricTriangular` approach might be better suited.
*   **Intuitiveness**: For developers who are not familiar with triangular distributions, the `symmetricTriangular` approach might be more accessible.

**Library Usage**

In this benchmark, there is no explicit mention of any libraries. However, if the code uses a library like NumJS or other libraries for mathematical operations, it's worth noting that these libraries can provide optimized and efficient implementations of common algorithms.

**Special JS Features**

This benchmark does not explicitly use any special JavaScript features beyond the standard `Math.random()` function. If additional features are used in the implementation (e.g., ES6 syntax), they might be included in future benchmarks or updates.

**Alternatives**

If you're looking for alternatives to these approaches, consider:

*   **Box-Muller Transform**: A method that uses the inverse CDF of a normal distribution to generate random numbers from any distribution.
*   **Inverse CDF Method**: An approach that uses the inverse cumulative distribution function (CDF) of the desired distribution to generate samples.

Latest run results:

Run details: (Test run date: 3 years ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36

Browser/OS: Chrome 91 on Windows

View result in a separate tab

Test name	Executions per second
Triangular	3412575.5 Ops/sec
Triangular Symmetric	3409863.5 Ops/sec

Related benchmarks:

Triangular RV Symmetric vs. Asymmetric (version: 0)

Comparing performance of: Triangular vs Triangular Symmetric

Created: 3 years ago by: Guest

Jump to the latest result

Triangular

Triangular Symmetric

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated 28 days ago):