QR decomposition depends less on DMat internals

src/lib.rs

@@ -119,6 +119,7 @@ extern crate test;
 pub mod na;
 mod structs;
 mod traits;
+mod linalg;
 
 // mod lower_triangular;
 // mod chol;

src/linalg/decompositions.rs

@@ -0,0 +1,58 @@
+use std::num::{Zero, Float};
+use na::DVec;
+use na::DMat;
+use traits::operations::{Transpose, ColSlice};
+use traits::geometry::Norm;
+use std::cmp::min;
+
+/// QR decomposition using Householder reflections
+/// # Arguments
+/// * `m` matrix to decompose
+pub fn decomp_qr<N: Clone + Num + Float>(m: &DMat<N>) -> (DMat<N>, DMat<N>) {
+    let rows = m.nrows();
+    let cols = m.ncols();
+    assert!(rows >= cols);
+    let mut q : DMat<N> = DMat::new_identity(rows);
+    let mut r = m.clone();
+
+    let subtract_reflection = |vec: DVec<N>| -> DMat<N> {
+        // FIXME: we don't handle the complex case here
+        let mut qk : DMat<N> = DMat::new_identity(rows);
+        let start = rows - vec.at.len();
+        for j in range(start, rows) {
+            for i in range(start, rows) {
+                unsafe {
+                    let vv = vec.at_fast(i-start)*vec.at_fast(j-start);
+                    let qkij = qk.at_fast(i,j);
+                    qk.set_fast(i, j, qkij - vv - vv);
+                }
+            }
+        }
+        qk
+    };
+
+    let iterations = min(rows-1, cols);
+
+    for ite in range(0u, iterations) {
+        let mut v = r.col_slice(ite, ite, rows);
+        //let mut v = r.col_slice<DVec<N>>(ite, rows-ite, rows);
+        let alpha =
+            if unsafe { v.at_fast(ite) } >= Zero::zero() {
+                -Norm::norm(&v)
+            }
+            else {
+                Norm::norm(&v)
+            };
+        unsafe {
+            let x = v.at_fast(0);
+            v.set_fast(0, x - alpha);
+        }
+        let _ = v.normalize();
+        let qk = subtract_reflection(v);
+        r = qk * r;
+        q = q * Transpose::transpose_cpy(&qk);
+    }
+
+    (q, r)
+}
+

src/linalg/mod.rs

@@ -0,0 +1,4 @@
+
+pub use self::decompositions::decomp_qr;
+
+mod decompositions;

src/na.rs

@@ -39,12 +39,12 @@ pub use traits::{
     Transpose,
     UniformSphereSample,
     AnyVec,
-    VecExt
+    VecExt,
+    ColSlice, RowSlice
 };
 
 pub use structs::{
     Identity,
-    decomp_qr,
     DMat, DVec,
     Iso2, Iso3, Iso4,
     Mat1, Mat2, Mat3, Mat4,
@@ -53,6 +53,10 @@ pub use structs::{
     Vec0, Vec1, Vec2, Vec3, Vec4, Vec5, Vec6
 };
 
+pub use linalg::{
+    decomp_qr
+};
+
 /// Traits to work around the language limitations related to operator overloading.
 ///
 /// The trait names are formed by:

src/structs/dmat.rs

@@ -4,14 +4,12 @@
 
 use rand::Rand;
 use rand;
-use std::num::{One, Zero, Float};
+use std::num::{One, Zero};
 use traits::operations::ApproxEq;
 use std::mem;
 use structs::dvec::{DVec, DVecMulRhs};
-use traits::operations::{Inv, Transpose, Mean, Cov};
+use traits::operations::{Inv, Transpose, Mean, Cov, ColSlice, RowSlice};
 use traits::structure::Cast;
-use traits::geometry::Norm;
-use std::cmp::min;
 use std::fmt::{Show, Formatter, Result};
 
 #[doc(hidden)]
@@ -497,61 +495,38 @@ impl<N: Clone + Num + Cast<f32> + DMatDivRhs<N, DMat<N>> + ToStr > Cov<DMat<N>>
     }
 }
 
+impl<N: Clone> ColSlice<DVec<N>> for DMat<N> {
+    fn col_slice(&self, col_id :uint, row_start: uint, row_end: uint) -> DVec<N> {
+        assert!(col_id < self.ncols);
+        assert!(row_start < row_end);
+        assert!(row_end <= self.nrows);
+        // we can init from slice thanks to the matrix being column major
+        let start= self.offset(row_start, col_id);
+        let stop = self.offset(row_end, col_id);
+        let slice = DVec::from_vec(
+            row_end - row_start, self.mij.slice(start, stop));
+        slice
+    }
+}
 
-/// QR decomposition using Householder reflections
-/// # Arguments
-/// * `m` matrix to decompose
-pub fn decomp_qr<N: Clone + Num + Float + Show>(m: &DMat<N>) -> (DMat<N>, DMat<N>) {
-  let rows = m.nrows();
-  let cols = m.ncols();
-  assert!(rows >= cols);
-  let mut q : DMat<N> = DMat::new_identity(rows);
-  let mut r = m.clone();
-
-  let subtract_reflection = |vec: DVec<N>| -> DMat<N> {
-      // FIXME: we don't handle the complex case here
-      let mut qk : DMat<N> = DMat::new_identity(rows);
-      let start = rows - vec.at.len();
-      for j in range(start, rows) {
-          for i in range(start, rows) {
-              unsafe {
-                  let vv = vec.at_fast(i-start)*vec.at_fast(j-start);
-                  let qkij = qk.at_fast(i,j);
-                  qk.set_fast(i, j, qkij - vv - vv);
-              }
-          }
-      }
-      qk
-  };
-
-  let iterations = min(rows-1, cols);
-
-  for ite in range(0u, iterations) {
-      // we get the ite-th column truncated from its first ite elements,
-      // this is fast thanks to the matrix being column major
-      let start= m.offset(ite, ite);
-      let stop = m.offset(rows, ite);
-      let mut v = DVec::from_vec(rows - ite, r.mij.slice(start, stop));
-      let alpha =
-          if unsafe { v.at_fast(ite) } >= Zero::zero() {
-              -Norm::norm(&v)
-          }
-          else {
-              Norm::norm(&v)
+impl<N: Clone> RowSlice<DVec<N>> for DMat<N> {
+    fn row_slice(&self, row_id :uint, col_start: uint, col_end: uint) -> DVec<N> {
+        assert!(row_id < self.nrows);
+        assert!(col_start < col_end);
+        assert!(col_end <= self.ncols);
+        let mut slice : DVec<N> = unsafe {
+            DVec::new_uninitialized(self.nrows)
         };
+        let mut slice_idx = 0u;
+        for col_id in range(col_start, col_end) {
             unsafe {
-          let x = v.at_fast(0);
-          v.set_fast(0, x - alpha);
+                slice.set_fast(slice_idx, self.at_fast(row_id, col_id));
             }
-      let _ = v.normalize();
-      let qk = subtract_reflection(v);
-      r = qk * r;
-      q = q * Transpose::transpose_cpy(&qk);
+            slice_idx += 1;
+        }
+        slice
     }
-
-  (q, r)
 }
-
 
 impl<N: ApproxEq<N>> ApproxEq<N> for DMat<N> {
     #[inline]
@@ -578,9 +553,9 @@ impl<N: Show + Clone> Show for DMat<N> {
     fn fmt(&self, form:&mut Formatter) -> Result {
         for i in range(0u, self.nrows()) {
             for j in range(0u, self.ncols()) {
-                write!(form.buf, "{} ", self.at(i, j));
+                let _ = write!(form.buf, "{} ", self.at(i, j));
             }
-            write!(form.buf, "\n");
+            let _ = write!(form.buf, "\n");
         }
         write!(form.buf, "\n")
     }

src/structs/mod.rs

@@ -1,7 +1,6 @@
 //! Data structures and implementations.
 
 pub use self::dmat::DMat;
-pub use self::dmat::decomp_qr;
 pub use self::dvec::DVec;
 pub use self::vec::{Vec0, Vec1, Vec2, Vec3, Vec4, Vec5, Vec6};
 pub use self::mat::{Identity, Mat1, Mat2, Mat3, Mat4, Mat5, Mat6};

src/traits/mod.rs

@@ -8,7 +8,7 @@ pub use self::structure::{FloatVec, FloatVecExt, Basis, Cast, Col, Dim, Indexabl
                           Iterable, IterableMut, Mat, Row, AnyVec, VecExt};
 
 pub use self::operations::{Absolute, ApproxEq, Cov, Inv, LMul, Mean, Outer, PartialOrd, RMul,
-                           ScalarAdd, ScalarSub, Transpose};
+                           ScalarAdd, ScalarSub, Transpose, ColSlice, RowSlice};
 pub use self::operations::{PartialOrdering, PartialLess, PartialEqual, PartialGreater, NotComparable};
 
 pub mod geometry;

src/traits/operations.rs

@@ -244,6 +244,18 @@ pub trait Mean<N> {
     fn mean(&Self) -> N;
 }
 
+/// Trait for objects that support column slicing
+pub trait ColSlice<VecLike> {
+    /// Returns a view to a slice of a column of a matrix.
+    fn col_slice(&self, col_id :uint, row_start: uint, row_end: uint) -> VecLike;
+}
+
+/// Trait for objects that support column slicing
+pub trait RowSlice<VecLike> {
+    /// Returns a view to a slice of a row of a matrix.
+    fn row_slice(&self, row_id :uint, col_start: uint, col_end: uint) -> VecLike;
+}
+
 // /// Cholesky decomposition.
 // pub trait Chol {
 //     /// Performs the cholesky decomposition on `self`. The resulting upper-triangular matrix is