# Crate rmpfit[−][src]

Very simple pure Rust implementation of the CMPFIT library: the Levenberg-Marquardt technique to solve the least-squares problem.

The code is mainly copied directly from CMPFIT almost without changing. The original CMPFIT tests (Linear (free parameters), Quad (free and fixed parameters), and Gaussian (free and fixed parameters) function) are reproduced and passed.

Just a few obvoius Rust-specific optimizations are done:

• Removing `goto` (fuf).
• Standart Rust Result as result.
• A few loops are zipped to help the compiler optimize the code (no performance tests are done anyway).
• Using trait `MPFitter` to call the user code.
• Using `bool` type if possible.

# Advantages

• Pure Rust.
• No external dependencies (assert_approx_eq just for testing).
• Internal Jacobian calculations.

# Disadvantages

• Sided, analitical or user provided derivates are not implemented.

# Usage Example

A user should implement trait `MPFitter` for its struct:

```use rmpfit::{MPFitter, MPResult, mpfit};

struct Linear {
x: Vec<f64>,
y: Vec<f64>,
ye: Vec<f64>,
}

impl MPFitter for Linear {
fn eval(&self, params: &[f64], deviates: &mut [f64]) -> MPResult<()> {
for (((d, x), y), ye) in deviates
.iter_mut()
.zip(self.x.iter())
.zip(self.y.iter())
.zip(self.ye.iter())
{
let f = params[0] + params[1] * *x;
*d = (*y - f) / *ye;
}
Ok(())
}

fn number_of_points(&self) -> usize {
self.x.len()
}
}

let l = Linear {
x: vec![
-1.7237128E+00,
1.8712276E+00,
-9.6608055E-01,
-2.8394297E-01,
1.3416969E+00,
1.3757038E+00,
-1.3703436E+00,
4.2581975E-02,
-1.4970151E-01,
8.2065094E-01,
],
y: vec![
1.9000429E-01,
6.5807428E+00,
1.4582725E+00,
2.7270851E+00,
5.5969253E+00,
5.6249280E+00,
0.787615,
3.2599759E+00,
2.9771762E+00,
4.5936475E+00,
],
ye: vec![0.07; 10],
};
// initializing input parameters
let mut init = [1., 1.];
let res = l.mpfit(&mut init, None, &Default::default()).unwrap();
assert_approx_eq!(init[0], 3.20996572); // actual 3.2
assert_approx_eq!(status.xerror[0], 0.02221018);
assert_approx_eq!(init[1], 1.77095420); // actual 1.78
assert_approx_eq!(status.xerror[1], 0.01893756);```

then `init` will contain the refined parameters of the fitting function. If user function fails to calculate residuals, it should return `MPError::Eval`.

## Structs

 MPConfig MPFIT configuration structure MPPar Parameter constraint structure MPStatus Status structure, for fit when it completes

## Enums

 MPError MPFIT error status MPSuccess Potential success status

## Traits

 MPFitter Trait to be implemented by user.

## Type Definitions

 MPResult MPFIT return result