forked from M-Labs/nalgebra
Add a SquareMat
trait for square matrices.
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6a194b2b62
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@ -152,6 +152,7 @@ pub use traits::{
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ScalarAdd, ScalarSub,
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ScalarAdd, ScalarSub,
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ScalarMul, ScalarDiv,
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ScalarMul, ScalarDiv,
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Shape,
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Shape,
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SquareMat,
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ToHomogeneous,
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ToHomogeneous,
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Transform, Transformation,
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Transform, Transformation,
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Translate, Translation,
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Translate, Translation,
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@ -644,7 +645,7 @@ pub fn append_rotation_wrt_center<LV: Neg<LV>,
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/// Builds a rotation matrix from `r`.
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/// Builds a rotation matrix from `r`.
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#[inline(always)]
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#[inline(always)]
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pub fn to_rot_mat<LV, AV, M: Mat<LV, LV> + Rotation<AV>, R: RotationMatrix<LV, AV, M>>(r: &R) -> M {
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pub fn to_rot_mat<N, LV, AV, M: Mat<N, LV, LV> + Rotation<AV>, R: RotationMatrix<N, LV, AV, M>>(r: &R) -> M {
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r.to_rot_mat()
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r.to_rot_mat()
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}
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}
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@ -1,6 +1,6 @@
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use std::num::{Zero, Float};
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use std::num::{Zero, Float};
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use traits::operations::{Transpose, ApproxEq};
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use traits::operations::{Transpose, ApproxEq};
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use traits::structure::{ColSlice, Eye, Indexable, Diag};
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use traits::structure::{ColSlice, Eye, Indexable, Diag, SquareMat};
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use traits::geometry::Norm;
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use traits::geometry::Norm;
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use std::cmp::min;
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use std::cmp::min;
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@ -73,19 +73,17 @@ pub fn qr<N, V, M>(m: &M) -> (M, M)
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}
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}
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/// Eigendecomposition of a square matrix using the qr algorithm.
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/// Eigendecomposition of a square matrix using the qr algorithm.
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pub fn eigen_qr<N, V, V2, M>(m: &M, eps: &N, niter: uint) -> (M, V2)
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pub fn eigen_qr<N, V, VS, M>(m: &M, eps: &N, niter: uint) -> (M, V)
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where N: Float,
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where N: Float,
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V: Indexable<uint, N> + Norm<N>,
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VS: Indexable<uint, N> + Norm<N>,
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V2: Zero,
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M: Indexable<(uint, uint), N> + SquareMat<N, V> + ColSlice<VS> + ApproxEq<N> + Clone {
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M: Clone + Eye + ColSlice<V> + Transpose + Indexable<(uint, uint), N> + Mul<M, M>
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+ Diag<V2> + ApproxEq<N> + Add<M, M> + Sub<M, M> {
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let (rows, cols) = m.shape();
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let (rows, cols) = m.shape();
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assert!(rows == cols, "The matrix being decomposed must be square.");
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assert!(rows == cols, "The matrix being decomposed must be square.");
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let mut eigenvectors: M = Eye::new_identity(rows);
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let mut eigenvectors: M = Eye::new_identity(rows);
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let mut eigenvalues = m.clone();
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let mut eigenvalues = m.clone();
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let mut shifter: M = Eye::new_identity(rows);
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// let mut shifter: M = Eye::new_identity(rows);
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let mut iter = 0u;
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let mut iter = 0u;
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for _ in range(0, niter) {
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for _ in range(0, niter) {
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@ -112,16 +110,9 @@ pub fn eigen_qr<N, V, V2, M>(m: &M, eps: &N, niter: uint) -> (M, V2)
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}
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}
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iter = iter + 1;
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iter = iter + 1;
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// FIXME: This is a very naive implementation.
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let (q, r) = qr(&eigenvalues);;
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let shift = unsafe { eigenvalues.unsafe_at((rows - 1, rows - 1)) };
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for i in range(0, rows) {
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eigenvalues = r * q;
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unsafe { shifter.unsafe_set((i, i), shift.clone()) }
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}
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let (q, r) = qr(&eigenvalues);// - shifter));
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eigenvalues = r * q /*+ shifter*/;
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eigenvectors = eigenvectors * q;
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eigenvectors = eigenvectors * q;
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}
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}
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@ -27,7 +27,7 @@ macro_rules! iso_impl(
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macro_rules! rotation_matrix_impl(
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macro_rules! rotation_matrix_impl(
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($t: ident, $trot: ident, $tlv: ident, $tav: ident) => (
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($t: ident, $trot: ident, $tlv: ident, $tav: ident) => (
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impl<N: Cast<f32> + FloatMath + Num + Clone>
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impl<N: Cast<f32> + FloatMath + Num + Clone>
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RotationMatrix<$tlv<N>, $tav<N>, $trot<N>> for $t<N> {
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RotationMatrix<N, $tlv<N>, $tav<N>, $trot<N>> for $t<N> {
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#[inline]
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#[inline]
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fn to_rot_mat(&self) -> $trot<N> {
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fn to_rot_mat(&self) -> $trot<N> {
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self.rotation.clone()
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self.rotation.clone()
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@ -122,8 +122,7 @@ macro_rules! dim_impl(
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macro_rules! rotation_matrix_impl(
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macro_rules! rotation_matrix_impl(
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($t: ident, $tlv: ident, $tav: ident) => (
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($t: ident, $tlv: ident, $tav: ident) => (
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impl<N: Cast<f32> + FloatMath + Clone>
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impl<N: Cast<f32> + FloatMath> RotationMatrix<N, $tlv<N>, $tav<N>, $t<N>> for $t<N> {
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RotationMatrix<$tlv<N>, $tav<N>, $t<N>> for $t<N> {
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#[inline]
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#[inline]
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fn to_rot_mat(&self) -> $t<N> {
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fn to_rot_mat(&self) -> $t<N> {
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self.clone()
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self.clone()
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@ -141,7 +141,7 @@ impl<LV: Neg<LV>, AV, M: Rotation<AV> + Translation<LV>> RotationWithTranslation
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/// Trait of transformation having a rotation extractable as a rotation matrix. This can typically
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/// Trait of transformation having a rotation extractable as a rotation matrix. This can typically
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/// be implemented by quaternions to convert them to a rotation matrix.
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/// be implemented by quaternions to convert them to a rotation matrix.
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pub trait RotationMatrix<LV, AV, M: Mat<LV, LV> + Rotation<AV>> : Rotation<AV> {
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pub trait RotationMatrix<N, LV, AV, M: Mat<N, LV, LV> + Rotation<AV>> : Rotation<AV> {
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/// Gets the rotation matrix represented by `self`.
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/// Gets the rotation matrix represented by `self`.
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fn to_rot_mat(&self) -> M;
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fn to_rot_mat(&self) -> M;
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}
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}
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@ -1,16 +1,16 @@
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//! Mathematical traits.
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//! Mathematical traits.
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pub use self::geometry::{AbsoluteRotate, Cross, CrossMatrix, Dot, FromHomogeneous, Norm, Orig,
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pub use traits::geometry::{AbsoluteRotate, Cross, CrossMatrix, Dot, FromHomogeneous, Norm, Orig,
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Rotate, Rotation, RotationMatrix, RotationWithTranslation, ToHomogeneous,
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Rotate, Rotation, RotationMatrix, RotationWithTranslation, ToHomogeneous,
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Transform, Transformation, Translate, Translation, UniformSphereSample};
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Transform, Transformation, Translate, Translation, UniformSphereSample};
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pub use self::structure::{FloatVec, FloatPnt, Basis, Cast, Col, Dim, Indexable, Iterable,
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pub use traits::structure::{FloatVec, FloatPnt, Basis, Cast, Col, Dim, Indexable, Iterable,
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IterableMut, Mat, Row, NumVec, NumPnt, PntAsVec, VecAsPnt, ColSlice,
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IterableMut, Mat, SquareMat, Row, NumVec, NumPnt, PntAsVec, VecAsPnt,
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RowSlice, Diag, Eye, Shape};
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ColSlice, RowSlice, Diag, Eye, Shape};
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pub use self::operations::{Absolute, ApproxEq, Axpy, Cov, Det, Inv, LMul, Mean, Outer, POrd,
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pub use traits::operations::{Absolute, ApproxEq, Axpy, Cov, Det, Inv, LMul, Mean, Outer, POrd,
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RMul, ScalarAdd, ScalarSub, ScalarMul, ScalarDiv, Transpose};
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RMul, ScalarAdd, ScalarSub, ScalarMul, ScalarDiv, Transpose};
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pub use self::operations::{POrdering, PartialLess, PartialEqual, PartialGreater, NotComparable};
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pub use traits::operations::{POrdering, PartialLess, PartialEqual, PartialGreater, NotComparable};
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pub mod geometry;
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pub mod geometry;
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pub mod structure;
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pub mod structure;
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@ -2,7 +2,7 @@
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use std::num::Zero;
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use std::num::Zero;
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use std::slice::{Items, MutItems};
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use std::slice::{Items, MutItems};
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use traits::operations::{RMul, LMul, Axpy};
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use traits::operations::{RMul, LMul, Axpy, Transpose};
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use traits::geometry::{Dot, Norm, Orig};
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use traits::geometry::{Dot, Norm, Orig};
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/// Traits of objects which can be created from an object of type `T`.
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/// Traits of objects which can be created from an object of type `T`.
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@ -14,9 +14,19 @@ pub trait Cast<T> {
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/// Trait of matrices.
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/// Trait of matrices.
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///
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///
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/// A matrix has rows and columns and are able to multiply them.
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/// A matrix has rows and columns and are able to multiply them.
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pub trait Mat<R, C> : Row<R> + Col<C> + RMul<R> + LMul<C> { }
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pub trait Mat<N, R, C>: Row<R> + Col<C> + RMul<R> + LMul<C> + Index<(uint, uint), N> { }
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impl<M: Row<R> + Col<C> + RMul<R> + LMul<C>, R, C> Mat<R, C> for M {
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impl<N, M, R, C> Mat<N, R, C> for M
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where M: Row<R> + Col<C> + RMul<R> + LMul<C> + Index<(uint, uint), N> {
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}
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/// Trait implemented by square matrices.
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pub trait SquareMat<N, V>: Mat<N, V, V> + Mul<Self, Self> + Eye + Transpose + Add<Self, Self> +
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Sub<Self, Self> + Diag<V> {
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}
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impl<N, V, M> SquareMat<N, V> for M
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where M: Mat<N, V, V> + Mul<M, M> + Eye + Transpose + Add<M, M> + Sub<M, M> + Diag<V> {
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}
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}
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/// Trait for constructing the identity matrix
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/// Trait for constructing the identity matrix
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@ -6,7 +6,7 @@ use std::num::{Float, abs};
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use std::rand::random;
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use std::rand::random;
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use std::cmp::{min, max};
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use std::cmp::{min, max};
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use na::{Vec1, Vec3, Mat1, Mat2, Mat3, Mat4, Mat5, Mat6, Rot3, Persp3, PerspMat3, Ortho3, OrthoMat3,
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use na::{Vec1, Vec3, Mat1, Mat2, Mat3, Mat4, Mat5, Mat6, Rot3, Persp3, PerspMat3, Ortho3, OrthoMat3,
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DMat, DVec, Indexable, Row, Col};
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DMat, DVec, Row, Col};
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macro_rules! test_inv_mat_impl(
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macro_rules! test_inv_mat_impl(
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($t: ty) => (
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($t: ty) => (
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@ -141,7 +141,7 @@ fn test_rotation2() {
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fn test_index_mat2() {
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fn test_index_mat2() {
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let mat: Mat2<f64> = random();
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let mat: Mat2<f64> = random();
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assert!(mat.at((0, 1)) == na::transpose(&mat).at((1, 0)));
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assert!(mat[(0, 1)] == na::transpose(&mat)[(1, 0)]);
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}
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}
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#[test]
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#[test]
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