gleam_stats - v1.0.0

gleam_stats/distributions/triangular

Functions related to continuous triangular random variables.

Functions

triangular_cdf

</> 
pub fn triangular_cdf(x: Float, a: Float, b: Float, c: Float) -> Result(
  Float,
  String,
)

Evaluate, at a certain point, the cumulative distribution function (cdf) of a continuous triangular random variable that takes values in the interval ‘[a, b]’ and has mode (a peak at value) ‘c’.

Example: 
 import gleam_stats/distributions/triangular
 import gleeunit/should

 pub fn example() {
   // Min value
   let a: Float = 0.
   // Max value
   let b: Float = 1.
   // The mode of the distribution
   let c: Float = 0.5
   // For illustrational purposes, evaluate the cdf at the 
   // point -100.0
   triangular.triangular_cdf(-100.0, a, b ,c) |> should.equal(Ok(0.0))
 }

triangular_mean

</> 
pub fn triangular_mean(a: Float, b: Float, c: Float) -> Result(
  Float,
  String,
)

Analytically compute the mean of a continuous triangular random variable
that takes values in the interval ‘[a, b]’ and has mode (a peak at value) ‘c’.

triangular_pdf

</> 
pub fn triangular_pdf(x: Float, a: Float, b: Float, c: Float) -> Result(
  Float,
  String,
)

Evaluate the probability density function (pdf) of a continuous triangular random variable that takes values in the interval ‘[a, b]’ and has mode (a peak at value) ‘c’.

Example: 
 import gleam_stats/distributions/triangular
 import gleeunit/should

 pub fn example() {
   // Min value
   let a: Float = 0.
   // Max value
   let b: Float = 1.
   // The mode of the distribution
   let c: Float = 0.5
   // For illustrational purposes, evaluate the pdf at the 
   // point -100.0
   triangular.triangular_pdf(-100.0, a, b ,c) |> should.equal(Ok(0.0))
 }

triangular_random

</> 
pub fn triangular_random(stream: Iterator(Int), a: Float, b: Float, c: Float, m: Int) -> Result(
  #(List(Float), Iterator(Int)),
  String,
)

Generate ‘m’ random numbers in the interval ‘[a, b]’ from a continuous triangular distribution with mode ‘c’.

The random numbers are generated using the inverse transform method.

Example: 
 import gleam/iterator.{Iterator}
 import gleam_stats/generator
 import gleam_stats/distributions/triangular

 pub fn example() {
   let seed: Int = 5
   let seq: Int = 1
   // Min value
   let a: Float = 0.
   // Max value
   let b: Float = 1.
   // The mode of the distribution
   let c: Float = 0.5
   assert Ok(out) =
     generators.seed_pcg32(seed)
     |> triangular.triangular_random(a, b, c, 5_000)
   let rands: List(Float) = pair.first(out)
   let stream: Iterator(Int) = pair.second(out)
 }

triangular_variance

</> 
pub fn triangular_variance(a: Float, b: Float, c: Float) -> Result(
  Float,
  String,
)

Analytically compute the variance of a continuous triangular random variable
that takes values in the interval ‘[a, b]’ and has mode (a peak at value) ‘c’.

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