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use ndarray::{Ix2, Array, RcArray, NdFloat, ArrayBase, DataMut};
use matrix::{Matrix, MFloat};
use square::SquareMatrix;
use error::LinalgError;
use solve::ImplSolve;
pub trait TriangularMatrix: Matrix + SquareMatrix {
fn solve_upper(&self, Self::Vector) -> Result<Self::Vector, LinalgError>;
fn solve_lower(&self, Self::Vector) -> Result<Self::Vector, LinalgError>;
}
impl<A: MFloat> TriangularMatrix for Array<A, Ix2> {
fn solve_upper(&self, b: Self::Vector) -> Result<Self::Vector, LinalgError> {
self.check_square()?;
let (n, _) = self.size();
let layout = self.layout()?;
let a = self.as_slice_memory_order().unwrap();
let x = ImplSolve::solve_triangle(layout, 'U' as u8, n, a, b.into_raw_vec())?;
Ok(Array::from_vec(x))
}
fn solve_lower(&self, b: Self::Vector) -> Result<Self::Vector, LinalgError> {
self.check_square()?;
let (n, _) = self.size();
let layout = self.layout()?;
let a = self.as_slice_memory_order().unwrap();
let x = ImplSolve::solve_triangle(layout, 'L' as u8, n, a, b.into_raw_vec())?;
Ok(Array::from_vec(x))
}
}
impl<A: MFloat> TriangularMatrix for RcArray<A, Ix2> {
fn solve_upper(&self, b: Self::Vector) -> Result<Self::Vector, LinalgError> {
let x = self.to_owned().solve_upper(b.to_owned())?;
Ok(x.into_shared())
}
fn solve_lower(&self, b: Self::Vector) -> Result<Self::Vector, LinalgError> {
let x = self.to_owned().solve_lower(b.to_owned())?;
Ok(x.into_shared())
}
}
pub fn drop_upper<A: NdFloat, S>(mut a: ArrayBase<S, Ix2>) -> ArrayBase<S, Ix2>
where S: DataMut<Elem = A>
{
for ((i, j), val) in a.indexed_iter_mut() {
if i < j {
*val = A::zero();
}
}
a
}
pub fn drop_lower<A: NdFloat, S>(mut a: ArrayBase<S, Ix2>) -> ArrayBase<S, Ix2>
where S: DataMut<Elem = A>
{
for ((i, j), val) in a.indexed_iter_mut() {
if i > j {
*val = A::zero();
}
}
a
}