Fix document and pub setting

Author: termoshtt

src/krylov/householder.rs

@@ -1,3 +1,8 @@
+//! Householder reflection
+//!
+//! - [Householder transformation - Wikipedia](https://en.wikipedia.org/wiki/Householder_transformation)
+//!
+
 use super::*;
 use crate::{inner::*, norm::*};
 use num_traits::One;
@@ -16,7 +21,11 @@ where
     alpha
 }
 
-/// Take a reflection using `w`
+/// Take a reflection `P = I - 2ww^T`
+///
+/// Panic
+/// ------
+/// - if the size of `w` and `a` mismaches
 pub fn reflect<A, S1, S2>(w: &ArrayBase<S1, Ix1>, a: &mut ArrayBase<S2, Ix1>)
 where
     A: Scalar + Lapack,
@@ -155,7 +164,7 @@ impl<A: Scalar + Lapack> Orthogonalizer for Householder<A> {
     }
 }
 
-/// Online QR decomposition of vectors
+/// Online QR decomposition using Householder reflection
 pub fn householder<A, S>(
     iter: impl Iterator<Item = ArrayBase<S, Ix1>>,
     dim: usize,

src/krylov/mgs.rs

@@ -4,25 +4,6 @@ use super::*;
 use crate::{generate::*, inner::*, norm::Norm};
 
 /// Iterative orthogonalizer using modified Gram-Schmit procedure
-///
-/// ```rust
-/// # use ndarray::*;
-/// # use ndarray_linalg::{krylov::*, *};
-/// let mut mgs = MGS::new(3);
-/// let coef = mgs.append(array![0.0, 1.0, 0.0], 1e-9).unwrap();
-/// close_l2(&coef, &array![1.0], 1e-9);
-///
-/// let coef = mgs.append(array![1.0, 1.0, 0.0], 1e-9).unwrap();
-/// close_l2(&coef, &array![1.0, 1.0], 1e-9);
-///
-/// // Fail if the vector is linearly dependent
-/// assert!(mgs.append(array![1.0, 2.0, 0.0], 1e-9).is_err());
-///
-/// // You can get coefficients of dependent vector
-/// if let Err(coef) = mgs.append(array![1.0, 2.0, 0.0], 1e-9) {
-///     close_l2(&coef, &array![2.0, 1.0, 0.0], 1e-9);
-/// }
-/// ```
 #[derive(Debug, Clone)]
 pub struct MGS<A> {
     /// Dimension of base space
@@ -31,7 +12,7 @@ pub struct MGS<A> {
     q: Vec<Array1<A>>,
 }
 
-impl<A: Scalar> MGS<A> {
+impl<A: Scalar + Lapack> MGS<A> {
     /// Create an empty orthogonalizer
     pub fn new(dimension: usize) -> Self {
         Self {
@@ -45,9 +26,8 @@ impl<A: Scalar> MGS<A> {
     /// - Returned array is coefficients and residual norm
     /// - `a` will contain the residual vector
     ///
-    fn orthogonalize<S>(&self, a: &mut ArrayBase<S, Ix1>) -> Array1<A>
+    pub fn orthogonalize<S>(&self, a: &mut ArrayBase<S, Ix1>) -> Array1<A>
     where
-        A: Lapack,
         S: DataMut<Elem = A>,
     {
         assert_eq!(a.len(), self.dim());
@@ -106,7 +86,7 @@ impl<A: Scalar + Lapack> Orthogonalizer for MGS<A> {
     }
 }
 
-/// Online QR decomposition of vectors using modified Gram-Schmit algorithm
+/// Online QR decomposition using modified Gram-Schmit algorithm
 pub fn mgs<A, S>(
     iter: impl Iterator<Item = ArrayBase<S, Ix1>>,
     dim: usize,

src/krylov/mod.rs

@@ -3,10 +3,10 @@
 use crate::types::*;
 use ndarray::*;
 
-mod householder;
-mod mgs;
+pub mod householder;
+pub mod mgs;
 
-pub use householder::*;
+pub use householder::{householder, Householder};
 pub use mgs::{mgs, MGS};
 
 /// Q-matrix
@@ -24,6 +24,28 @@ pub type Q<A> = Array2<A>;
 pub type R<A> = Array2<A>;
 
 /// Trait for creating orthogonal basis from iterator of arrays
+///
+/// Example
+/// -------
+///
+/// ```rust
+/// # use ndarray::*;
+/// # use ndarray_linalg::{krylov::*, *};
+/// let mut mgs = MGS::new(3);
+/// let coef = mgs.append(array![0.0, 1.0, 0.0], 1e-9).unwrap();
+/// close_l2(&coef, &array![1.0], 1e-9);
+///
+/// let coef = mgs.append(array![1.0, 1.0, 0.0], 1e-9).unwrap();
+/// close_l2(&coef, &array![1.0, 1.0], 1e-9);
+///
+/// // Fail if the vector is linearly dependent
+/// assert!(mgs.append(array![1.0, 2.0, 0.0], 1e-9).is_err());
+///
+/// // You can get coefficients of dependent vector
+/// if let Err(coef) = mgs.append(array![1.0, 2.0, 0.0], 1e-9) {
+///     close_l2(&coef, &array![2.0, 1.0, 0.0], 1e-9);
+/// }
+/// ```
 pub trait Orthogonalizer {
     type Elem: Scalar;