Documentation For Screenshots

Mix.install([
  {:scholar, "~> 0.1"},
  {:explorer, "~> 0.5.6"},
  {:exla, "~> 0.5.2"},
  {:req, "~> 0.3.6"},
  {:kino_vega_lite, "~> 0.1.8"},
  {:kino, "~> 0.9.0"},
  {:kino_explorer, "~> 0.1.6"}
])

Pre-requisites

Setting up aliases to generate some visualizations

alias VegaLite, as: Vl
require Explorer.DataFrame, as: DF
require Explorer.Series, as: S
Nx.global_default_backend(EXLA.Backend)

defmodule LinearData do
  import Nx.Defn

  defn data do
    key = Nx.Random.key(42)
    size = 100
    {x, new_key} = Nx.Random.uniform(key, 0, 2, shape: {size, 1}, type: :f64)
    {noise, _} = Nx.Random.uniform(new_key, -0.5, 0.5, shape: {size, 1}, type: :f64)
    y = 3 * x + 4 + noise
    {x, y}
  end
end

Linear Regression

size = 100
{x, y} = LinearData.data()

df = DF.new(x: x, y: y)

Now let’s plot the generated points.

model = Scholar.Linear.LinearRegression.fit(x, y)

[intercept] = Nx.to_flat_list(model.intercept)
[coefficients] = Nx.to_flat_list(model.coefficients)
x_1 = 0
x_2 = 2

line = %{
  x: [x_1, x_2],
  y: [x_1 * coefficients + intercept, x_2 * coefficients + intercept]
}

Vl.new(
  title: [
    text: "Scatterplot of Generated Data",
    offset: 20
  ],
  width: 630,
  height: 630
)
|> Vl.layers([
  Vl.new()
  |> Vl.data_from_values(df)
  |> Vl.mark(:circle)
  |> Vl.encode_field(:x, "x",
    type: :quantitative,
    scale: [domain: [-0.05, 2.05]],
    axis: [grid: false]
  )
  |> Vl.encode_field(:y, "y",
    type: :quantitative,
    scale: [domain: [2.5, 12]],
    axis: [grid: false]
  ),
  Vl.new()
  |> Vl.data_from_values(line)
  |> Vl.mark(:line, color: :green)
  |> Vl.encode_field(:x, "x", type: :quantitative, scale: [domain: [-0.05, 2.05]])
  |> Vl.encode_field(:y, "y", type: :quantitative, scale: [domain: [2.5, 12]])
])