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use ndarray::*;
use rand::*;
use std::ops::*;
use super::convert::*;
use super::error::*;
use super::types::*;
pub fn conjugate<A, Si, So>(a: &ArrayBase<Si, Ix2>) -> ArrayBase<So, Ix2>
where
A: Conjugate,
Si: Data<Elem = A>,
So: DataOwned<Elem = A> + DataMut,
{
let mut a = replicate(&a.t());
for val in a.iter_mut() {
*val = Conjugate::conj(*val);
}
a
}
pub fn random<A, S, Sh, D>(sh: Sh) -> ArrayBase<S, D>
where
A: RandNormal,
S: DataOwned<Elem = A>,
D: Dimension,
Sh: ShapeBuilder<Dim = D>,
{
let mut rng = thread_rng();
ArrayBase::from_shape_fn(sh, |_| A::randn(&mut rng))
}
pub fn random_hermite<A, S>(n: usize) -> ArrayBase<S, Ix2>
where
A: RandNormal + Conjugate + Add<Output = A>,
S: DataOwned<Elem = A> + DataMut,
{
let mut a = random((n, n));
for i in 0..n {
a[(i, i)] = a[(i, i)] + Conjugate::conj(a[(i, i)]);
for j in (i + 1)..n {
a[(i, j)] = Conjugate::conj(a[(j, i)])
}
}
a
}
pub fn random_hpd<A, S>(n: usize) -> ArrayBase<S, Ix2>
where
A: RandNormal + Conjugate + LinalgScalar,
S: DataOwned<Elem = A> + DataMut,
{
let a: Array2<A> = random((n, n));
let ah: Array2<A> = conjugate(&a);
replicate(&ah.dot(&a))
}
pub fn from_diag<A>(d: &[A]) -> Array2<A>
where
A: LinalgScalar,
{
let n = d.len();
let mut e = Array::zeros((n, n));
for i in 0..n {
e[(i, i)] = d[i];
}
e
}
pub fn hstack<A, S>(xs: &[ArrayBase<S, Ix1>]) -> Result<Array<A, Ix2>>
where
A: LinalgScalar,
S: Data<Elem = A>,
{
let views: Vec<_> = xs.iter()
.map(|x| {
let n = x.len();
x.view().into_shape((n, 1)).unwrap()
})
.collect();
stack(Axis(1), &views).map_err(|e| e.into())
}
pub fn vstack<A, S>(xs: &[ArrayBase<S, Ix1>]) -> Result<Array<A, Ix2>>
where
A: LinalgScalar,
S: Data<Elem = A>,
{
let views: Vec<_> = xs.iter()
.map(|x| {
let n = x.len();
x.view().into_shape((1, n)).unwrap()
})
.collect();
stack(Axis(0), &views).map_err(|e| e.into())
}