src/seminorms.rs

/*! This module implements the convolution operators $𝒟$.

The principal data type of the module is [`ConvolutionOp`] and the main abstraction
the trait [`DiscreteMeasureOp`].
*/

use std::iter::Zip;
use std::ops::RangeFrom;
use alg_tools::types::*;
use alg_tools::loc::Loc;
use alg_tools::sets::Cube;
use alg_tools::bisection_tree::*;
use alg_tools::mapping::RealMapping;
use alg_tools::iter::{Mappable, FilterMapX};
use alg_tools::linops::{Mapping, Linear, BoundedLinear};
use alg_tools::instance::Instance;
use alg_tools::nalgebra_support::ToNalgebraRealField;
use alg_tools::norms::Linfinity;
use crate::measures::{DiscreteMeasure, DeltaMeasure, SpikeIter, Radon, RNDM};
use nalgebra::DMatrix;
use std::marker::PhantomData;
use itertools::Itertools;

/// 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>, Radon, Linfinity, F>
where
    F : Float + ToNalgebraRealField,
    Domain : 'static + Clone + PartialEq,
{
    /// The output type of [`Self::preapply`].
    type PreCodomain;

    /// Creates a finite-dimensional presentatin of the operator restricted to a fixed support.
    ///
    /// <p>
    /// This returns the matrix $C_*𝒟C$, where $C ∈ 𝕃(ℝ^n; 𝒵(Ω))$, $Ca = ∑_{i=1}^n α_i δ_{x_i}$
    /// for a $x_1, …, x_n$ the coordinates given by the iterator `I`, and $a=(α_1,…,α_n)$.
    /// Here $C_* ∈ 𝕃(C_c(Ω); ℝ^n) $ stands for the preadjoint.
    /// </p>
    fn findim_matrix<'a, I>(&self, points : I) -> DMatrix<F::MixedType>
    where I : ExactSizeIterator<Item=&'a Domain> + Clone;

    /// [`Mapping`] that typically returns an uninitialised [`PreBTFN`]
    /// instead of a full [`BTFN`].
    fn preapply(&self, μ : DiscreteMeasure<Domain, F>) -> Self::PreCodomain;
}

// Blanket implementation of a measure as a linear functional over a predual
// (that by assumption is a linear functional over a measure).
/*impl<F, Domain, Predual> Linear<Predual>
for DiscreteMeasure<Domain, F>
where F : Float + ToNalgebraRealField,
      Predual : Linear<DiscreteMeasure<Domain, F>, Codomain=F> {
    type Codomain = F;

    #[inline]
    fn apply(&self, ω : &Predual) -> F {
        ω.apply(self)
    }
}*/

//
// Convolutions for discrete measures
//

/// A trait alias for simple convolution kernels.
pub trait SimpleConvolutionKernel<F : Float, const N : usize>
: RealMapping<F, N> + Support<F, N> + Bounded<F> + Clone + 'static {}

impl<T, F : Float, const N : usize> SimpleConvolutionKernel<F, N> for T
where T : RealMapping<F, N> + Support<F, N> + Bounded<F> + Clone + 'static {}

/// [`SupportGenerator`] for [`ConvolutionOp`].
#[derive(Clone,Debug)]
pub struct ConvolutionSupportGenerator<F : Float, K, const N : usize>
where K : SimpleConvolutionKernel<F, N> {
    kernel : K,
    centres : RNDM<F, N>,
}

impl<F : Float, K, const N : usize> ConvolutionSupportGenerator<F, K, N>
where K : SimpleConvolutionKernel<F, N> {

    /// Construct the convolution kernel corresponding to `δ`, i.e., one centered at `δ.x` and
    /// weighted by `δ.α`.
    #[inline]
    fn construct_kernel<'a>(&'a self, δ : &'a DeltaMeasure<Loc<F, N>, F>)
    -> Weighted<Shift<K, F, N>, F> {
        self.kernel.clone().shift(δ.x).weigh(δ.α)
    }

    /// This is a helper method for the implementation of [`ConvolutionSupportGenerator::all_data`].
    /// It filters out `δ` with zero weight, and otherwise returns the corresponding convolution
    /// kernel. The `id` is passed through as-is.
    #[inline]
    fn construct_kernel_and_id_filtered<'a>(
        &'a self,
        (id, δ) : (usize, &'a DeltaMeasure<Loc<F, N>, F>)
    ) -> Option<(usize, Weighted<Shift<K, F, N>, F>)> {
        (δ.α != F::ZERO).then(|| (id.into(), self.construct_kernel(δ)))
    }
}

impl<F : Float, K, const N : usize> SupportGenerator<F, N>
for ConvolutionSupportGenerator<F, K, N>
where K : SimpleConvolutionKernel<F, N> {
    type Id = usize;
    type SupportType = Weighted<Shift<K, F, N>, F>;
    type AllDataIter<'a> = FilterMapX<'a, Zip<RangeFrom<usize>, SpikeIter<'a, Loc<F, N>, F>>,
                                      Self, (Self::Id, Self::SupportType)>;

    #[inline]
    fn support_for(&self, d : Self::Id) -> Self::SupportType {
        self.construct_kernel(&self.centres[d])
    }

    #[inline]
    fn support_count(&self) -> usize {
        self.centres.len()
    }

    #[inline]
    fn all_data(&self) -> Self::AllDataIter<'_> {
        (0..).zip(self.centres.iter_spikes())
             .filter_mapX(self, Self::construct_kernel_and_id_filtered)
    }
}

/// Representation of a convolution operator $𝒟$.
#[derive(Clone,Debug)]
pub struct ConvolutionOp<F, K, BT, const N : usize>
where F : Float + ToNalgebraRealField,
      BT : BTImpl<F, N, Data=usize>,
      K : SimpleConvolutionKernel<F, N> {
    /// Depth of the [`BT`] bisection tree for the outputs [`Mapping::apply`].
    depth : BT::Depth,
    /// Domain of the [`BT`] bisection tree for the outputs [`Mapping::apply`].
    domain : Cube<F, N>,
    /// The convolution kernel
    kernel : K,
    _phantoms : PhantomData<(F,BT)>,
}

impl<F, K, BT, const N : usize> ConvolutionOp<F, K, BT, N>
where F : Float + ToNalgebraRealField,
      BT : BTImpl<F, N, Data=usize>,
      K : SimpleConvolutionKernel<F, N> {

    /// Creates a new convolution operator $𝒟$ with `kernel` on `domain`.
    ///
    /// 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,
            domain : domain,
            kernel : kernel,
            _phantoms : PhantomData
        }
    }

    /// Returns the support generator for this convolution operator.
    fn support_generator(&self, μ : RNDM<F, N>)
    -> ConvolutionSupportGenerator<F, K, N> {

        // TODO: can we avoid cloning μ?
        ConvolutionSupportGenerator {
            kernel : self.kernel.clone(),
            centres : μ
        }
    }

    /// Returns a reference to the kernel of this convolution operator.
    pub fn kernel(&self) -> &K {
        &self.kernel
    }
}

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>
{

    type Codomain = BTFN<F, ConvolutionSupportGenerator<F, K, N>, BT, N>;

    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)
    }
}

/// [`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> {

    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)|
        // ≤ sup_z ∑_i |α_i| |φ(z - x_i)|
        // ≤ ∑_i |α_i| |φ|_∞
        // = |μ|_ℳ |φ|_∞
        self.kernel.bounds().uniform()
    }
}


impl<F, K, BT, const N : usize> DiscreteMeasureOp<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 PreCodomain = PreBTFN<F, ConvolutionSupportGenerator<F, K, N>, N>;

    fn findim_matrix<'a, I>(&self, points : I) -> DMatrix<F::MixedType>
    where I : ExactSizeIterator<Item=&'a Loc<F,N>> + Clone {
        // TODO: Preliminary implementation. It be best to use sparse matrices or
        // possibly explicit operators without matrices
        let n = points.len();
        let points_clone = points.clone();
        let pairs = points.cartesian_product(points_clone);
        let kernel = &self.kernel;
        let values = pairs.map(|(x, y)| kernel.apply(y-x).to_nalgebra_mixed());
        DMatrix::from_iterator(n, n, values)
    }

    /// 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 : RNDM<F, N>) -> Self::PreCodomain {
        BTFN::new_pre(self.support_generator(μ))
    }
}

/// Generates an scalar operation (e.g. [`std::ops::Mul`], [`std::ops::Div`])
/// for [`ConvolutionSupportGenerator`].
macro_rules! make_convolutionsupportgenerator_scalarop_rhs {
    ($trait:ident, $fn:ident, $trait_assign:ident, $fn_assign:ident) => {
        impl<F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
        std::ops::$trait_assign<F>
        for ConvolutionSupportGenerator<F, K, N> {
            fn $fn_assign(&mut self, t : F) {
                self.centres.$fn_assign(t);
            }
        }

        impl<F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
        std::ops::$trait<F>
        for ConvolutionSupportGenerator<F, K, N> {
            type Output = ConvolutionSupportGenerator<F, K, N>;
            fn $fn(mut self, t : F) -> Self::Output {
                std::ops::$trait_assign::$fn_assign(&mut self.centres, t);
                self
            }
        }
        impl<'a, F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
        std::ops::$trait<F>
        for &'a ConvolutionSupportGenerator<F, K, N> {
            type Output = ConvolutionSupportGenerator<F, K, N>;
            fn $fn(self, t : F) -> Self::Output {
                ConvolutionSupportGenerator{
                    kernel : self.kernel.clone(),
                    centres : (&self.centres).$fn(t),
                }
            }
        }
    }
}

make_convolutionsupportgenerator_scalarop_rhs!(Mul, mul, MulAssign, mul_assign);
make_convolutionsupportgenerator_scalarop_rhs!(Div, div, DivAssign, div_assign);


/// Generates an unary operation (e.g. [`std::ops::Neg`]) for [`ConvolutionSupportGenerator`].
macro_rules! make_convolutionsupportgenerator_unaryop {
    ($trait:ident, $fn:ident) => {
        impl<F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
        std::ops::$trait
        for ConvolutionSupportGenerator<F, K, N> {
            type Output = ConvolutionSupportGenerator<F, K, N>;
            fn $fn(mut self) -> Self::Output {
                self.centres = self.centres.$fn();
                self
            }
        }

        impl<'a, F : Float, K : SimpleConvolutionKernel<F, N>, const N : usize>
        std::ops::$trait
        for &'a ConvolutionSupportGenerator<F, K, N> {
            type Output = ConvolutionSupportGenerator<F, K, N>;
            fn $fn(self) -> Self::Output {
                ConvolutionSupportGenerator{
                    kernel : self.kernel.clone(),
                    centres : (&self.centres).$fn(),
                }
            }
        }
    }
}

make_convolutionsupportgenerator_unaryop!(Neg, neg);