Turn data into functions! A simple and functional machine learning library, (re)implemented in Gleam for the Erlang ecosystem.

• For Erlang projects, add the dependency into rebar.config:

{deps, [
    {emel, "1.0.0"}
]}.

• For Elixir projects, add the dependency into mix.exs:

defp deps do
  [
    {:emel, "~> 1.0.0"}
  ]
end

• For Gleam projects, run gleam add emel.

Data = [
  {[7.0, 7.0], "bad"},
  {[7.0, 4.0], "bad"},
  {[3.0, 4.0], "good"},
  {[1.0, 4.0], "good"}
],
F = emel@ml@k_nearest_neighbors:classifier(Data, 3),
F([3.0, 7.0]). % "good"

data = [
  {[1.794638, 15.15426], 0.510998918},
  {[3.220726, 229.6516], 105.6583692},
  {[5.780040, 3480.201], 1776.99}
]
f = :emel@ml@linear_regression.predictor(data)
f.([3.0, 230.0]) # 106.74114058686602

import emel/ml/neural_network as nn
let data = [
  #([0.8, 0.0, 0.0], "x"),
  #([0.0, 0.9, 0.0], "y"),
  #([0.0, 0.0, 0.8], "z"),
]
let f = nn.classifier(
    data,
    [4],           // hidden layers
    0.01,          // learning rate
    0.1,           // error threshold
    1000           // maximum iterations
)
f([0.0, 0.8, 0.0]) // "y"

The documentation describes all the public functions. Gleam developers can pay attention to the type annotations. Erlang and Elixir devs may take a look at the examples which are written in Erlang.

• Linear Regression
• K Nearest Neighbors
• Decision Tree
• Naive Bayes
• K Means
• Perceptron
• Logistic Regression
• Neural Network

• Algebra
• Geometry
• Statistics

For the documentation of the previous version (0.3.0), click here.