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use opensrdk_linear_algebra::{pp::trf::PPTRF, SymmetricPackedMatrix};

use crate::{DistributionError, InverseWishartError, RandomVariable};

#[derive(Clone, Debug, PartialEq)]
pub struct InverseWishartParams {
    lpsi: PPTRF,
    nu: f64,
}

impl InverseWishartParams {
    pub fn new(lpsi: PPTRF, nu: f64) -> Result<Self, DistributionError> {
        let p = lpsi.0.dim();

        if nu <= p as f64 - 1.0 {
            return Err(DistributionError::InvalidParameters(
                InverseWishartError::NuMustBeGTEDimension.into(),
            ));
        }

        Ok(Self { lpsi, nu })
    }

    pub fn lpsi(&self) -> &PPTRF {
        &self.lpsi
    }

    pub fn nu(&self) -> f64 {
        self.nu
    }
}

impl RandomVariable for InverseWishartParams {
    type RestoreInfo = usize;

    fn transform_vec(&self) -> (Vec<f64>, Self::RestoreInfo) {
        let p = self.lpsi.0.dim();
        ([self.lpsi.0.elems(), &[self.nu]].concat(), p)
    }

    fn len(&self) -> usize {
        let t = self.lpsi.0.elems().len();
        t + 1usize
    }

    fn restore(v: &[f64], info: &Self::RestoreInfo) -> Result<Self, DistributionError> {
        if v.len() != info + 1 {
            return Err(DistributionError::InvalidRestoreVector);
        }
        let p = *info;
        let nu = v[v.len() - 1];
        let lpsi = PPTRF(SymmetricPackedMatrix::from(p, v[0..p].to_vec()).unwrap());
        Self::new(lpsi, nu)
    }
}