Coding style fix.
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@ -11,10 +11,10 @@ use std::cmp::min;
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/// * `dim` - the dimension of the space the resulting matrix operates in
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/// * `start` - the starting dimension of the subspace of the reflexion
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/// * `vec` - the vector defining the reflection.
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pub fn householder_matrix<N: Float,
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pub fn householder_matrix<N, V, M>(dim: uint, start: uint, vec: V) -> M
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where N: Float,
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M: Eye + Indexable<(uint, uint), N>,
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V: Indexable<uint, N>>
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(dim: uint, start: uint, vec: V) -> M {
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V: Indexable<uint, N> {
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let mut qk : M = Eye::new_identity(dim);
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let subdim = vec.shape();
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@ -38,12 +38,10 @@ pub fn householder_matrix<N: Float,
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///
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/// # Arguments
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/// * `m` - matrix to decompose
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pub fn qr<N: Float,
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pub fn qr<N, V, M>(m: &M) -> (M, M)
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where N: Float,
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V: Indexable<uint, N> + Norm<N>,
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M: Clone + Eye + ColSlice<V> + Transpose
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+ Indexable<(uint, uint), N> + Mul<M, M>>
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(m: &M)
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-> (M, M) {
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M: Clone + Eye + ColSlice<V> + Transpose + Indexable<(uint, uint), N> + Mul<M, M> {
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let (rows, cols) = m.shape();
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assert!(rows >= cols);
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let mut q : M = Eye::new_identity(rows);
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@ -75,14 +73,12 @@ pub fn qr<N: Float,
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}
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/// Eigendecomposition of a square matrix using the qr algorithm.
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pub fn eigen_qr<N: Float,
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pub fn eigen_qr<N, V, V2, M>(m: &M, eps: &N, niter: uint) -> (M, V2)
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where N: Float,
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V: Indexable<uint, N> + Norm<N>,
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V2: Zero,
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M: Clone + Eye + ColSlice<V> + Transpose
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+ Indexable<(uint, uint), N> + Mul<M, M>
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+ Diag<V2> + ApproxEq<N> + Add<M, M>
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+ Sub<M, M>>
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(m: &M, eps: &N, niter: uint) -> (M, V2) {
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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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assert!(rows == cols, "The matrix being decomposed must be square.");
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