argamak 🐎

A Gleam library for tensor maths.

“I admire the elegance of your method of computation; it must be nice to ride through these fields upon the horse of true mathematics while the like of us have to make our way laboriously on foot.”

—Albert Einstein, to Tullio Levi-Civita, circa 1915–1917

Installation

As a dependency of your Gleam project

• Add argamak to gleam.toml from the command line

$ gleam add argamak

As a dependency of your Mix project

• Add argamak to mix.exs

defp deps do
  [
    {:argamak, "~> 0.3"},
  ]
end

As a dependency of your Rebar3 project

• Add argamak to rebar.config

{deps, [
  {argamak, "0.3.0"}
]}.

JavaScript

The @tensorflow/tfjs package is a runtime requirement for argamak; its import path in the argamak_ffi.mjs module might need adjustment, depending on your use case.

Usage

// derby.gleam
import argamak/axis.{Axis, Infer}
import argamak/space
import argamak/tensor.{InvalidData, TensorError}
import gleam/function
import gleam/io
import gleam/list
import gleam/result
import gleam/string

pub fn announce_winner(
  from horses: List(String),
  with times: List(Float),
) -> Result(Nil, TensorError) {
  assert Ok(d2) = space.d2(Infer("Horse"), Axis("Trial", 2))
  try x = tensor.from_floats(of: times, into: d2)

  let announce = function.compose(string.inspect, io.println)

  announce("Trial times per horse")
  tensor.print(x)

  announce("Mean time per horse")
  let mean_times =
    x
    |> tensor.mean(with: fn(a) { axis.name(a) == "Trial" })
    |> tensor.debug

  let all_axes = fn(_) { True }

  announce("Fastest mean time")
  let time =
    mean_times
    |> tensor.min_over(with: all_axes)
    |> tensor.debug
    |> tensor.to_string(return: tensor.Data, wrap_at: 0)

  announce("Fastest horse")
  try horse =
    mean_times
    |> tensor.arg_min(with: all_axes)
    |> tensor.debug
    |> tensor.to_int
  try horse =
    horses
    |> list.at(get: horse)
    |> result.replace_error(InvalidData)

  horse <> " wins the day with a mean time of " <> time <> " minutes!"
  |> announce
  |> Ok
}

Example

> derby.announce_winner(
>   from: ["Pony Express", "Hay Girl", "Low Rider"],
>   with: [1.2, 1.3, 1.3, 1.0, 1.5, 0.9],
> )
"Trial times per horse"
Tensor(
  Format(Float32),
  Space(Axis("Horse", 3), Axis("Trial", 2)),
  [[1.2, 1.3],
   [1.3, 1.0],
   [1.5, 0.9]],
)
"Mean time per horse"
Tensor(
  Format(Float32),
  Space(Axis("Horse", 3)),
  [1.25, 1.15,  1.2],
)
"Fastest mean time"
Tensor(
  Format(Float32),
  Space(),
  1.15,
)
"Fastest horse"
Tensor(
  Format(Float32),
  Space(),
  1.0,
)
"Hay Girl wins the day with a mean time of 1.15 minutes!"
Ok(Nil)