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use ndarray::{Ix2, Array, LinalgScalar};
use std::fmt::Debug;
use num_traits::float::Float;
use lapack::c::Layout;
use matrix::Matrix;
use square::SquareMatrix;
use error::LinalgError;
use eigh::ImplEigh;
use qr::ImplQR;
use svd::ImplSVD;
use norm::ImplNorm;
use solve::ImplSolve;
use cholesky::ImplCholesky;
pub trait HermiteMatrix: SquareMatrix + Matrix {
fn eigh(self) -> Result<(Self::Vector, Self), LinalgError>;
fn ssqrt(self) -> Result<Self, LinalgError>;
fn cholesky(self) -> Result<Self, LinalgError>;
}
impl<A> HermiteMatrix for Array<A, Ix2>
where A: ImplQR + ImplSVD + ImplNorm + ImplSolve + ImplEigh + ImplCholesky + LinalgScalar + Float + Debug
{
fn eigh(self) -> Result<(Self::Vector, Self), LinalgError> {
self.check_square()?;
let layout = self.layout()?;
let (rows, cols) = self.size();
let (w, a) = ImplEigh::eigh(layout, rows, self.into_raw_vec())?;
let ea = Array::from_vec(w);
let va = match layout {
Layout::ColumnMajor => Array::from_vec(a).into_shape((rows, cols)).unwrap().reversed_axes(),
Layout::RowMajor => Array::from_vec(a).into_shape((rows, cols)).unwrap(),
};
Ok((ea, va))
}
fn ssqrt(self) -> Result<Self, LinalgError> {
let (n, _) = self.size();
let (e, v) = self.eigh()?;
let mut res = Array::zeros((n, n));
for i in 0..n {
for j in 0..n {
res[(i, j)] = e[i].sqrt() * v[(j, i)];
}
}
Ok(v.dot(&res))
}
fn cholesky(self) -> Result<Self, LinalgError> {
self.check_square()?;
let (n, _) = self.size();
let layout = self.layout()?;
let a = ImplCholesky::cholesky(layout, n, self.into_raw_vec())?;
let mut c = match layout {
Layout::RowMajor => Array::from_vec(a).into_shape((n, n)).unwrap(),
Layout::ColumnMajor => Array::from_vec(a).into_shape((n, n)).unwrap().reversed_axes(),
};
for ((i, j), val) in c.indexed_iter_mut() {
if i > j {
*val = A::zero();
}
}
Ok(c)
}
}