RustedSciThe 0.4.6

Rust framework for symbolic and numerical computing:BVP ( Newton-Raphson frozen/damped/with collocations ), IVP( BDF, Radau, Backward Euler, LSODE, LSODA, RK45, DoPri), nonlinear equations ( Levenberg, Gavin) and more
Documentation 
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//! Universal fitting guide.
//!
//! This guide demonstrates the high-level depot wrapper over both fitting
//! backends:
//! - LM for classic symbolic least squares,
//! - VarPro when the model is separable and you want to choose the backend
//!   through `Method`.
//!
//! run cargo run --example universal_fitting_guide

use RustedSciThe::numerical::optimization::{Method, UniversalFitting, UniversalFittingResult};
use RustedSciThe::symbolic::symbolic_engine::Expr;

fn main() {
    println!("=== Universal Fitting Guide ===\n");
    universal_lm();
    universal_varpro();
}

fn universal_lm() {
    println!("Example 1: Universal wrapper with classic LM");
    println!("Model: y = a * x + b");
    println!("Here the wrapper uses the LM backend.\n");

    let x_data = (0..8).map(|x| x as f64).collect::<Vec<_>>();
    let y_data = x_data.iter().map(|&x| 4.0 * x - 1.0).collect::<Vec<_>>();

    let result = UniversalFitting::new()
        .with_method(Method::LM)
        .with_data(x_data, y_data)
        .with_equation_str("a * x + b".to_string())
        .with_arg("x".to_string())
        .with_unknowns(vec!["a".to_string(), "b".to_string()])
        .with_initial_guess(vec![1.0, 0.0])
        .with_tolerance(1e-10)
        .build()
        .expect("universal LM fit should build");

    let lm = match result {
        UniversalFittingResult::LM(fit) => fit,
        UniversalFittingResult::VARPRO(_) => panic!("expected LM result"),
    };

    let map = lm
        .map_of_solutions
        .expect("LM path should produce a solution map");
    println!("Recovered parameters:");
    println!("  a = {:.6}", map["a"]);
    println!("  b = {:.6}", map["b"]);
    println!();
}

fn universal_varpro() {
    println!("Example 2: Universal wrapper with VarPro");
    println!("Model: y = a * exp(-x / tau) + b");
    println!("Here the wrapper switches backend with Method::VARPRO.\n");

    let x_data = (0..30).map(|i| i as f64 * 0.2).collect::<Vec<_>>();
    let y_data = x_data
        .iter()
        .map(|&x| 2.5 * (-x / 1.7).exp() + 0.8)
        .collect::<Vec<_>>();

    let result = UniversalFitting::new()
        .with_method(Method::VARPRO)
        .with_data(x_data, y_data)
        .with_arg("x".to_string())
        .with_parameters(vec!["tau".to_string()])
        .with_initial_guess(vec![1.2])
        .with_basis_str("tau", "exp(-x/tau)")
        .with_equations(vec![Expr::parse_expression("1")])
        .build()
        .expect("universal VarPro fit should build");

    let fit = match result {
        UniversalFittingResult::VARPRO(fit) => fit,
        UniversalFittingResult::LM(_) => panic!("expected VarPro result"),
    };

    let nonlinear = fit.nonlinear_parameters();
    let linear = fit
        .linear_coefficients()
        .expect("VarPro path should return linear coefficients");

    println!("Recovered parameters:");
    println!("  tau = {:.6}", nonlinear[0]);
    println!("  a   = {:.6}", linear[0]);
    println!("  b   = {:.6}", linear[1]);
    println!();
}