--- a/src/seminorms.rs Thu Aug 29 00:00:00 2024 -0500
+++ b/src/seminorms.rs Tue Dec 31 09:25:45 2024 -0500
@@ -12,9 +12,11 @@
use alg_tools::bisection_tree::*;
use alg_tools::mapping::RealMapping;
use alg_tools::iter::{Mappable, FilterMapX};
-use alg_tools::linops::{Apply, Linear, BoundedLinear};
+use alg_tools::linops::{Mapping, Linear, BoundedLinear};
+use alg_tools::instance::Instance;
use alg_tools::nalgebra_support::ToNalgebraRealField;
-use crate::measures::{DiscreteMeasure, DeltaMeasure, SpikeIter};
+use alg_tools::norms::Linfinity;
+use crate::measures::{DiscreteMeasure, DeltaMeasure, SpikeIter, Radon, RNDM};
use nalgebra::DMatrix;
use std::marker::PhantomData;
use itertools::Itertools;
@@ -22,9 +24,12 @@
/// Abstraction for operators $𝒟 ∈ 𝕃(𝒵(Ω); C_c(Ω))$.
///
/// Here $𝒵(Ω) ⊂ ℳ(Ω)$ is the space of sums of delta measures, presented by [`DiscreteMeasure`].
-pub trait DiscreteMeasureOp<Domain, F> : BoundedLinear<DiscreteMeasure<Domain, F>, FloatType=F>
-where F : Float + ToNalgebraRealField,
- Domain : 'static {
+pub trait DiscreteMeasureOp<Domain, F>
+ : BoundedLinear<DiscreteMeasure<Domain, F>, Radon, Linfinity, F>
+where
+ F : Float + ToNalgebraRealField,
+ Domain : 'static + Clone + PartialEq,
+{
/// The output type of [`Self::preapply`].
type PreCodomain;
@@ -38,7 +43,7 @@
fn findim_matrix<'a, I>(&self, points : I) -> DMatrix<F::MixedType>
where I : ExactSizeIterator<Item=&'a Domain> + Clone;
- /// [`Apply::apply`] that typically returns an uninitialised [`PreBTFN`]
+ /// [`Mapping`] that typically returns an uninitialised [`PreBTFN`]
/// instead of a full [`BTFN`].
fn preapply(&self, μ : DiscreteMeasure<Domain, F>) -> Self::PreCodomain;
}
@@ -73,7 +78,7 @@
pub struct ConvolutionSupportGenerator<F : Float, K, const N : usize>
where K : SimpleConvolutionKernel<F, N> {
kernel : K,
- centres : DiscreteMeasure<Loc<F, N>, F>,
+ centres : RNDM<F, N>,
}
impl<F : Float, K, const N : usize> ConvolutionSupportGenerator<F, K, N>
@@ -130,9 +135,9 @@
where F : Float + ToNalgebraRealField,
BT : BTImpl<F, N, Data=usize>,
K : SimpleConvolutionKernel<F, N> {
- /// Depth of the [`BT`] bisection tree for the outputs [`Apply::apply`].
+ /// Depth of the [`BT`] bisection tree for the outputs [`Mapping::apply`].
depth : BT::Depth,
- /// Domain of the [`BT`] bisection tree for the outputs [`Apply::apply`].
+ /// Domain of the [`BT`] bisection tree for the outputs [`Mapping::apply`].
domain : Cube<F, N>,
/// The convolution kernel
kernel : K,
@@ -146,7 +151,7 @@
/// Creates a new convolution operator $𝒟$ with `kernel` on `domain`.
///
- /// The output of [`Apply::apply`] is a [`BT`] of given `depth`.
+ /// The output of [`Mapping::apply`] is a [`BT`] of given `depth`.
pub fn new(depth : BT::Depth, domain : Cube<F, N>, kernel : K) -> Self {
ConvolutionOp {
depth : depth,
@@ -157,7 +162,7 @@
}
/// Returns the support generator for this convolution operator.
- fn support_generator(&self, μ : DiscreteMeasure<Loc<F, N>, F>)
+ fn support_generator(&self, μ : RNDM<F, N>)
-> ConvolutionSupportGenerator<F, K, N> {
// TODO: can we avoid cloning μ?
@@ -173,94 +178,43 @@
}
}
-impl<F, K, BT, const N : usize> Apply<DiscreteMeasure<Loc<F, N>, F>>
+impl<F, K, BT, const N : usize> Mapping<RNDM<F, N>>
for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
- BT : BTImpl<F, N, Data=usize>,
- K : SimpleConvolutionKernel<F, N>,
- Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N> {
+where
+ F : Float + ToNalgebraRealField,
+ BT : BTImpl<F, N, Data=usize>,
+ K : SimpleConvolutionKernel<F, N>,
+ Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N>
+{
- type Output = BTFN<F, ConvolutionSupportGenerator<F, K, N>, BT, N>;
+ type Codomain = BTFN<F, ConvolutionSupportGenerator<F, K, N>, BT, N>;
- fn apply(&self, μ : DiscreteMeasure<Loc<F, N>, F>) -> Self::Output {
- let g = self.support_generator(μ);
+ fn apply<I>(&self, μ : I) -> Self::Codomain
+ where I : Instance<RNDM<F, N>> {
+ let g = self.support_generator(μ.own());
BTFN::construct(self.domain.clone(), self.depth, g)
}
}
-impl<'a, F, K, BT, const N : usize> Apply<&'a DiscreteMeasure<Loc<F, N>, F>>
+/// [`ConvolutionOp`]s as linear operators over [`DiscreteMeasure`]s.
+impl<F, K, BT, const N : usize> Linear<RNDM<F, N>>
+for ConvolutionOp<F, K, BT, N>
+where
+ F : Float + ToNalgebraRealField,
+ BT : BTImpl<F, N, Data=usize>,
+ K : SimpleConvolutionKernel<F, N>,
+ Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N>
+{ }
+
+impl<F, K, BT, const N : usize>
+BoundedLinear<RNDM<F, N>, Radon, Linfinity, F>
for ConvolutionOp<F, K, BT, N>
where F : Float + ToNalgebraRealField,
BT : BTImpl<F, N, Data=usize>,
K : SimpleConvolutionKernel<F, N>,
Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N> {
- type Output = BTFN<F, ConvolutionSupportGenerator<F, K, N>, BT, N>;
-
- fn apply(&self, μ : &'a DiscreteMeasure<Loc<F, N>, F>) -> Self::Output {
- self.apply(μ.clone())
- }
-}
-
-/// [`ConvolutionOp`]s as linear operators over [`DiscreteMeasure`]s.
-impl<F, K, BT, const N : usize> Linear<DiscreteMeasure<Loc<F, N>, F>>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
- BT : BTImpl<F, N, Data=usize>,
- K : SimpleConvolutionKernel<F, N>,
- Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N> {
- type Codomain = BTFN<F, ConvolutionSupportGenerator<F, K, N>, BT, N>;
-}
-
-impl<F, K, BT, const N : usize> Apply<DeltaMeasure<Loc<F, N>, F>>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
- BT : BTImpl<F, N, Data=usize>,
- K : SimpleConvolutionKernel<F, N> {
-
- type Output = Weighted<Shift<K, F, N>, F>;
-
- #[inline]
- fn apply(&self, δ : DeltaMeasure<Loc<F, N>, F>) -> Self::Output {
- self.kernel.clone().shift(δ.x).weigh(δ.α)
- }
-}
-
-impl<'a, F, K, BT, const N : usize> Apply<&'a DeltaMeasure<Loc<F, N>, F>>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
- BT : BTImpl<F, N, Data=usize>,
- K : SimpleConvolutionKernel<F, N> {
-
- type Output = Weighted<Shift<K, F, N>, F>;
-
- #[inline]
- fn apply(&self, δ : &'a DeltaMeasure<Loc<F, N>, F>) -> Self::Output {
- self.kernel.clone().shift(δ.x).weigh(δ.α)
- }
-}
-
-/// [`ConvolutionOp`]s as linear operators over [`DeltaMeasure`]s.
-///
-/// The codomain is different from the implementation for [`DiscreteMeasure`].
-impl<F, K, BT, const N : usize> Linear<DeltaMeasure<Loc<F, N>, F>>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
- BT : BTImpl<F, N, Data=usize>,
- K : SimpleConvolutionKernel<F, N> {
- type Codomain = Weighted<Shift<K, F, N>, F>;
-}
-
-impl<F, K, BT, const N : usize> BoundedLinear<DiscreteMeasure<Loc<F, N>, F>>
-for ConvolutionOp<F, K, BT, N>
-where F : Float + ToNalgebraRealField,
- BT : BTImpl<F, N, Data=usize>,
- K : SimpleConvolutionKernel<F, N>,
- Weighted<Shift<K, F, N>, F> : LocalAnalysis<F, BT::Agg, N> {
-
- type FloatType = F;
-
- fn opnorm_bound(&self) -> F {
+ fn opnorm_bound(&self, _ : Radon, _ : Linfinity) -> F {
// With μ = ∑_i α_i δ_{x_i}, we have
// |𝒟μ|_∞
// = sup_z |∑_i α_i φ(z - x_i)|
@@ -292,10 +246,10 @@
DMatrix::from_iterator(n, n, values)
}
- /// A version of [`Apply::apply`] that does not instantiate the [`BTFN`] codomain with
+ /// A version of [`Mapping::apply`] that does not instantiate the [`BTFN`] codomain with
/// a bisection tree, instead returning a [`PreBTFN`]. This can improve performance when
/// the output is to be added as the right-hand-side operand to a proper BTFN.
- fn preapply(&self, μ : DiscreteMeasure<Loc<F, N>, F>) -> Self::PreCodomain {
+ fn preapply(&self, μ : RNDM<F, N>) -> Self::PreCodomain {
BTFN::new_pre(self.support_generator(μ))
}
}