Implement Mean for DVec, DVecN, VecN and MatN.

Fix #166.

src/structs/dmat.rs

@@ -502,8 +502,8 @@ impl<N: BaseNum + Cast<f64> + Clone> Mean<DVec<N>> for DMat<N> {
         let mut res: DVec<N> = DVec::new_zeros(self.ncols);
         let normalizer: N    = Cast::from(1.0f64 / self.nrows as f64);
 
-        for i in 0..self.nrows {
+        for i in 0 .. self.nrows {
-            for j in 0..self.ncols {
+            for j in 0 .. self.ncols {
                 unsafe {
                     let acc = res.unsafe_at(j) + self.unsafe_at((i, j)) * normalizer;
                     res.unsafe_set(j, acc);
@@ -524,8 +524,8 @@ impl<N: BaseNum + Cast<f64> + Clone> Cov<DMat<N>> for DMat<N> {
         let mean = self.mean();
 
         // FIXME: use the rows iterator when available
-        for i in 0..self.nrows {
+        for i in 0 .. self.nrows {
-            for j in 0..self.ncols {
+            for j in 0 .. self.ncols {
                 unsafe {
                     centered.unsafe_set((i, j), self.unsafe_at((i, j)) - mean.unsafe_at(j));
                 }
@@ -535,6 +535,7 @@ impl<N: BaseNum + Cast<f64> + Clone> Cov<DMat<N>> for DMat<N> {
         // FIXME: return a triangular matrix?
         let fnormalizer: f64 = Cast::from(self.nrows() - 1);
         let normalizer: N    = Cast::from(fnormalizer);
+
         // FIXME: this will do 2 allocations for temporaries!
         (Transpose::transpose(&centered) * centered) / normalizer
     }
@@ -545,10 +546,12 @@ impl<N: Copy + Clone> ColSlice<DVec<N>> for DMat<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
+
+        // 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_slice(row_end - row_start, &self.mij[start .. stop]);
+
         slice
     }
 }
@@ -558,6 +561,7 @@ impl<N: Copy> RowSlice<DVec<N>> for DMat<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(col_end - col_start)
         };
@@ -568,6 +572,7 @@ impl<N: Copy> RowSlice<DVec<N>> for DMat<N> {
             }
             slice_idx += 1;
         }
+
         slice
     }
 }

src/structs/dvec.rs

@@ -8,9 +8,9 @@ use std::iter::repeat;
 use std::ops::{Add, Sub, Mul, Div, Neg, Index, IndexMut};
 use rand::{self, Rand};
 use num::{Zero, One};
-use traits::operations::{ApproxEq, Axpy};
+use traits::operations::{ApproxEq, Axpy, Mean};
 use traits::geometry::{Dot, Norm};
-use traits::structure::{Iterable, IterableMut, Indexable, Shape, BaseFloat, BaseNum};
+use traits::structure::{Iterable, IterableMut, Indexable, Shape, BaseFloat, BaseNum, Cast};
 #[cfg(feature="arbitrary")]
 use quickcheck::{Arbitrary, Gen};
 

src/structs/dvec_macros.rs

@@ -290,6 +290,14 @@ macro_rules! dvec_impl(
             }
         }
 
+        impl<N: BaseFloat + Cast<f64>> Mean<N> for $dvec<N> {
+            #[inline]
+            fn mean(&self) -> N {
+                let normalizer = ::cast(1.0f64 / self.len() as f64);
+                self.iter().fold(::zero(), |acc, x| acc + *x * normalizer)
+            }
+        }
+
         impl<N: ApproxEq<N>> ApproxEq<N> for $dvec<N> {
             #[inline]
             fn approx_epsilon(_: Option<$dvec<N>>) -> N {

src/structs/mat.rs

@@ -14,7 +14,7 @@ use structs::dvec::{DVec1, DVec2, DVec3, DVec4, DVec5, DVec6};
 
 use traits::structure::{Cast, Row, Col, Iterable, IterableMut, Dim, Indexable, Eye, ColSlice,
                         RowSlice, Diag, DiagMut, Shape, BaseFloat, BaseNum, Repeat};
-use traits::operations::{Absolute, Transpose, Inv, Outer, EigenQR};
+use traits::operations::{Absolute, Transpose, Inv, Outer, EigenQR, Mean};
 use traits::geometry::{ToHomogeneous, FromHomogeneous, Orig};
 use linalg;
 #[cfg(feature="arbitrary")]
@@ -81,6 +81,7 @@ outer_impl!(Vec1, Mat1);
 eigen_qr_impl!(Mat1, Vec1);
 arbitrary_impl!(Mat1, m11);
 rand_impl!(Mat1, m11);
+mean_impl!(Mat1, Vec1, 1);
 
 /// Square matrix of dimension 2.
 #[repr(C)]
@@ -134,6 +135,7 @@ outer_impl!(Vec2, Mat2);
 eigen_qr_impl!(Mat2, Vec2);
 arbitrary_impl!(Mat2, m11, m12, m21, m22);
 rand_impl!(Mat2, m11, m12, m21, m22);
+mean_impl!(Mat2, Vec2, 2);
 
 /// Square matrix of dimension 3.
 #[repr(C)]
@@ -230,6 +232,7 @@ rand_impl!(Mat3,
     m21, m22, m23,
     m31, m32, m33
 );
+mean_impl!(Mat3, Vec3, 3);
 
 /// Square matrix of dimension 4.
 #[repr(C)]
@@ -349,6 +352,7 @@ rand_impl!(Mat4,
   m31, m32, m33, m34,
   m41, m42, m43, m44
 );
+mean_impl!(Mat4, Vec4, 4);
 
 /// Square matrix of dimension 5.
 #[repr(C)]
@@ -485,6 +489,7 @@ rand_impl!(Mat5,
   m41, m42, m43, m44, m45,
   m51, m52, m53, m54, m55
 );
+mean_impl!(Mat5, Vec5, 5);
 
 /// Square matrix of dimension 6.
 #[repr(C)]
@@ -626,3 +631,4 @@ rand_impl!(Mat6,
   m51, m52, m53, m54, m55, m56,
   m61, m62, m63, m64, m65, m66
 );
+mean_impl!(Mat6, Vec6, 6);

src/structs/mat_macros.rs

@@ -726,3 +726,26 @@ macro_rules! eigen_qr_impl(
         }
     )
 );
+
+
+macro_rules! mean_impl(
+    ($t: ident, $v: ident, $dim: expr) => (
+        impl<N: BaseNum + Cast<f64> + Clone> Mean<$v<N>> for $t<N> {
+            fn mean(&self) -> $v<N> {
+                let mut res: $v<N> = ::zero();
+                let normalizer: N  = Cast::from(1.0f64 / $dim as f64);
+        
+                for i in 0 .. $dim {
+                    for j in 0 .. $dim {
+                        unsafe {
+                            let acc = res.unsafe_at(j) + self.unsafe_at((i, j)) * normalizer;
+                            res.unsafe_set(j, acc);
+                        }
+                    }
+                }
+        
+                res
+            }
+        }
+    )
+);

src/structs/vec.rs

@@ -9,7 +9,7 @@ use std::slice::{Iter, IterMut};
 use std::iter::{Iterator, FromIterator, IntoIterator};
 use rand::{Rand, Rng};
 use num::{Zero, One};
-use traits::operations::{ApproxEq, POrd, POrdering, Axpy, Absolute};
+use traits::operations::{ApproxEq, POrd, POrdering, Axpy, Absolute, Mean};
 use traits::geometry::{Transform, Rotate, FromHomogeneous, ToHomogeneous, Dot, Norm,
                        Translation, Translate};
 use traits::structure::{Basis, Cast, Dim, Indexable, Iterable, IterableMut, Shape, NumVec,
@@ -90,6 +90,7 @@ num_float_vec_impl!(Vec1);
 absolute_vec_impl!(Vec1, x);
 arbitrary_impl!(Vec1, x);
 rand_impl!(Vec1, x);
+mean_impl!(Vec1);
 
 /// Vector of dimension 2.
 #[repr(C)]
@@ -143,6 +144,7 @@ num_float_vec_impl!(Vec2);
 absolute_vec_impl!(Vec2, x, y);
 arbitrary_impl!(Vec2, x, y);
 rand_impl!(Vec2, x, y);
+mean_impl!(Vec2);
 
 /// Vector of dimension 3.
 #[repr(C)]
@@ -198,6 +200,7 @@ num_float_vec_impl!(Vec3);
 absolute_vec_impl!(Vec3, x, y, z);
 arbitrary_impl!(Vec3, x, y, z);
 rand_impl!(Vec3, x, y, z);
+mean_impl!(Vec3);
 
 
 /// Vector of dimension 4.
@@ -256,6 +259,7 @@ num_float_vec_impl!(Vec4);
 absolute_vec_impl!(Vec4, x, y, z, w);
 arbitrary_impl!(Vec4, x, y, z, w);
 rand_impl!(Vec4, x, y, z, w);
+mean_impl!(Vec4);
 
 /// Vector of dimension 5.
 #[repr(C)]
@@ -315,6 +319,7 @@ num_float_vec_impl!(Vec5);
 absolute_vec_impl!(Vec5, x, y, z, w, a);
 arbitrary_impl!(Vec5, x, y, z, w, a);
 rand_impl!(Vec5, x, y, z, w, a);
+mean_impl!(Vec5);
 
 /// Vector of dimension 6.
 #[repr(C)]
@@ -374,3 +379,4 @@ num_float_vec_impl!(Vec6);
 absolute_vec_impl!(Vec6, x, y, z, w, a, b);
 arbitrary_impl!(Vec6, x, y, z, w, a, b);
 rand_impl!(Vec6, x, y, z, w, a, b);
+mean_impl!(Vec6);

src/structs/vec_macros.rs

@@ -805,3 +805,15 @@ macro_rules! rand_impl(
         }
     )
 );
+
+macro_rules! mean_impl(
+    ($t: ident) => (
+        impl<N: BaseFloat + Cast<f64>> Mean<N> for $t<N> {
+            #[inline]
+            fn mean(&self) -> N {
+                let normalizer = ::cast(1.0f64 / self.len() as f64);
+                self.iter().fold(::zero(), |acc, x| acc + *x * normalizer)
+            }
+        }
+    )
+);

src/traits/operations.rs

@@ -325,7 +325,7 @@ pub trait Cov<M> {
     }
 }
 
-/// Trait for computing the covariance of a set of data.
+/// Trait for computing the mean of a set of data.
 pub trait Mean<N> {
     /// Computes the mean of the observations stored by `v`.
     ///