README

Introduction

High performance platform for generating random values, with probabilities or weights.

Upgrading from before v1.0.0?

Docs

See Hex docs. Documentation will not be kept in the README.

Visual Examples

Uniform random

for _ <- 1..5000 do
  Enum.random(0..3)
end

probabilities = [
  0.3, 0.05, 0.6, 0.05
]
WeightedRandom.preprocess_p(probabilities)
|> WeightedRandom.take(1000)

# Weights offer an alternative paradigm to probabilities.
# By default, every number has a weight of 1.0
# Let's add a little weight to the outcome of 2 for a total of 1.8

outcomes = 0..3
weights = [
  %{target: 2, amount: 0.8}
]

WeightedRandom.preprocess(outcomes, weights)
|> WeightedRandom.take(5000)

WeightedRandom integrates well with the Curves library, which I also wrote.

####
# By using different predefined curves, we clearly get very distinct shapes
# (Of course, some curves work better than others when doing this)

curve = :ease_in_out
outcomes = 0..100
weights = [%{curve: curve, radius: 25, target: 50, amount: 100}]

# see that `radius` field? 
# It basically means we are now targeting all numbers from 25-75,
# or rather (target - radius) to (target + radius)
# But instead of applying the weight amount of 100 evenly, it spreads it out as an ease_in_out bezier curve.

WeightedRandom.preprocess(outcomes, weights)
|> WeightedRandom.take(1_000_000)

#### Define your own bezier curve ####
curve = [
  {0, 0},
  {0.33, -4},
  {0.67, 4},
  {1, 1}
]
outcomes = 0..100
weights = [%{target: 50, amount: 200, radius: 25, curve: curve}]
####


WeightedRandom.preprocess(outcomes, weights)
|> WeightedRandom.take(1_000_000)

Hire me

No AI was used in the creation of this project. Just good old fashioned software engineering.

Full stack developer seeking new challenges. You can reach me at hireme@aaronjprice.com