Vector Inputs (#29)

* Test ColVecs

* Test RowVecs

* Bump patch

* Update docs

* Remove redundant code

* Apply formatting suggestions

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Simplify RowVecs implementation

* Update src/bayesian_linear_regression.jl

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

* Add error test

* Apply suggestions from code review

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Project.toml

@@ -1,7 +1,7 @@
 name = "BayesianLinearRegressors"
 uuid = "f579363c-4606-5e5c-a623-c4549f609c4b"
 authors = ["Will Tebbutt <wt0881@my.bristol.ac.uk>"]
-version = "0.3.4"
+version = "0.3.5"
 
 [deps]
 AbstractGPs = "99985d1d-32ba-4be9-9821-2ec096f28918"

README.md

@@ -17,9 +17,15 @@ The interface sits at roughly the same level as that of [Distributions.jl](https
 
 ## Conventions
 
-A `BayesianLinearRegressor` in `D` dimensions works with data where:
-- inputs `X` should be a `D x N` matrix of `Real`s where each column is from one data point.
-- outputs `y` should be an `N`-vector of `Real`s, where each element is from one data point.
+`BayesianLinearRegressors` is consistent with `AbstractGPs`.
+Consequently, a `BayesianLinearRegressor` in `D` dimensions can work with the following input types:
+1. `ColVecs` -- a wrapper around an `D x N` matrix of `Real`s saying that each column should be interpreted as an input.
+2. `RowVecs`s -- a wrapper around an `N x D` matrix of `Real`s, saying that each row should be interpreted as an input.
+3. `Matrix{<:Real}` -- must be `D x N`. Prefer using `ColVecs` or `RowVecs` for the sake of being explicit.
+
+Consult the `Design` section of the [KernelFunctions.jl](https://juliagaussianprocesses.github.io/KernelFunctions.jl/dev/design/) docs for more info on these conventions.
+
+Outputs for a BayesianLinearRegressor should be an `AbstractVector{<:Real}` of length `N`.
 
 ## Example Usage
 
@@ -38,7 +44,7 @@ f = BayesianLinearRegressor(mw, Λw)
 
 # Index into the regressor and assume heterscedastic observation noise `Σ_noise`.
 N = 10
-X = collect(hcat(collect(range(-5.0, 5.0, length=N)), ones(N))')
+X = ColVecs(collect(hcat(collect(range(-5.0, 5.0, length=N)), ones(N))'))
 Σ_noise = Diagonal(exp.(randn(N)))
 fX = f(X, Σ_noise)
 
@@ -70,7 +76,7 @@ logpdf(f′(X, Σ_noise), y)
 
 # Sample from the posterior predictive distribution.
 N_plt = 1000
-X_plt = hcat(collect(range(-6.0, 6.0, length=N_plt)), ones(N_plt))'
+X_plt = ColVecs(hcat(collect(range(-6.0, 6.0, length=N_plt)), ones(N_plt))')
 f′X_plt = rand(rng, f′(X_plt, eps()), 100) # Samples with machine-epsilon noise for stability
 
 # Compute some posterior marginal statisics.

src/bayesian_linear_regression.jl

@@ -17,15 +17,28 @@ const FiniteBLR = FiniteGP{<:BayesianLinearRegressor}
 
 # All code below implements the primary + secondary AbstractGPs.jl APIs.
 
-AbstractGPs.mean(fx::FiniteBLR) = fx.x.X' * fx.f.mw
+x_as_colvecs(fx::FiniteBLR) = x_as_colvecs(fx.x)
+
+x_as_colvecs(x::ColVecs) = x
+
+x_as_colvecs(x::RowVecs) = ColVecs(x.X')
+
+function x_as_colvecs(x::T) where {T<:AbstractVector}
+    return error(
+        "$T is not a subtype of AbstractVector that is known. Please provide either a",
+        "ColVecs or RowVecs.",
+    )
+end
+
+AbstractGPs.mean(fx::FiniteBLR) = x_as_colvecs(fx).X' * fx.f.mw
 
 function AbstractGPs.cov(fx::FiniteBLR)
-    α = _cholesky(fx.f.Λw).U' \ fx.x.X
+    α = _cholesky(fx.f.Λw).U' \ x_as_colvecs(fx).X
     return Symmetric(α' * α + fx.Σy)
 end
 
 function AbstractGPs.var(fx::FiniteBLR)
-    α = _cholesky(fx.f.Λw).U' \ fx.x.X
+    α = _cholesky(fx.f.Λw).U' \ x_as_colvecs(fx).X
     return vec(sum(abs2, α; dims=1)) .+ diag(fx.Σy)
 end
 
@@ -34,8 +47,9 @@ AbstractGPs.mean_and_cov(fx::FiniteBLR) = (mean(fx), cov(fx))
 AbstractGPs.mean_and_var(fx::FiniteBLR) = (mean(fx), var(fx))
 
 function AbstractGPs.rand(rng::AbstractRNG, fx::FiniteBLR, samples::Int)
-    w = fx.f.mw .+ _cholesky(fx.f.Λw).U \ randn(rng, size(fx.x.X, 1), samples)
-    return fx.x.X' * w .+ _cholesky(fx.Σy).U' * randn(rng, size(fx.x.X, 2), samples)
+    X = x_as_colvecs(fx).X
+    w = fx.f.mw .+ _cholesky(fx.f.Λw).U \ randn(rng, size(X, 1), samples)
+    return X' * w .+ _cholesky(fx.Σy).U' * randn(rng, size(X, 2), samples)
 end
 
 function AbstractGPs.logpdf(fx::FiniteBLR, y::AbstractVector{<:Real})
@@ -56,9 +70,9 @@ end
 
 # Computation utilised in both `logpdf` and `posterior`.
 function __compute_inference_quantities(fx::FiniteBLR, y::AbstractVector{<:Real})
-    length(y) == size(fx.x.X, 2) || throw(error("length(y) != size(fx.x.X, 2)"))
+    X = x_as_colvecs(fx).X
+    length(y) == size(X, 2) || throw(error("length(y) != size(fx.x.X, 2)"))
     blr = fx.f
-    X = fx.x.X
     N = length(y)
 
     Uw = _cholesky(blr.Λw).U