Merge branch 'master' into syntacticsugarallkernels

Author: theogf

Project.toml

@@ -18,7 +18,7 @@ Compat = "2.2, 3"
 Distances = "0.8"
 Requires = "1.0.1"
 SpecialFunctions = "0.8, 0.9, 0.10"
-StatsBase = "0.32"
+StatsBase = "0.32, 0.33"
 StatsFuns = "0.8, 0.9"
 ZygoteRules = "0.2"
 julia = "1.0"

README.md

@@ -1,7 +1,10 @@
-[![Build Status](https://travis-ci.org/theogf/KernelFunctions.jl.svg?branch=master)](https://travis-ci.org/theogf/KernelFunctions.jl)
-[![Coverage Status](https://coveralls.io/repos/github/theogf/KernelFunctions.jl/badge.svg?branch=master)](https://coveralls.io/github/theogf/KernelFunctions.jl?branch=master)
-[![Documentation](https://img.shields.io/badge/docs-dev-blue.svg)](https://theogf.github.io/KernelFunctions.jl/dev/)
 # KernelFunctions.jl
+
+[![Build Status](https://travis-ci.com/JuliaGaussianProcesses/KernelFunctions.jl.svg?branch=master)](https://travis-ci.com/JuliaGaussianProcesses/KernelFunctions.jl)
+[![Coverage Status](https://coveralls.io/repos/github/JuliaGaussianProcesses/KernelFunctions.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaGaussianProcesses/KernelFunctions.jl?branch=master)
+[![Documentation (stable)](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliagaussianprocesses.github.io/KernelFunctions.jl/stable)
+[![Documentation (latest)](https://img.shields.io/badge/docs-dev-blue.svg)](https://juliagaussianprocesses.github.io/KernelFunctions.jl/dev)
+
 ## Kernel functions for machine learning
 
 KernelFunctions.jl provide a flexible and complete framework for kernel functions, pretransforming the input data.
@@ -43,4 +46,4 @@ Directly inspired by the [MLKernels](https://github.com/trthatcher/MLKernels.jl)
 
 ## Issues/Contributing
 
-If you notice a problem or would like to contribute by adding more kernel functions or features please [submit an issue](https://github.com/theogf/KernelFunctions.jl/issues).
+If you notice a problem or would like to contribute by adding more kernel functions or features please [submit an issue](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/issues).

docs/make.jl

@@ -21,6 +21,6 @@ makedocs(
 # for more information.
 deploydocs(
     deps = Deps.pip("mkdocs", "python-markdown-math"),
-    repo = "github.com/theogf/KernelFunctions.jl.git",
+    repo = "github.com/JuliaGaussianProcesses/KernelFunctions.jl.git",
     target = "build"
 )

src/KernelFunctions.jl

@@ -1,11 +1,12 @@
 """
-KernelFunctions. [Github](https://github.com/theogf/KernelFunctions.jl) [Documentation](https://theogf.github.io/KernelFunctions.jl/dev/)
+KernelFunctions. [Github](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl)
+[Documentation](https://juliagaussianprocesses.github.io/KernelFunctions.jl/stable/)
 """
 module KernelFunctions
 
 export kernelmatrix, kernelmatrix!, kerneldiagmatrix, kerneldiagmatrix!, kappa
 export transform
-export params, duplicate, set! # Helpers
+export duplicate, set! # Helpers
 
 export Kernel
 export ConstantKernel, WhiteKernel, EyeKernel, ZeroKernel
@@ -45,7 +46,7 @@ include("distances/dotproduct.jl")
 include("distances/delta.jl")
 include("transform/transform.jl")
 
-for k in ["exponential","matern","polynomial","constant","rationalquad","exponentiated","cosine","maha"]
+for k in ["exponential","matern","polynomial","constant","rationalquad","exponentiated","cosine","maha","fbm"]
     include(joinpath("kernels",k*".jl"))
 end
 include("kernels/transformedkernel.jl")

src/generic.jl

@@ -15,13 +15,11 @@ printshifted(io::IO,κ::Kernel,shift::Int) = print(io,"$κ")
 Base.show(io::IO,κ::Kernel) = print(io,nameof(typeof(κ)))
 
 ### Syntactic sugar for creating matrices and using kernel functions
-
 function concretetypes(k, ktypes::Vector)
     isempty(subtypes(k)) ? push!(ktypes, k) : concretetypes.(subtypes(k), Ref(ktypes))
     return ktypes
 end
 
-
 for k in concretetypes(Kernel, [])
     @eval begin
         @inline (κ::$k)(x::AbstractVector{<:Real}, y::AbstractVector{<:Real}) = kappa(κ, x, y)

src/kernels/fbm.jl

@@ -0,0 +1,92 @@
+"""
+    FBMKernel(; h::Real=0.5)
+
+Fractional Brownian motion kernel with Hurst index h from (0,1) given by
+```
+    κ(x,y) =  ( |x|²ʰ + |y|²ʰ - |x-y|²ʰ ) / 2
+```
+
+For h=1/2, this is the Wiener Kernel, for h>1/2, the increments are
+positively correlated and for h<1/2 the increments are negatively correlated.
+"""
+struct FBMKernel{T<:Real} <: BaseKernel
+    h::T
+    function FBMKernel(;h::T=0.5) where {T<:Real}
+        @assert h<=1.0 && h>=0.0 "FBMKernel: Given Hurst index h is invalid."
+        return new{T}(h)
+    end
+end
+
+_fbm(modX, modY, modXY, h) = (modX^h + modY^h - modXY^h)/2
+
+function kernelmatrix(κ::FBMKernel, X::AbstractMatrix; obsdim::Int = defaultobs)
+    @assert obsdim ∈ [1,2] "obsdim should be 1 or 2 (see docs of kernelmatrix))"
+    modX = sum(abs2, X; dims = 3 - obsdim)
+    modXX = pairwise(SqEuclidean(), X, dims = obsdim)
+    return _fbm.(vec(modX), reshape(modX, 1, :), modXX, κ.h)
+end
+
+function kernelmatrix!(K::AbstractMatrix, κ::FBMKernel, X::AbstractMatrix; obsdim::Int = defaultobs)
+    @assert obsdim ∈ [1,2] "obsdim should be 1 or 2 (see docs of kernelmatrix))"
+    modX = sum(abs2, X; dims = 3 - obsdim)
+    modXX = pairwise(SqEuclidean(), X, dims = obsdim)
+    K .= _fbm.(vec(modX), reshape(modX, 1, :), modXX, κ.h)
+    return K
+end
+
+function kernelmatrix(
+    κ::FBMKernel,
+    X::AbstractMatrix,
+    Y::AbstractMatrix;
+    obsdim::Int = defaultobs,
+)
+    @assert obsdim ∈ [1,2] "obsdim should be 1 or 2 (see docs of kernelmatrix))"   
+    modX = sum(abs2, X, dims=3-obsdim)
+    modY = sum(abs2, Y, dims=3-obsdim)
+    modXY = pairwise(SqEuclidean(), X, Y,dims=obsdim)
+    return _fbm.(vec(modX), reshape(modY, 1, :), modXY, κ.h)
+end
+
+function kernelmatrix!(
+    K::AbstractMatrix,
+    κ::FBMKernel,
+    X::AbstractMatrix,
+    Y::AbstractMatrix;
+    obsdim::Int = defaultobs,
+)
+    @assert obsdim ∈ [1,2] "obsdim should be 1 or 2 (see docs of kernelmatrix))"   
+    modX = sum(abs2, X, dims=3-obsdim)
+    modY = sum(abs2, Y, dims=3-obsdim)
+    modXY = pairwise(SqEuclidean(), X, Y,dims=obsdim)
+    K .= _fbm.(vec(modX), reshape(modY, 1, :), modXY, κ.h)
+    return K
+end
+
+## Apply kernel on two vectors ##
+function _kernel(
+        κ::FBMKernel,
+        x::AbstractVector,
+        y::AbstractVector;
+        obsdim::Int = defaultobs
+    )
+    @assert length(x) == length(y) "x and y don't have the same dimension!"
+    return κ(x,y)
+end
+
+#Syntactic Sugar
+function (κ::FBMKernel)(x::AbstractVector{<:Real}, y::AbstractVector{<:Real})
+    modX = sum(abs2, x)
+    modY = sum(abs2, y)
+    modXY = sqeuclidean(x, y)
+    return (modX^κ.h + modY^κ.h - modXY^κ.h)/2
+end
+
+(κ::FBMKernel)(x::Real, y::Real) = (abs2(x)^κ.h + abs2(y)^κ.h - abs2(x-y)^κ.h)/2
+
+function (κ::FBMKernel)(X::AbstractMatrix{<:Real}, Y::AbstractMatrix{<:Real}; obsdim::Integer=defaultobs)
+    return kernelmatrix(κ, X, Y, obsdim=obsdim)
+end
+
+function (κ::FBMKernel)(X::AbstractMatrix{<:Real}; obsdim::Integer=defaultobs)
+    return kernelmatrix(κ, X, obsdim=obsdim)
+end

src/matrix/kernelmatrix.jl

@@ -9,7 +9,7 @@ kernelmatrix!
 
 
 function kernelmatrix!(
-        K::Matrix,
+        K::AbstractMatrix,
         κ::Kernel,
         X::AbstractMatrix;
         obsdim::Int = defaultobs

test/test_kernels.jl

@@ -29,6 +29,21 @@ id = IdentityTransform();
             @test kappa(k, 0.5) == c
         end
     end
+    @testset "FBM" begin
+        k = FBMKernel(h=0.3)
+        @test k(v1,v2) ≈ (sqeuclidean(v1, zero(v1))^0.3 + sqeuclidean(v2, zero(v2))^0.3 - sqeuclidean(v1-v2, zero(v1-v2))^0.3)/2 atol=1e-5
+        
+        # kernelmatrix tests
+        m1 = rand(3,3)
+        m2 = rand(3,3)
+        @test kernelmatrix(k, m1, m1) ≈ kernelmatrix(k, m1) atol=1e-5
+        @test kernelmatrix(k, m1, m2) ≈ k(m1, m2) atol=1e-5
+
+        
+        x1 = rand()
+        x2 = rand()
+        @test kernelmatrix(k, x1*ones(1,1), x2*ones(1,1))[1] ≈ k(x1, x2) atol=1e-5
+    end
     @testset "Cosine" begin
         k = CosineKernel()
         @test eltype(k) == Any