faer 0.24.0

linear algebra library
Documentation 
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#![allow(missing_docs)]
mod meanvar;
pub use meanvar::{NanHandling, col_mean, col_varm, row_mean, row_varm};
pub mod prelude {
	pub use super::ComplexDistribution;
	#[cfg(feature = "rand")]
	pub use super::{
		CwiseColDistribution, CwiseMatDistribution, CwiseRowDistribution,
		DistributionExt, UnitaryMat,
	};
	#[cfg(feature = "rand")]
	pub use rand::prelude::*;
	#[cfg(feature = "rand")]
	pub use rand_distr::{Normal, StandardNormal, StandardUniform};
}
/// A generic random value distribution for complex numbers.
#[derive(Clone, Copy, Debug)]
pub struct ComplexDistribution<Re, Im = Re> {
	re: Re,
	im: Im,
}
impl<Re, Im> ComplexDistribution<Re, Im> {
	/// Creates a complex distribution from independent
	/// distributions of the real and imaginary parts.
	pub fn new(re: Re, im: Im) -> Self {
		ComplexDistribution { re, im }
	}
}
#[cfg(feature = "rand")]
pub use self::rand::*;
#[cfg(feature = "rand")]
mod rand {
	use super::ComplexDistribution;
	use crate::internal_prelude::*;
	use rand::Rng;
	use rand::distr::Distribution;
	pub trait DistributionExt {
		fn rand<T>(&self, rng: &mut (impl ?Sized + rand::Rng)) -> T
		where
			Self: Distribution<T>,
		{
			self.sample(rng)
		}
	}
	impl<T: ?Sized> DistributionExt for T {}
	#[derive(Copy, Clone, Debug)]
	pub struct CwiseMatDistribution<Rows: Shape, Cols: Shape, D> {
		pub nrows: Rows,
		pub ncols: Cols,
		pub dist: D,
	}
	#[derive(Copy, Clone, Debug)]
	pub struct CwiseColDistribution<Rows: Shape, D> {
		pub nrows: Rows,
		pub dist: D,
	}
	#[derive(Copy, Clone, Debug)]
	pub struct CwiseRowDistribution<Cols: Shape, D> {
		pub ncols: Cols,
		pub dist: D,
	}
	#[derive(Copy, Clone, Debug)]
	pub struct UnitaryMat<Dim: Shape, D> {
		pub dim: Dim,
		pub standard_normal: D,
	}
	impl<T, Rows: Shape, Cols: Shape, D: Distribution<T>>
		Distribution<Mat<T, Rows, Cols>> for CwiseMatDistribution<Rows, Cols, D>
	{
		#[inline]
		fn sample<R: rand::Rng + ?Sized>(
			&self,
			rng: &mut R,
		) -> Mat<T, Rows, Cols> {
			Mat::from_fn(self.nrows, self.ncols, |_, _| self.dist.sample(rng))
		}
	}
	impl<T, Rows: Shape, D: Distribution<T>> Distribution<Col<T, Rows>>
		for CwiseColDistribution<Rows, D>
	{
		#[inline]
		fn sample<R: rand::Rng + ?Sized>(&self, rng: &mut R) -> Col<T, Rows> {
			Col::from_fn(self.nrows, |_| self.dist.sample(rng))
		}
	}
	impl<T, Cols: Shape, D: Distribution<T>> Distribution<Row<T, Cols>>
		for CwiseRowDistribution<Cols, D>
	{
		#[inline]
		fn sample<R: rand::Rng + ?Sized>(&self, rng: &mut R) -> Row<T, Cols> {
			Row::from_fn(self.ncols, |_| self.dist.sample(rng))
		}
	}
	impl<T: ComplexField, D: Distribution<T>> Distribution<Mat<T>>
		for UnitaryMat<usize, D>
	{
		fn sample<R: rand::prelude::Rng + ?Sized>(
			&self,
			rng: &mut R,
		) -> Mat<T> {
			let qr = CwiseMatDistribution {
				nrows: self.dim,
				ncols: self.dim,
				dist: &self.standard_normal,
			}
			.sample(rng)
			.qr();
			let r_diag = qr.R().diagonal().column_vector();
			let mut q = qr.compute_Q();
			for j in 0..self.dim {
				let ref r = r_diag[j];
				let ref r = if *r == zero() {
					one()
				} else {
					r.mul_real(r.abs().recip())
				};
				z!(q.as_mut().col_mut(j)).for_each(|uz!(q)| {
					*q *= r;
				});
			}
			q
		}
	}
	impl<T, Re, Im> Distribution<Complex<T>> for ComplexDistribution<Re, Im>
	where
		Re: Distribution<T>,
		Im: Distribution<T>,
	{
		fn sample<R: Rng + ?Sized>(&self, rng: &mut R) -> Complex<T> {
			Complex::new(self.re.sample(rng), self.im.sample(rng))
		}
	}
}