chore: more doc updates

src/algorithm/neighbour/fastpair.rs

@@ -74,10 +74,8 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
         Ok(init)
     }
 
-    ///
     /// Initialise `FastPair` by passing a `Array2`.
     /// Build a FastPairs data-structure from a set of (new) points.
-    ///
     fn init(&mut self) {
         // basic measures
         let len = self.samples.shape().0;

src/linalg/basic/arrays.rs

@@ -1294,7 +1294,7 @@ pub trait Array2<T: Debug + Display + Copy + Sized>: MutArrayView2<T> + Sized +
             }
         }
     }
-    ///
+    /// merge 1d arrays
     fn merge_1d<'a>(&'a self, arrays: &'a [&'a dyn ArrayView1<T>], axis: u8, append: bool) -> Self {
         assert!(
             axis == 1 || axis == 0,
@@ -1362,7 +1362,7 @@ pub trait Array2<T: Debug + Display + Copy + Sized>: MutArrayView2<T> + Sized +
             }
         }
     }
-    ///
+    /// Stack arrays in sequence vertically
     fn v_stack(&self, other: &dyn ArrayView2<T>) -> Self {
         let (nrows, ncols) = self.shape();
         let (other_nrows, other_ncols) = other.shape();
@@ -1378,7 +1378,7 @@ pub trait Array2<T: Debug + Display + Copy + Sized>: MutArrayView2<T> + Sized +
             0,
         )
     }
-    ///
+    /// Stack arrays in sequence horizontally
     fn h_stack(&self, other: &dyn ArrayView2<T>) -> Self {
         let (nrows, ncols) = self.shape();
         let (other_nrows, other_ncols) = other.shape();
@@ -1407,7 +1407,7 @@ pub trait Array2<T: Debug + Display + Copy + Sized>: MutArrayView2<T> + Sized +
     fn col_iter<'a>(&'a self) -> Box<dyn Iterator<Item = Box<dyn ArrayView1<T> + 'a>> + 'a> {
         Box::new((0..self.shape().1).map(move |r| self.get_col(r)))
     }
-    ///
+    /// take elements from 2d array
     fn take(&self, index: &[usize], axis: u8) -> Self {
         let (nrows, ncols) = self.shape();
 

src/linear/bg_solver.rs

@@ -133,7 +133,7 @@ pub trait BiconjugateGradientSolver<'a, T: FloatNumber, X: Array2<T>> {
         y.copy_from(&x.xa(true, a));
     }
 
-    ///
+    /// Extract the diagonal from a matrix
     fn diag(a: &X) -> Vec<T> {
         let (nrows, ncols) = a.shape();
         let n = nrows.min(ncols);

src/linear/lasso_optimizer.rs

@@ -208,7 +208,6 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
         Ok(w)
     }
 
-    ///
     fn sumlogneg(f: &X) -> T {
         let (n, _) = f.shape();
         let mut sum = T::zero();
@@ -220,11 +219,9 @@ impl<T: FloatNumber, X: Array2<T>> InteriorPointOptimizer<T, X> {
     }
 }
 
-///
 impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
     for InteriorPointOptimizer<T, X>
 {
-    ///
     fn solve_preconditioner(&self, a: &'a X, b: &[T], x: &mut [T]) {
         let (_, p) = a.shape();
 
@@ -234,7 +231,6 @@ impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
         }
     }
 
-    ///
     fn mat_vec_mul(&self, _: &X, x: &Vec<T>, y: &mut Vec<T>) {
         let (_, p) = self.ata.shape();
         let x_slice = Vec::from_slice(x.slice(0..p).as_ref());
@@ -246,7 +242,6 @@ impl<'a, T: FloatNumber, X: Array2<T>> BiconjugateGradientSolver<'a, T, X>
         }
     }
 
-    ///
     fn mat_t_vec_mul(&self, a: &X, x: &Vec<T>, y: &mut Vec<T>) {
         self.mat_vec_mul(a, x, y);
     }

src/neighbors/knn_regressor.rs

@@ -88,25 +88,21 @@ pub struct KNNRegressor<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D:
 impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
     KNNRegressor<TX, TY, X, Y, D>
 {
-    ///
     fn y(&self) -> &Y {
         self.y.as_ref().unwrap()
     }
 
-    ///
     fn knn_algorithm(&self) -> &KNNAlgorithm<TX, D> {
         self.knn_algorithm
             .as_ref()
             .expect("Missing parameter: KNNAlgorithm")
     }
 
-    ///
     fn weight(&self) -> &KNNWeightFunction {
         self.weight.as_ref().expect("Missing parameter: weight")
     }
 
     #[allow(dead_code)]
-    ///
     fn k(&self) -> usize {
         self.k.unwrap()
     }

src/optimization/first_order/gradient_descent.rs

@@ -16,7 +16,6 @@ pub struct GradientDescent {
     pub g_atol: f64,
 }
 
-///
 impl Default for GradientDescent {
     fn default() -> Self {
         GradientDescent {
@@ -27,9 +26,7 @@ impl Default for GradientDescent {
     }
 }
 
-///
 impl<T: FloatNumber> FirstOrderOptimizer<T> for GradientDescent {
-    ///
     fn optimize<'a, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &'a F<'_, T, X>,

src/optimization/first_order/lbfgs.rs

@@ -33,9 +33,7 @@ pub struct LBFGS {
     pub m: usize,
 }
 
-///
 impl Default for LBFGS {
-    ///
     fn default() -> Self {
         LBFGS {
             max_iter: 1000,
@@ -52,7 +50,6 @@ impl Default for LBFGS {
 }
 
 impl LBFGS {
-    ///
     fn two_loops<T: FloatNumber + RealNumber, X: Array1<T>>(&self, state: &mut LBFGSState<T, X>) {
         let lower = state.iteration.max(self.m) - self.m;
         let upper = state.iteration;
@@ -94,7 +91,6 @@ impl LBFGS {
         state.s.mul_scalar_mut(-T::one());
     }
 
-    ///
     fn init_state<T: FloatNumber + RealNumber, X: Array1<T>>(&self, x: &X) -> LBFGSState<T, X> {
         LBFGSState {
             x: x.clone(),
@@ -118,7 +114,6 @@ impl LBFGS {
         }
     }
 
-    ///
     fn update_state<'a, T: FloatNumber + RealNumber, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &'a F<'_, T, X>,
@@ -160,7 +155,6 @@ impl LBFGS {
         df(&mut state.x_df, &state.x);
     }
 
-    ///
     fn assess_convergence<T: FloatNumber, X: Array1<T>>(
         &self,
         state: &mut LBFGSState<T, X>,
@@ -194,7 +188,6 @@ impl LBFGS {
         g_converged || x_converged || state.counter_f_tol > self.successive_f_tol
     }
 
-    ///
     fn update_hessian<T: FloatNumber, X: Array1<T>>(
         &self,
         _: &DF<'_, X>,
@@ -233,9 +226,7 @@ struct LBFGSState<T: FloatNumber, X: Array1<T>> {
     alpha: T,
 }
 
-///
 impl<T: FloatNumber + RealNumber> FirstOrderOptimizer<T> for LBFGS {
-    ///
     fn optimize<'a, X: Array1<T>, LS: LineSearchMethod<T>>(
         &self,
         f: &F<'_, T, X>,

src/optimization/line_search.rs

@@ -53,9 +53,7 @@ impl<T: Float> Default for Backtracking<T> {
     }
 }
 
-///
 impl<T: Float> LineSearchMethod<T> for Backtracking<T> {
-    ///
     fn search(
         &self,
         f: &(dyn Fn(T) -> T),