parent
cc2a70664d
commit
660b868603
|
@ -493,6 +493,57 @@ impl<N: Scalar, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
|
|||
res
|
||||
}
|
||||
|
||||
/// Folds a function `f` on each entry of `self`.
|
||||
#[inline]
|
||||
pub fn fold<Acc>(&self, init: Acc, mut f: impl FnMut(Acc, N) -> Acc) -> Acc {
|
||||
let (nrows, ncols) = self.data.shape();
|
||||
|
||||
let mut res = init;
|
||||
|
||||
for j in 0..ncols.value() {
|
||||
for i in 0..nrows.value() {
|
||||
unsafe {
|
||||
let a = *self.data.get_unchecked(i, j);
|
||||
res = f(res, a)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
|
||||
/// Folds a function `f` on each pairs of entries from `self` and `rhs`.
|
||||
#[inline]
|
||||
pub fn zip_fold<N2, R2, C2, S2, Acc>(&self, rhs: &Matrix<N2, R2, C2, S2>, init: Acc, mut f: impl FnMut(Acc, N, N2) -> Acc) -> Acc
|
||||
where
|
||||
N2: Scalar,
|
||||
R2: Dim,
|
||||
C2: Dim,
|
||||
S2: Storage<N2, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2>
|
||||
{
|
||||
let (nrows, ncols) = self.data.shape();
|
||||
|
||||
let mut res = init;
|
||||
|
||||
assert!(
|
||||
(nrows.value(), ncols.value()) == rhs.shape(),
|
||||
"Matrix simultaneous traversal error: dimension mismatch."
|
||||
);
|
||||
|
||||
for j in 0..ncols.value() {
|
||||
for i in 0..nrows.value() {
|
||||
unsafe {
|
||||
let a = *self.data.get_unchecked(i, j);
|
||||
let b = *rhs.data.get_unchecked(i, j);
|
||||
res = f(res, a, b)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
|
||||
/// Transposes `self` and store the result into `out`.
|
||||
#[inline]
|
||||
pub fn transpose_to<R2, C2, SB>(&self, out: &mut Matrix<N, R2, C2, SB>)
|
||||
|
@ -1251,67 +1302,6 @@ impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
|
|||
}
|
||||
}
|
||||
|
||||
impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
|
||||
/// The squared L2 norm of this vector.
|
||||
#[inline]
|
||||
pub fn norm_squared(&self) -> N {
|
||||
let mut res = N::zero();
|
||||
|
||||
for i in 0..self.ncols() {
|
||||
let col = self.column(i);
|
||||
res += col.dot(&col)
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
|
||||
/// The L2 norm of this matrix.
|
||||
#[inline]
|
||||
pub fn norm(&self) -> N {
|
||||
self.norm_squared().sqrt()
|
||||
}
|
||||
|
||||
/// A synonym for the norm of this matrix.
|
||||
///
|
||||
/// Aka the length.
|
||||
///
|
||||
/// This function is simply implemented as a call to `norm()`
|
||||
#[inline]
|
||||
pub fn magnitude(&self) -> N {
|
||||
self.norm()
|
||||
}
|
||||
|
||||
/// A synonym for the squared norm of this matrix.
|
||||
///
|
||||
/// Aka the squared length.
|
||||
///
|
||||
/// This function is simply implemented as a call to `norm_squared()`
|
||||
#[inline]
|
||||
pub fn magnitude_squared(&self) -> N {
|
||||
self.norm_squared()
|
||||
}
|
||||
|
||||
/// Returns a normalized version of this matrix.
|
||||
#[inline]
|
||||
pub fn normalize(&self) -> MatrixMN<N, R, C>
|
||||
where DefaultAllocator: Allocator<N, R, C> {
|
||||
self / self.norm()
|
||||
}
|
||||
|
||||
/// Returns a normalized version of this matrix unless its norm as smaller or equal to `eps`.
|
||||
#[inline]
|
||||
pub fn try_normalize(&self, min_norm: N) -> Option<MatrixMN<N, R, C>>
|
||||
where DefaultAllocator: Allocator<N, R, C> {
|
||||
let n = self.norm();
|
||||
|
||||
if n <= min_norm {
|
||||
None
|
||||
} else {
|
||||
Some(self / n)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<N: Scalar + Zero + One + ClosedAdd + ClosedSub + ClosedMul, D: Dim, S: Storage<N, D>>
|
||||
Vector<N, D, S>
|
||||
{
|
||||
|
@ -1386,32 +1376,6 @@ impl<N: Real, D: Dim, S: Storage<N, D>> Unit<Vector<N, D, S>> {
|
|||
}
|
||||
}
|
||||
|
||||
impl<N: Real, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
|
||||
/// Normalizes this matrix in-place and returns its norm.
|
||||
#[inline]
|
||||
pub fn normalize_mut(&mut self) -> N {
|
||||
let n = self.norm();
|
||||
*self /= n;
|
||||
|
||||
n
|
||||
}
|
||||
|
||||
/// Normalizes this matrix in-place or does nothing if its norm is smaller or equal to `eps`.
|
||||
///
|
||||
/// If the normalization succeeded, returns the old normal of this matrix.
|
||||
#[inline]
|
||||
pub fn try_normalize_mut(&mut self, min_norm: N) -> Option<N> {
|
||||
let n = self.norm();
|
||||
|
||||
if n <= min_norm {
|
||||
None
|
||||
} else {
|
||||
*self /= n;
|
||||
Some(n)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<N, R: Dim, C: Dim, S> AbsDiffEq for Unit<Matrix<N, R, C, S>>
|
||||
where
|
||||
N: Scalar + AbsDiffEq,
|
||||
|
|
|
@ -0,0 +1,203 @@
|
|||
use num::{Signed, Zero};
|
||||
use std::cmp::PartialOrd;
|
||||
|
||||
use allocator::Allocator;
|
||||
use ::{Real, Scalar};
|
||||
use storage::{Storage, StorageMut};
|
||||
use base::{DefaultAllocator, Matrix, Dim, MatrixMN};
|
||||
use constraint::{SameNumberOfRows, SameNumberOfColumns, ShapeConstraint};
|
||||
|
||||
|
||||
// FIXME: this should be be a trait on alga?
|
||||
pub trait Norm<N: Scalar> {
|
||||
fn norm<R, C, S>(&self, m: &Matrix<N, R, C, S>) -> N
|
||||
where R: Dim, C: Dim, S: Storage<N, R, C>;
|
||||
fn metric_distance<R1, C1, S1, R2, C2, S2>(&self, m1: &Matrix<N, R1, C1, S1>, m2: &Matrix<N, R2, C2, S2>) -> N
|
||||
where R1: Dim, C1: Dim, S1: Storage<N, R1, C1>,
|
||||
R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2>;
|
||||
}
|
||||
|
||||
/// Euclidean norm.
|
||||
pub struct EuclideanNorm;
|
||||
/// Lp norm.
|
||||
pub struct LpNorm(pub i32);
|
||||
/// L-infinite norm aka. Chebytchev norm aka. uniform norm aka. suppremum norm.
|
||||
pub struct UniformNorm;
|
||||
|
||||
impl<N: Real> Norm<N> for EuclideanNorm {
|
||||
#[inline]
|
||||
fn norm<R, C, S>(&self, m: &Matrix<N, R, C, S>) -> N
|
||||
where R: Dim, C: Dim, S: Storage<N, R, C> {
|
||||
m.norm_squared().sqrt()
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn metric_distance<R1, C1, S1, R2, C2, S2>(&self, m1: &Matrix<N, R1, C1, S1>, m2: &Matrix<N, R2, C2, S2>) -> N
|
||||
where R1: Dim, C1: Dim, S1: Storage<N, R1, C1>,
|
||||
R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
|
||||
m1.zip_fold(m2, N::zero(), |acc, a, b| {
|
||||
let diff = a - b;
|
||||
acc + diff * diff
|
||||
}).sqrt()
|
||||
}
|
||||
}
|
||||
|
||||
impl<N: Real> Norm<N> for LpNorm {
|
||||
#[inline]
|
||||
fn norm<R, C, S>(&self, m: &Matrix<N, R, C, S>) -> N
|
||||
where R: Dim, C: Dim, S: Storage<N, R, C> {
|
||||
m.fold(N::zero(), |a, b| {
|
||||
a + b.abs().powi(self.0)
|
||||
}).powf(::convert(1.0 / (self.0 as f64)))
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn metric_distance<R1, C1, S1, R2, C2, S2>(&self, m1: &Matrix<N, R1, C1, S1>, m2: &Matrix<N, R2, C2, S2>) -> N
|
||||
where R1: Dim, C1: Dim, S1: Storage<N, R1, C1>,
|
||||
R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
|
||||
m1.zip_fold(m2, N::zero(), |acc, a, b| {
|
||||
let diff = a - b;
|
||||
acc + diff.abs().powi(self.0)
|
||||
}).powf(::convert(1.0 / (self.0 as f64)))
|
||||
}
|
||||
}
|
||||
|
||||
impl<N: Scalar + PartialOrd + Signed> Norm<N> for UniformNorm {
|
||||
#[inline]
|
||||
fn norm<R, C, S>(&self, m: &Matrix<N, R, C, S>) -> N
|
||||
where R: Dim, C: Dim, S: Storage<N, R, C> {
|
||||
m.amax()
|
||||
}
|
||||
|
||||
#[inline]
|
||||
fn metric_distance<R1, C1, S1, R2, C2, S2>(&self, m1: &Matrix<N, R1, C1, S1>, m2: &Matrix<N, R2, C2, S2>) -> N
|
||||
where R1: Dim, C1: Dim, S1: Storage<N, R1, C1>,
|
||||
R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R1, R2> + SameNumberOfColumns<C1, C2> {
|
||||
m1.zip_fold(m2, N::zero(), |acc, a, b| {
|
||||
let val = (a - b).abs();
|
||||
if val > acc {
|
||||
val
|
||||
} else {
|
||||
acc
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
impl<N: Real, R: Dim, C: Dim, S: Storage<N, R, C>> Matrix<N, R, C, S> {
|
||||
/// The squared L2 norm of this vector.
|
||||
#[inline]
|
||||
pub fn norm_squared(&self) -> N {
|
||||
let mut res = N::zero();
|
||||
|
||||
for i in 0..self.ncols() {
|
||||
let col = self.column(i);
|
||||
res += col.dot(&col)
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
|
||||
/// The L2 norm of this matrix.
|
||||
#[inline]
|
||||
pub fn norm(&self) -> N {
|
||||
self.norm_squared().sqrt()
|
||||
}
|
||||
|
||||
/// Computes the metric distance between `self` and `rhs` using the Euclidean metric.
|
||||
#[inline]
|
||||
pub fn metric_distance<R2, C2, S2>(&self, rhs: &Matrix<N, R2, C2, S2>) -> N
|
||||
where R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2> {
|
||||
self.apply_metric_distance(rhs, &EuclideanNorm)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn apply_norm(&self, norm: &impl Norm<N>) -> N {
|
||||
norm.norm(self)
|
||||
}
|
||||
|
||||
#[inline]
|
||||
pub fn apply_metric_distance<R2, C2, S2>(&self, rhs: &Matrix<N, R2, C2, S2>, norm: &impl Norm<N>) -> N
|
||||
where R2: Dim, C2: Dim, S2: Storage<N, R2, C2>,
|
||||
ShapeConstraint: SameNumberOfRows<R, R2> + SameNumberOfColumns<C, C2> {
|
||||
norm.metric_distance(self,rhs)
|
||||
}
|
||||
|
||||
/// The Lp norm of this matrix.
|
||||
#[inline]
|
||||
pub fn lp_norm(&self, p: i32) -> N {
|
||||
self.apply_norm(&LpNorm(p))
|
||||
}
|
||||
|
||||
/// A synonym for the norm of this matrix.
|
||||
///
|
||||
/// Aka the length.
|
||||
///
|
||||
/// This function is simply implemented as a call to `norm()`
|
||||
#[inline]
|
||||
pub fn magnitude(&self) -> N {
|
||||
self.norm()
|
||||
}
|
||||
|
||||
/// A synonym for the squared norm of this matrix.
|
||||
///
|
||||
/// Aka the squared length.
|
||||
///
|
||||
/// This function is simply implemented as a call to `norm_squared()`
|
||||
#[inline]
|
||||
pub fn magnitude_squared(&self) -> N {
|
||||
self.norm_squared()
|
||||
}
|
||||
|
||||
/// Returns a normalized version of this matrix.
|
||||
#[inline]
|
||||
pub fn normalize(&self) -> MatrixMN<N, R, C>
|
||||
where DefaultAllocator: Allocator<N, R, C> {
|
||||
self / self.norm()
|
||||
}
|
||||
|
||||
/// Returns a normalized version of this matrix unless its norm as smaller or equal to `eps`.
|
||||
#[inline]
|
||||
pub fn try_normalize(&self, min_norm: N) -> Option<MatrixMN<N, R, C>>
|
||||
where DefaultAllocator: Allocator<N, R, C> {
|
||||
let n = self.norm();
|
||||
|
||||
if n <= min_norm {
|
||||
None
|
||||
} else {
|
||||
Some(self / n)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
impl<N: Real, R: Dim, C: Dim, S: StorageMut<N, R, C>> Matrix<N, R, C, S> {
|
||||
/// Normalizes this matrix in-place and returns its norm.
|
||||
#[inline]
|
||||
pub fn normalize_mut(&mut self) -> N {
|
||||
let n = self.norm();
|
||||
*self /= n;
|
||||
|
||||
n
|
||||
}
|
||||
|
||||
/// Normalizes this matrix in-place or does nothing if its norm is smaller or equal to `eps`.
|
||||
///
|
||||
/// If the normalization succeeded, returns the old normal of this matrix.
|
||||
#[inline]
|
||||
pub fn try_normalize_mut(&mut self, min_norm: N) -> Option<N> {
|
||||
let n = self.norm();
|
||||
|
||||
if n <= min_norm {
|
||||
None
|
||||
} else {
|
||||
*self /= n;
|
||||
Some(n)
|
||||
}
|
||||
}
|
||||
}
|
Loading…
Reference in New Issue