# odesign
`odesign` is an optimal design of experiments library written in pure rust.
There are at least these following use cases:
- Fast calculation of optimal designs of arbitrary linear models with custom
design bounds and optimalities.
- Research in area of optimal designs; e.g. I am working on a new optimal design
feature selection algorithm, a mixture of SFFS, D-, C- and
Measurements-Costs-Optimality, allowing to perform model feature selection and
measurements alternating.
## Get started
Please have a look at the book on [odesign.rs](https://odesign.rs) for a high
level introduction and theoretical background and at the docs on
[docs.rs/odesign](https://docs.rs/odesign) for the implementation details.
## Basic Example
In short, this is a
[basic example](https://git.sr.ht/~maunke/odesign/tree/main/item/odesign-examples/examples/basic/main.rs)
of an optimal design of the simple polynomial 1 + x within design bounds [-1,
+1] and 101 equally distributed grid points as an init design.
```rust
use nalgebra::{SVector, Vector1};
use num_dual::DualNum;
use odesign::{
DOptimality, Feature, FeatureFunction, FeatureSet, LinearModel, OptimalDesign, Result,
};
use std::sync::Arc;
#[derive(Feature)]
#[dimension = 1]
struct Monomial {
i: i32,
}
impl FeatureFunction<1> for Monomial {
fn f<D: DualNum<f64>>(&self, x: &SVector<D, 1>) -> D {
x[0].powi(self.i)
}
}
// f(x): 1 + x
fn main() -> Result<()> {
let mut fs = FeatureSet::new();
let c: Arc<_> = Monomial { i: 0 }.into();
fs.push(c);
let c: Arc<_> = Monomial { i: 1 }.into();
fs.push(c);
let lm = LinearModel::new(fs.features);
let optimality: Arc<_> = DOptimality::new(lm.into()).into();
let lower = Vector1::new(-1.0);
let upper = Vector1::new(1.0);
let q = Vector1::new(101);
let mut od = OptimalDesign::new()
.with_optimality(optimality)
.with_bound_args(lower, upper)?
.with_init_design_grid_args(lower, upper, q)?;
od.solve();
println!("{od}");
Ok(())
}
// Output
// ---------- Design ----------
// Weight Support Vector
// 0.5000 [ -1.0000 ]
// 0.5000 [ +1.0000 ]
// -------- Statistics --------
// Optimality measure: 1.000000
// No. support vectors: 2
// Iterations: 1
// ----------------------------
```