Fix unused_result lint errors.
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c235728fb0
commit
740d19437c
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@ -1,4 +1,5 @@
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#![feature(test)]
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#![feature(test)]
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#![allow(unused_macros)]
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extern crate test;
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extern crate test;
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extern crate rand;
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extern crate rand;
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@ -215,7 +215,7 @@ impl<N: Real, R: DimName, C: DimName> FiniteDimInnerSpace for MatrixMN<N, R, C>
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match Self::dimension() {
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match Self::dimension() {
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1 => {
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1 => {
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if vs.len() == 0 {
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if vs.len() == 0 {
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f(&Self::canonical_basis_element(0));
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let _ = f(&Self::canonical_basis_element(0));
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}
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}
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},
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},
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2 => {
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2 => {
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@ -227,7 +227,7 @@ impl<N: Real, R: DimName, C: DimName> FiniteDimInnerSpace for MatrixMN<N, R, C>
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let v = &vs[0];
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let v = &vs[0];
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let res = Self::from_column_slice(&[-v[1], v[0]]);
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let res = Self::from_column_slice(&[-v[1], v[0]]);
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f(&res.normalize());
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let _ = f(&res.normalize());
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}
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}
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// Otherwise, nothing.
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// Otherwise, nothing.
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@ -252,11 +252,11 @@ impl<N: Real, R: DimName, C: DimName> FiniteDimInnerSpace for MatrixMN<N, R, C>
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let _ = a.normalize_mut();
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let _ = a.normalize_mut();
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if f(&a.cross(v)) {
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if f(&a.cross(v)) {
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f(&a);
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let _ = f(&a);
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}
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}
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}
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}
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else if vs.len() == 2 {
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else if vs.len() == 2 {
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f(&vs[0].cross(&vs[1]).normalize());
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let _ = f(&vs[0].cross(&vs[1]).normalize());
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}
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}
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},
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},
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_ => {
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_ => {
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@ -144,7 +144,7 @@ macro_rules! componentwise_binop_impl(
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if self.data.is_contiguous() && rhs.data.is_contiguous() && out.data.is_contiguous() {
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if self.data.is_contiguous() && rhs.data.is_contiguous() && out.data.is_contiguous() {
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let arr1 = self.data.as_slice();
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let arr1 = self.data.as_slice();
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let arr2 = rhs.data.as_slice();
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let arr2 = rhs.data.as_slice();
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let mut out = out.data.as_mut_slice();
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let out = out.data.as_mut_slice();
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for i in 0 .. arr1.len() {
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for i in 0 .. arr1.len() {
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unsafe {
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unsafe {
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*out.get_unchecked_mut(i) = arr1.get_unchecked(i).$method(*arr2.get_unchecked(i));
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*out.get_unchecked_mut(i) = arr1.get_unchecked(i).$method(*arr2.get_unchecked(i));
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@ -175,7 +175,7 @@ macro_rules! componentwise_binop_impl(
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// This is the most common case and should be deduced at compile-time.
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// This is the most common case and should be deduced at compile-time.
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// FIXME: use specialization instead?
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// FIXME: use specialization instead?
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if self.data.is_contiguous() && rhs.data.is_contiguous() {
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if self.data.is_contiguous() && rhs.data.is_contiguous() {
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let mut arr1 = self.data.as_mut_slice();
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let arr1 = self.data.as_mut_slice();
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let arr2 = rhs.data.as_slice();
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let arr2 = rhs.data.as_slice();
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for i in 0 .. arr2.len() {
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for i in 0 .. arr2.len() {
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unsafe {
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unsafe {
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@ -206,7 +206,7 @@ macro_rules! componentwise_binop_impl(
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// FIXME: use specialization instead?
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// FIXME: use specialization instead?
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if self.data.is_contiguous() && rhs.data.is_contiguous() {
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if self.data.is_contiguous() && rhs.data.is_contiguous() {
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let arr1 = self.data.as_slice();
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let arr1 = self.data.as_slice();
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let mut arr2 = rhs.data.as_mut_slice();
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let arr2 = rhs.data.as_mut_slice();
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for i in 0 .. arr1.len() {
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for i in 0 .. arr1.len() {
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unsafe {
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unsafe {
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let res = arr1.get_unchecked(i).$method(*arr2.get_unchecked(i));
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let res = arr1.get_unchecked(i).$method(*arr2.get_unchecked(i));
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@ -218,7 +218,7 @@ macro_rules! componentwise_binop_impl(
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for j in 0 .. self.ncols() {
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for j in 0 .. self.ncols() {
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for i in 0 .. self.nrows() {
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for i in 0 .. self.nrows() {
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unsafe {
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unsafe {
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let mut r = rhs.get_unchecked_mut(i, j);
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let r = rhs.get_unchecked_mut(i, j);
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*r = self.get_unchecked(i, j).$method(*r)
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*r = self.get_unchecked(i, j).$method(*r)
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}
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}
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}
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}
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@ -104,8 +104,8 @@ impl<N: Real, D: DimSub<Dynamic>> Cholesky<N, D>
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pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>)
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pub fn solve_mut<R2: Dim, C2: Dim, S2>(&self, b: &mut Matrix<N, R2, C2, S2>)
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where S2: StorageMut<N, R2, C2>,
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where S2: StorageMut<N, R2, C2>,
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ShapeConstraint: SameNumberOfRows<R2, D> {
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ShapeConstraint: SameNumberOfRows<R2, D> {
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self.chol.solve_lower_triangular_mut(b);
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let _ = self.chol.solve_lower_triangular_mut(b);
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self.chol.tr_solve_lower_triangular_mut(b);
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let _ = self.chol.tr_solve_lower_triangular_mut(b);
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}
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}
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/// Returns the solution of the system `self * x = b` where `self` is the decomposed matrix and
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/// Returns the solution of the system `self * x = b` where `self` is the decomposed matrix and
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@ -175,8 +175,8 @@ impl<N: Real, D: DimMin<D, Output = D>> FullPivLU<N, D, D>
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if self.is_invertible() {
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if self.is_invertible() {
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self.p.permute_rows(b);
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self.p.permute_rows(b);
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self.lu.solve_lower_triangular_with_diag_mut(b, N::one());
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let _ = self.lu.solve_lower_triangular_with_diag_mut(b, N::one());
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self.lu.solve_upper_triangular_mut(b);
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let _ = self.lu.solve_upper_triangular_mut(b);
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self.q.inv_permute_rows(b);
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self.q.inv_permute_rows(b);
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true
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true
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@ -73,7 +73,7 @@ pub fn try_invert_to<N: Real, D: Dim, S>(mut matrix: MatrixN<N, D>,
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}
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}
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}
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}
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matrix.solve_lower_triangular_with_diag_mut(out, N::one());
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let _ = matrix.solve_lower_triangular_with_diag_mut(out, N::one());
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matrix.solve_upper_triangular_mut(out)
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matrix.solve_upper_triangular_mut(out)
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}
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}
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@ -216,7 +216,7 @@ impl<N: Real, D: DimMin<D, Output = D>> LU<N, D, D>
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assert!(self.lu.is_square(), "LU solve: unable to solve a non-square system.");
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assert!(self.lu.is_square(), "LU solve: unable to solve a non-square system.");
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self.p.permute_rows(b);
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self.p.permute_rows(b);
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self.lu.solve_lower_triangular_with_diag_mut(b, N::one());
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let _ = self.lu.solve_lower_triangular_with_diag_mut(b, N::one());
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self.lu.solve_upper_triangular_mut(b)
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self.lu.solve_upper_triangular_mut(b)
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}
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}
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@ -1,4 +1,4 @@
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//! Factorization of real matrices.
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//! [Reexported at the root of this crate.] Factorization of real matrices.
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mod solve;
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mod solve;
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mod determinant;
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mod determinant;
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