Merge branch 'master-dev'

Author: theogf

.travis.yml

@@ -0,0 +1,23 @@
+## Documentation: http://docs.travis-ci.com/user/languages/julia/
+language: julia
+os:
+  - linux
+  - osx
+julia:
+  - 1.0
+  - nightly
+notifications:
+  email: false
+after_success:
+  # push coverage results to Coveralls
+  - julia -e 'using Pkg; Pkg.add("Coverage"); using Coverage; Coveralls.submit(process_folder())'
+jobs:
+  include:
+    - stage: "Documentation"
+      julia: 1.0
+      os: linux
+      script:
+        - export DOCUMENTER_DEBUG=true
+        - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
+        - julia --project=docs/ docs/make.jl
+      after_succes: skip

Manifest.toml

@@ -5,3 +5,4 @@ version = "0.1.0"
 [deps]
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

Project.toml

@@ -1,3 +1,4 @@
+[![Build Status](https://travis-ci.org/theogf/KernelFunctions.jl.svg?branch=master)](https://travis-ci.org/theogf/AugmentedGaussianProcesses.jl)
 # KernelFunctions.jl (WIP)
 Julia Package for kernel functions for machine learning
 
@@ -9,4 +10,4 @@ Julia Package for kernel functions for machine learning
 - BLAS backend
 
 
-Directly inspired by the [MLKernels](https://github.com/trthatcher/MLKernels.jl)
+Directly inspired by the [MLKernels](https://github.com/trthatcher/MLKernels.jl) package

README.md

@@ -70,7 +70,7 @@ function kernel(
         obsdim::Int = defaultobs
     ) where {T,T₁<:Real,T₂<:Real}
     # TODO Verify dimensions
-    _kappamatrix!(κ, pairwise(metric(κ),X,Y,dims=obsdim))
+    kappa(κ, evaluate(metric(κ),x,y))
 end
 
 """
@@ -85,7 +85,9 @@ function kernelmatrix(
         obsdim::Int = defaultobs,
         symmetrize::Bool = true
     ) where {T,T₁<:Real}
-    return kernelmatrix!(Matrix{promote_float(T,T₁)}(undef,size(X,obsdim),size(X,obsdim)),κ,X,obsdim=obsdim,symmetrize=symmetrize)
+    Tₛ = typeof(zero(eltype(X))*zero(T))
+    m = size(X,obsdim)
+    return kernelmatrix!(Matrix{promote_float(T,T₁)}(undef,m,m),κ,X,obsdim=obsdim,symmetrize=symmetrize)
 end
 
 """
@@ -100,7 +102,10 @@ function kernelmatrix(
         Y::AbstractMatrix{T₂};
         obsdim=defaultobs
     ) where {T,T₁<:Real,T₂<:Real}
-    kernelmatrix!(Matrix{promote_float(T,T₁,T₂)}(undef,size(X,obsdim),size(Y,obsdim)),κ,X,Y,obsdim=obsdim)
+    Tₛ = typeof(zero(eltype(X))*zero(eltype(Y))*zero(T))
+    m = size(X,obsdim)
+    n = size(Y,obsdim)
+    kernelmatrix!(Matrix{Tₛ}(undef,m,n),κ,X,Y,obsdim=obsdim)
 end
 
 

src/kernelmatrix.jl

@@ -5,13 +5,13 @@ B = rand(dims...)
 K = [zeros(dims[1],dims[1]),zeros(dims[2],dims[2])]
 k = SquaredExponentialKernel()
 
-@testset "Inplace kernelmatrix" begin
+@testset "Inplace Kernel Matrix" begin
     for obsdim in [1,2]
         @test kernelmatrix!(K[obsdim],k,A,B,obsdim=obsdim) == kernelmatrix(k,A,B,obsdim=obsdim)
     end
 end
 
-@testset "Kernal matrix" begin
+@testset "Kernel matrix" begin
     for obsdim in [1,2]
         @test kernelmatrix(k,A,B,obsdim=obsdim) == kappa.([k],pairwise(KernelFunctions.metric(k),A,B,dims=obsdim))
     end