aegir

Overview

Strongly-typed, compile-time autodifferentiation in Rust.

aegir is an experimental autodifferentiation framework designed to leverage the powerful type-system in Rust and avoid runtime as much as humanly possible. The approach taken resembles that of expression templates, as commonly used in linear-algebra libraries written in C++.

Key Features

• Built-in arithmetic, linear-algebraic, trigonometric and special operators.
• Infinitely differentiable: Jacobian, Hessian, etc...
• Custom DSL for operator expansion.
• Decoupled/generic tensor type.

Installation

toml [dependencies] aegir = "1.0"

Example

```rust

[macro_use]

extern crate aegir; extern crate rand;

use aegir::{Differentiable, Function, Identifier, Node, ids::{X, Y, W}};

db!(Database { x: X, y: Y, w: W });

fn main() { let mut weights = [0.0, 0.0];

let x = X.into_var();
let y = Y.into_var();
let w = W.into_var();

let model = x.dot(w);

// Using standard method calls...
let sse = model.sub(y).squared();
let adj = sse.adjoint(W);

// ...or using aegir! macro
let sse = aegir!((model - y) ^ 2);
let adj = sse.adjoint(W);

for _ in 0..100000 {
    let [x1, x2] = [rand::random::<f64>(), rand::random::<f64>()];

    let g = adj.evaluate(Database {
        // Independent variables:
        x: [x1, x2],

        // Dependent variable:
        y: x1 * 2.0 - x2 * 4.0,

        // Model weights:
        w: &weights,
    }).unwrap();

    weights[0] -= 0.01 * g[0][0];
    weights[1] -= 0.01 * g[0][1];
}

println!("{:?}", weights.to_vec());

} ```