2020-03-21 19:16:46 +08:00
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use simba::scalar::{RealField, SubsetOf, SupersetOf};
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2020-03-22 06:22:55 +08:00
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use simba::simd::SimdRealField;
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2016-12-05 05:44:42 +08:00
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2019-03-23 21:29:07 +08:00
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use crate::base::allocator::Allocator;
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use crate::base::dimension::{DimMin, DimName, DimNameAdd, DimNameSum, U1};
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use crate::base::{DefaultAllocator, MatrixN};
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2016-12-05 05:44:42 +08:00
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2020-03-21 19:16:46 +08:00
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use crate::geometry::{
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AbstractRotation, Isometry, Similarity, SuperTCategoryOf, TAffine, Transform, Translation,
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};
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2016-12-05 05:44:42 +08:00
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/*
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* This file provides the following conversions:
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* =============================================
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*
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2017-08-03 01:37:44 +08:00
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* Similarity -> Similarity
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* Similarity -> Transform
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* Similarity -> Matrix (homogeneous)
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2016-12-05 05:44:42 +08:00
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*/
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2017-08-03 01:37:44 +08:00
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impl<N1, N2, D: DimName, R1, R2> SubsetOf<Similarity<N2, D, R2>> for Similarity<N1, D, R1>
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where
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N1: RealField + SubsetOf<N2>,
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N2: RealField + SupersetOf<N1>,
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R1: AbstractRotation<N1, D> + SubsetOf<R2>,
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R2: AbstractRotation<N2, D>,
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DefaultAllocator: Allocator<N1, D> + Allocator<N2, D>,
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{
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2016-12-05 05:44:42 +08:00
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#[inline]
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fn to_superset(&self) -> Similarity<N2, D, R2> {
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Similarity::from_isometry(self.isometry.to_superset(), self.scaling().to_superset())
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}
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#[inline]
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fn is_in_subset(sim: &Similarity<N2, D, R2>) -> bool {
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crate::is_convertible::<_, Isometry<N1, D, R1>>(&sim.isometry)
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&& crate::is_convertible::<_, N1>(&sim.scaling())
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}
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#[inline]
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fn from_superset_unchecked(sim: &Similarity<N2, D, R2>) -> Self {
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Similarity::from_isometry(
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sim.isometry.to_subset_unchecked(),
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sim.scaling().to_subset_unchecked(),
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)
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}
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}
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2017-08-03 01:37:44 +08:00
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impl<N1, N2, D, R, C> SubsetOf<Transform<N2, D, C>> for Similarity<N1, D, R>
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where
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N1: RealField,
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N2: RealField + SupersetOf<N1>,
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C: SuperTCategoryOf<TAffine>,
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R: AbstractRotation<N1, D>
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+ SubsetOf<MatrixN<N1, DimNameSum<D, U1>>>
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+ SubsetOf<MatrixN<N2, DimNameSum<D, U1>>>,
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D: DimNameAdd<U1> + DimMin<D, Output = D>, // needed by .determinant()
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DefaultAllocator: Allocator<N1, D>
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+ Allocator<N1, D, D>
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+ Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>>
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+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
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+ Allocator<(usize, usize), D>
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+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
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+ Allocator<N2, D, D>
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+ Allocator<N2, D>,
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{
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#[inline]
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fn to_superset(&self) -> Transform<N2, D, C> {
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Transform::from_matrix_unchecked(self.to_homogeneous().to_superset())
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}
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#[inline]
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fn is_in_subset(t: &Transform<N2, D, C>) -> bool {
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<Self as SubsetOf<_>>::is_in_subset(t.matrix())
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}
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#[inline]
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fn from_superset_unchecked(t: &Transform<N2, D, C>) -> Self {
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Self::from_superset_unchecked(t.matrix())
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}
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}
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2017-08-03 01:37:44 +08:00
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impl<N1, N2, D, R> SubsetOf<MatrixN<N2, DimNameSum<D, U1>>> for Similarity<N1, D, R>
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where
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N1: RealField,
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N2: RealField + SupersetOf<N1>,
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R: AbstractRotation<N1, D>
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+ SubsetOf<MatrixN<N1, DimNameSum<D, U1>>>
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+ SubsetOf<MatrixN<N2, DimNameSum<D, U1>>>,
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D: DimNameAdd<U1> + DimMin<D, Output = D>, // needed by .determinant()
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DefaultAllocator: Allocator<N1, D>
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+ Allocator<N1, D, D>
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+ Allocator<N1, DimNameSum<D, U1>, DimNameSum<D, U1>>
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+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
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+ Allocator<(usize, usize), D>
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+ Allocator<N2, DimNameSum<D, U1>, DimNameSum<D, U1>>
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+ Allocator<N2, D, D>
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+ Allocator<N2, D>,
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{
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#[inline]
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fn to_superset(&self) -> MatrixN<N2, DimNameSum<D, U1>> {
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self.to_homogeneous().to_superset()
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}
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#[inline]
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fn is_in_subset(m: &MatrixN<N2, DimNameSum<D, U1>>) -> bool {
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let mut rot = m.fixed_slice::<D, D>(0, 0).clone_owned();
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if rot
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.fixed_columns_mut::<U1>(0)
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.try_normalize_mut(N2::zero())
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.is_some()
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&& rot
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.fixed_columns_mut::<U1>(1)
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.try_normalize_mut(N2::zero())
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.is_some()
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&& rot
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.fixed_columns_mut::<U1>(2)
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.try_normalize_mut(N2::zero())
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.is_some()
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{
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// FIXME: could we avoid explicit the computation of the determinant?
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// (its sign is needed to see if the scaling factor is negative).
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if rot.determinant() < N2::zero() {
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rot.fixed_columns_mut::<U1>(0).neg_mut();
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rot.fixed_columns_mut::<U1>(1).neg_mut();
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rot.fixed_columns_mut::<U1>(2).neg_mut();
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}
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let bottom = m.fixed_slice::<U1, D>(D::dim(), 0);
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// Scalar types agree.
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m.iter().all(|e| SupersetOf::<N1>::is_in_subset(e)) &&
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// The normalized block part is a rotation.
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// rot.is_special_orthogonal(N2::default_epsilon().sqrt()) &&
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// The bottom row is (0, 0, ..., 1)
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bottom.iter().all(|e| e.is_zero()) && m[(D::dim(), D::dim())] == N2::one()
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} else {
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false
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}
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}
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#[inline]
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fn from_superset_unchecked(m: &MatrixN<N2, DimNameSum<D, U1>>) -> Self {
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let mut mm = m.clone_owned();
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let na = mm.fixed_slice_mut::<D, U1>(0, 0).normalize_mut();
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let nb = mm.fixed_slice_mut::<D, U1>(0, 1).normalize_mut();
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let nc = mm.fixed_slice_mut::<D, U1>(0, 2).normalize_mut();
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let mut scale = (na + nb + nc) / crate::convert(3.0); // We take the mean, for robustness.
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// FIXME: could we avoid the explicit computation of the determinant?
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// (its sign is needed to see if the scaling factor is negative).
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if mm.fixed_slice::<D, D>(0, 0).determinant() < N2::zero() {
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mm.fixed_slice_mut::<D, U1>(0, 0).neg_mut();
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mm.fixed_slice_mut::<D, U1>(0, 1).neg_mut();
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mm.fixed_slice_mut::<D, U1>(0, 2).neg_mut();
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scale = -scale;
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}
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let t = m.fixed_slice::<D, U1>(0, D::dim()).into_owned();
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let t = Translation {
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vector: crate::convert_unchecked(t),
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};
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2020-03-21 19:16:46 +08:00
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Self::from_parts(
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t,
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crate::convert_unchecked(mm),
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crate::convert_unchecked(scale),
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)
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2016-12-05 05:44:42 +08:00
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}
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}
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2018-10-13 16:25:34 +08:00
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2020-03-22 06:22:55 +08:00
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impl<N: SimdRealField, D: DimName, R> From<Similarity<N, D, R>> for MatrixN<N, DimNameSum<D, U1>>
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2018-10-13 16:25:34 +08:00
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where
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D: DimNameAdd<U1>,
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R: SubsetOf<MatrixN<N, DimNameSum<D, U1>>>,
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DefaultAllocator: Allocator<N, D> + Allocator<N, DimNameSum<D, U1>, DimNameSum<D, U1>>,
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{
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#[inline]
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fn from(sim: Similarity<N, D, R>) -> Self {
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sim.to_homogeneous()
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}
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}
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