Change the SVD methods to return a Result instead of panicking

src/linalg/svd.rs

@@ -485,34 +485,40 @@ where
 
     /// Rebuild the original matrix.
     ///
-    /// This is useful if some of the singular values have been manually modified.  Panics if the
+    /// This is useful if some of the singular values have been manually modified.
-    /// right- and left- singular vectors have not been computed at construction-time.
+    /// Returns `Err` if the right- and left- singular vectors have not been
-    pub fn recompose(self) -> MatrixMN<N, R, C> {
+    /// computed at construction-time.
-        let mut u = self.u.expect("SVD recomposition: U has not been computed.");
+    pub fn recompose(self) -> Result<MatrixMN<N, R, C>, &'static str> {
-        let v_t = self.v_t
+        match (self.u, self.v_t) {
-            .expect("SVD recomposition: V^t has not been computed.");
+            (Some(_u), Some(_v_t)) => {
+                let mut u = _u;
+                let v_t = _v_t;
 
                 for i in 0..self.singular_values.len() {
                     let val = self.singular_values[i];
                     u.column_mut(i).mul_assign(val);
                 }
-
+                Ok(u * v_t)
-        u * v_t
+            }
+            (None, None) => Err("SVD recomposition: U and V^t have not been computed."),
+            (None, _) => Err("SVD recomposition: U has not been computed."),
+            (_, None) => Err("SVD recomposition: V^t has not been computed.")
+        }
     }
 
     /// Computes the pseudo-inverse of the decomposed matrix.
     ///
     /// Any singular value smaller than `eps` is assumed to be zero.
-    /// Panics if the right- and left- singular vectors have not been computed at
+    /// Returns `Err` if the right- and left- singular vectors have not
-    /// construction-time.
+    /// been computed at construction-time.
-    pub fn pseudo_inverse(mut self, eps: N) -> MatrixMN<N, C, R>
+    pub fn pseudo_inverse(mut self, eps: N) -> Result<MatrixMN<N, C, R>, &'static str>
     where
         DefaultAllocator: Allocator<N, C, R>,
     {
-        assert!(
+        if eps < N::zero() {
-            eps >= N::zero(),
+            Err("SVD pseudo inverse: the epsilon must be non-negative.")
-            "SVD pseudo inverse: the epsilon must be non-negative."
+        }
-        );
+        else {
             for i in 0..self.singular_values.len() {
                 let val = self.singular_values[i];
 
@@ -523,35 +529,31 @@ where
                 }
             }
 
-        self.recompose().transpose()
+            self.recompose().map(|m| m.transpose())
+        }
     }
 
     /// Solves the system `self * x = b` where `self` is the decomposed matrix and `x` the unknown.
     ///
     /// Any singular value smaller than `eps` is assumed to be zero.
-    /// Returns `None` if the singular vectors `U` and `V` have not been computed.
+    /// Returns `Err` if the singular vectors `U` and `V` have not been computed.
     // FIXME: make this more generic wrt the storage types and the dimensions for `b`.
     pub fn solve<R2: Dim, C2: Dim, S2>(
         &self,
         b: &Matrix<N, R2, C2, S2>,
         eps: N,
-    ) -> MatrixMN<N, C, C2>
+    ) -> Result<MatrixMN<N, C, C2>, &'static str>
     where
         S2: Storage<N, R2, C2>,
         DefaultAllocator: Allocator<N, C, C2> + Allocator<N, DimMinimum<R, C>, C2>,
         ShapeConstraint: SameNumberOfRows<R, R2>,
     {
-        assert!(
+        if eps < N::zero() {
-            eps >= N::zero(),
+            Err("SVD solve: the epsilon must be non-negative.")
-            "SVD solve: the epsilon must be non-negative."
+        }
-        );
+        else {
-        let u = self.u
+            match (&self.u, &self.v_t) {
-            .as_ref()
+                (Some(u), Some(v_t)) => {
-            .expect("SVD solve: U has not been computed.");
-        let v_t = self.v_t
-            .as_ref()
-            .expect("SVD solve: V^t has not been computed.");
-
                     let mut ut_b = u.tr_mul(b);
 
                     for j in 0..ut_b.ncols() {
@@ -567,7 +569,13 @@ where
                         }
                     }
 
-        v_t.tr_mul(&ut_b)
+                    Ok(v_t.tr_mul(&ut_b))
+                }
+                (None, None) => Err("SVD solve: U and V^t have not been computed."),
+                (None, _) => Err("SVD solve: U has not been computed."),
+                (_, None) => Err("SVD solve: V^t has not been computed.")
+            }
+        }
     }
 }
 
@@ -623,7 +631,7 @@ where
     /// Computes the pseudo-inverse of this matrix.
     ///
     /// All singular values below `eps` are considered equal to 0.
-    pub fn pseudo_inverse(self, eps: N) -> MatrixMN<N, C, R>
+    pub fn pseudo_inverse(self, eps: N) -> Result<MatrixMN<N, C, R>, &'static str>
     where
         DefaultAllocator: Allocator<N, C, R>,
     {

tests/linalg/svd.rs

@@ -9,7 +9,7 @@ mod quickcheck_tests {
         fn svd(m: DMatrix<f64>) -> bool {
             if m.len() > 0 {
                 let svd = m.clone().svd(true, true);
-                let recomp_m = svd.clone().recompose();
+                let recomp_m = svd.clone().recompose().unwrap();
                 let (u, s, v_t) = (svd.u.unwrap(), svd.singular_values, svd.v_t.unwrap());
                 let ds = DMatrix::from_diagonal(&s);
 
@@ -90,7 +90,7 @@ mod quickcheck_tests {
         fn svd_pseudo_inverse(m: DMatrix<f64>) -> bool {
             if m.len() > 0 {
                 let svd = m.clone().svd(true, true);
-                let pinv = svd.pseudo_inverse(1.0e-10);
+                let pinv = svd.pseudo_inverse(1.0e-10).unwrap();
 
                 if m.nrows() > m.ncols() {
                     println!("{}", &pinv * &m);
@@ -117,10 +117,10 @@ mod quickcheck_tests {
                 let b1 = DVector::new_random(n);
                 let b2 = DMatrix::new_random(n, nb);
 
-                let sol1 = svd.solve(&b1, 1.0e-7);
+                let sol1 = svd.solve(&b1, 1.0e-7).unwrap();
-                let sol2 = svd.solve(&b2, 1.0e-7);
+                let sol2 = svd.solve(&b2, 1.0e-7).unwrap();
 
-                let recomp = svd.recompose();
+                let recomp = svd.recompose().unwrap();
                 if !relative_eq!(m, recomp, epsilon = 1.0e-6) {
                     println!("{}{}", m, recomp);
                 }
@@ -262,22 +262,22 @@ fn svd_singular_horizontal() {
 fn svd_zeros() {
     let m = DMatrix::from_element(10, 10, 0.0);
     let svd = m.clone().svd(true, true);
-    assert_eq!(m, svd.recompose());
+    assert_eq!(Ok(m), svd.recompose());
 }
 
 #[test]
 fn svd_identity() {
     let m = DMatrix::<f64>::identity(10, 10);
     let svd = m.clone().svd(true, true);
-    assert_eq!(m, svd.recompose());
+    assert_eq!(Ok(m), svd.recompose());
 
     let m = DMatrix::<f64>::identity(10, 15);
     let svd = m.clone().svd(true, true);
-    assert_eq!(m, svd.recompose());
+    assert_eq!(Ok(m), svd.recompose());
 
     let m = DMatrix::<f64>::identity(15, 10);
     let svd = m.clone().svd(true, true);
-    assert_eq!(m, svd.recompose());
+    assert_eq!(Ok(m), svd.recompose());
 }
 
 #[test]
@@ -294,7 +294,7 @@ fn svd_with_delimited_subproblem() {
     m[(8,8)] = 16.0; m[(3,9)] = 17.0;
     m[(9,9)] = 18.0;
     let svd = m.clone().svd(true, true);
-    assert!(relative_eq!(m, svd.recompose(), epsilon = 1.0e-7));
+    assert!(relative_eq!(m, svd.recompose().unwrap(), epsilon = 1.0e-7));
 
     // Rectangular versions.
     let mut m = DMatrix::<f64>::from_element(15, 10, 0.0);
@@ -309,10 +309,10 @@ fn svd_with_delimited_subproblem() {
     m[(8,8)] = 16.0; m[(3,9)] = 17.0;
     m[(9,9)] = 18.0;
     let svd = m.clone().svd(true, true);
-    assert!(relative_eq!(m, svd.recompose(), epsilon = 1.0e-7));
+    assert!(relative_eq!(m, svd.recompose().unwrap(), epsilon = 1.0e-7));
 
     let svd = m.transpose().svd(true, true);
-    assert!(relative_eq!(m.transpose(), svd.recompose(), epsilon = 1.0e-7));
+    assert!(relative_eq!(m.transpose(), svd.recompose().unwrap(), epsilon = 1.0e-7));
 }
 
 #[test]
@@ -328,7 +328,15 @@ fn svd_fail() {
     println!("Singular values: {}", svd.singular_values);
     println!("u: {:.5}", svd.u.unwrap());
     println!("v: {:.5}", svd.v_t.unwrap());
-    let recomp = svd.recompose();
+    let recomp = svd.recompose().unwrap();
     println!("{:.5}{:.5}", m, recomp);
     assert!(relative_eq!(m, recomp, epsilon = 1.0e-5));
 }
+
+#[test]
+fn svd_err() {
+    let m = DMatrix::from_element(10, 10, 0.0);
+    let svd = m.clone().svd(false, false);
+    assert_eq!(Err("SVD recomposition: U and V^t have not been computed."), svd.clone().recompose());
+    assert_eq!(Err("SVD pseudo inverse: the epsilon must be non-negative."), svd.clone().pseudo_inverse(-1.0));
+}