num-dual
Generalized, recursive, scalar and vector (hyper) dual numbers for the automatic and exact calculation of (partial) derivatives. Including bindings for python.
Installation and Usage
Python
The python package can be installed directly from PyPI:
pip install num_dual
Rust
Add this to your Cargo.toml:
[dependencies]
num-dual = "0.4"
Example
Python
Compute the first and second derivative of a scalar-valued function.
from num_dual import derive2
import numpy as np
def f(x):
return np.exp(x) / np.sqrt(np.sin(x)**3 + np.cos(x)**3)
x = derive2(1.5)
result = f(x)
print('f(x) = {}'.format(result.value))
print('df/dx = {}'.format(result.first_derivative))
print('d2f/dx2 = {}'.format(result.second_derivative))
Rust
This example defines a generic function that can be called using any (hyper) dual number and automatically calculates derivatives.
use num_dual::*;
fn f<D: DualNum<f64>>(x: D, y: D) -> D {
x.powi(3) * y.powi(2)
}
fn main() {
let (x, y) = (5.0, 4.0);
// Calculate a simple derivative
let x_dual = Dual64::from(x).derive();
let y_dual = Dual64::from(y);
println!("{}", f(x_dual, y_dual)); // 2000 + [1200]ε
// Calculate a gradient
let xy_dual_vec = StaticVec::new_vec([x,y]).map(DualVec64::<2>::from).derive();
println!("{}", f(xy_dual_vec[0], xy_dual_vec[1]).eps); // [1200, 1000]
// Calculate a Hessian
let xy_dual2 = StaticVec::new_vec([x,y]).map(Dual2Vec64::<2>::from).derive();
println!("{}", f(xy_dual2[0], xy_dual2[1]).v2); // [[480, 600], [600, 250]]
// for x=cos(t) and y=sin(t) calculate the third derivative w.r.t. t
let t = Dual3_64::from(1.0).derive();
println!("{}", f(t.cos(), t.sin()).v3); // 7.358639755305733
}
Documentation
- You can find the documentation of the rust crate here.
- The documentation of the python package can be found here.
Python
For the following commands to work you have to have the package installed (see: installing from source).
cd docs
make html
Open _build/html/index.html in your browser.
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