feat: refactoring, adds Result to most public API

src/linalg/svd.rs

@@ -19,7 +19,7 @@
 //!                 &[0.7, 0.3, 0.8]
 //!         ]);
 //!
-//! let svd = A.svd();
+//! let svd = A.svd().unwrap();
 //! let u: DenseMatrix<f64> = svd.U;
 //! let v: DenseMatrix<f64> = svd.V;
 //! let s: Vec<f64> = svd.s;
@@ -33,6 +33,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 #![allow(non_snake_case)]
 
+use crate::error::Failed;
 use crate::linalg::BaseMatrix;
 use crate::math::num::RealNumber;
 use std::fmt::Debug;
@@ -55,23 +56,23 @@ pub struct SVD<T: RealNumber, M: SVDDecomposableMatrix<T>> {
 /// Trait that implements SVD decomposition routine for any matrix.
 pub trait SVDDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
     /// Solves Ax = b. Overrides original matrix in the process.
-    fn svd_solve_mut(self, b: Self) -> Self {
-        self.svd_mut().solve(b)
+    fn svd_solve_mut(self, b: Self) -> Result<Self, Failed> {
+        self.svd_mut().and_then(|svd| svd.solve(b))
     }
 
     /// Solves Ax = b
-    fn svd_solve(&self, b: Self) -> Self {
-        self.svd().solve(b)
+    fn svd_solve(&self, b: Self) -> Result<Self, Failed> {
+        self.svd().and_then(|svd| svd.solve(b))
     }
 
     /// Compute the SVD decomposition of a matrix.
-    fn svd(&self) -> SVD<T, Self> {
+    fn svd(&self) -> Result<SVD<T, Self>, Failed> {
         self.clone().svd_mut()
     }
 
     /// Compute the SVD decomposition of a matrix. The input matrix
     /// will be used for factorization.
-    fn svd_mut(self) -> SVD<T, Self> {
+    fn svd_mut(self) -> Result<SVD<T, Self>, Failed> {
         let mut U = self;
 
         let (m, n) = U.shape();
@@ -406,7 +407,7 @@ pub trait SVDDecomposableMatrix<T: RealNumber>: BaseMatrix<T> {
             }
         }
 
-        SVD::new(U, v, w)
+        Ok(SVD::new(U, v, w))
     }
 }
 
@@ -427,7 +428,7 @@ impl<T: RealNumber, M: SVDDecomposableMatrix<T>> SVD<T, M> {
         }
     }
 
-    pub(crate) fn solve(&self, mut b: M) -> M {
+    pub(crate) fn solve(&self, mut b: M) -> Result<M, Failed> {
         let p = b.shape().1;
 
         if self.U.shape().0 != b.shape().0 {
@@ -460,7 +461,7 @@ impl<T: RealNumber, M: SVDDecomposableMatrix<T>> SVD<T, M> {
             }
         }
 
-        b
+        Ok(b)
     }
 }
 
@@ -491,7 +492,7 @@ mod tests {
             &[0.6240573, -0.44947578, -0.6391588],
         ]);
 
-        let svd = A.svd();
+        let svd = A.svd().unwrap();
 
         assert!(V.abs().approximate_eq(&svd.V.abs(), 1e-4));
         assert!(U.abs().approximate_eq(&svd.U.abs(), 1e-4));
@@ -692,7 +693,7 @@ mod tests {
             ],
         ]);
 
-        let svd = A.svd();
+        let svd = A.svd().unwrap();
 
         assert!(V.abs().approximate_eq(&svd.V.abs(), 1e-4));
         assert!(U.abs().approximate_eq(&svd.U.abs(), 1e-4));
@@ -707,7 +708,7 @@ mod tests {
         let b = DenseMatrix::from_2d_array(&[&[0.5, 0.2], &[0.5, 0.8], &[0.5, 0.3]]);
         let expected_w =
             DenseMatrix::from_2d_array(&[&[-0.20, -1.28], &[0.87, 2.22], &[0.47, 0.66]]);
-        let w = a.svd_solve_mut(b);
+        let w = a.svd_solve_mut(b).unwrap();
         assert!(w.approximate_eq(&expected_w, 1e-2));
     }
 }