use russell_lab::{vec_approx_eq, Vector};
use russell_sparse::prelude::*;
use russell_sparse::StrError;

fn main() -> Result<(), StrError> {
    // constants
    let ndim = 5; // number of rows = number of columns
    let nnz = 13; // number of non-zero values, including duplicates

    // allocate solver
    let mut umfpack = SolverUMFPACK::new()?;

    // allocate the coefficient matrix
    //  2  3  .  .  .
    //  3  .  4  .  6
    //  . -1 -3  2  .
    //  .  .  1  .  .
    //  .  4  2  .  1
    let mut coo = SparseMatrix::new_coo(ndim, ndim, nnz, Sym::No)?;
    coo.put(0, 0, 1.0)?; // << (0, 0, a00/2) duplicate
    coo.put(0, 0, 1.0)?; // << (0, 0, a00/2) duplicate
    coo.put(1, 0, 3.0)?;
    coo.put(0, 1, 3.0)?;
    coo.put(2, 1, -1.0)?;
    coo.put(4, 1, 4.0)?;
    coo.put(1, 2, 4.0)?;
    coo.put(2, 2, -3.0)?;
    coo.put(3, 2, 1.0)?;
    coo.put(4, 2, 2.0)?;
    coo.put(2, 3, 2.0)?;
    coo.put(1, 4, 6.0)?;
    coo.put(4, 4, 1.0)?;

    // parameters
    let mut params = LinSolParams::new();
    params.verbose = false;
    params.compute_determinant = true;

    // call factorize
    umfpack.factorize(&mut coo, Some(params))?;

    // allocate x and rhs
    let mut x = Vector::new(ndim);
    let rhs = Vector::from(&[8.0, 45.0, -3.0, 3.0, 19.0]);

    // calculate the solution
    umfpack.solve(&mut x, &coo, &rhs, false)?;
    println!("x =\n{}", x);

    // check the results
    let correct = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    vec_approx_eq(&x, &correct, 1e-14);

    // analysis
    let mut stats = StatsLinSol::new();
    umfpack.update_stats(&mut stats);
    let (mx, ex) = (stats.determinant.mantissa_real, stats.determinant.exponent);
    println!("det(a) = {:?}", mx * f64::powf(10.0, ex));
    println!("rcond  = {:?}", stats.output.umfpack_rcond_estimate);
    Ok(())
}