This project is a proof-of-concept auto differentiation library written in Rust. The idea behind auto differentiation is to define a function and have the library compute the derivative of this function for us implicitly.
Compute the gradient of simple functions found in the functions module.
It is designed to be easy to extend rugrads to include user defined functions.
This is an early draft and the API needs a lot more work. Below is an example of how you would specify a function in rugrads to be differentiated.
extern crate rugrads;
use rugrads::{Context, Gradient};
use rugrads::functions::*;
// First we define our context and function variables
let mut context = Context::new();
let x = context.create_variable(0.5);
let y = context.create_variable(0.3);
// Below we build: y * sin(x) + cos(y)
let f = y * sin(x) + cos(y)
let mut grad = Gradient::of(f, context);
// Take gradient with respect to x
grad.grad(x);
// Or with respect to y
grad.grad(y);
// We can also change the initial seed values and recompute:
grad.context().set_variable_value(x, 0.8);
grad.grad(x);The API is still actively evolving to be more flexible. I would love to receive any suggestions!