From 625b72237a342c8d3bf60ec05541f8cb4a78faff Mon Sep 17 00:00:00 2001
From: agerlach <599421+agerlach@users.noreply.github.com>
Date: Sat, 9 Oct 2021 15:18:06 -0400
Subject: [PATCH] Add `minimum`, `maximum`, `extrema` for `AbstractMvNormal`
 and `Product` (#1319)

* adds minimum, maximum, extrema to AbstractMvNormal and Product distributions

* add docstrings and update docs

* update docstrrings

* add isless tests for extrema

* clean-up, remove broadcast in favor of map

* move/update minimum/maximum fallback def/docstring

Co-authored-by: Adam R Gerlach <adam.gerlach.1@afresearchlab.com>
---
 docs/src/multivariate.md     |  4 ++++
 src/common.jl                | 20 ++++++++++++++++++++
 src/multivariate/mvnormal.jl |  2 ++
 src/multivariate/product.jl  |  2 ++
 src/univariates.jl           | 21 ---------------------
 test/mvnormal.jl             |  4 ++++
 test/product.jl              |  6 +++++-
 7 files changed, 37 insertions(+), 22 deletions(-)

diff --git a/docs/src/multivariate.md b/docs/src/multivariate.md
index c1edc18cb4..be4f5427a6 100644
--- a/docs/src/multivariate.md
+++ b/docs/src/multivariate.md
@@ -71,8 +71,12 @@ invcov(::Distributions.AbstractMvNormal)
 logdetcov(::Distributions.AbstractMvNormal)
 sqmahal(::Distributions.AbstractMvNormal, ::AbstractArray)
 rand(::AbstractRNG, ::Distributions.AbstractMvNormal)
+minimum(::Distributions.AbstractMvNormal)
+maximum(::Distributions.AbstractMvNormal)
+extrema(::Distributions.AbstractMvNormal)
 ```
 
+
 ### MvLogNormal
 
 In addition to the methods listed in the common interface above, we also provide the following methods:
diff --git a/src/common.jl b/src/common.jl
index 36c19a5bc3..f2f1798354 100644
--- a/src/common.jl
+++ b/src/common.jl
@@ -138,6 +138,26 @@ value_support(::Type{<:Distribution{VF,VS}}) where {VF,VS} = VS
 # to be decided: how to handle multivariate/matrixvariate distributions?
 Broadcast.broadcastable(d::UnivariateDistribution) = Ref(d)
 
+"""
+    minimum(d::Distribution)
+
+Return the minimum of the support of `d`.
+"""
+minimum(d::Distribution)
+
+"""
+    maximum(d::Distribution)
+
+Return the maximum of the support of `d`.
+"""
+maximum(d::Distribution)
+
+"""
+    extrema(d::Distribution)
+
+Return the minimum and maximum of the support of `d` as a 2-tuple.
+"""
+Base.extrema(d::Distribution) = minimum(d), maximum(d)
 
 ## TODO: the following types need to be improved
 abstract type SufficientStats end
diff --git a/src/multivariate/mvnormal.jl b/src/multivariate/mvnormal.jl
index 6e6c8cad0a..3827af61ac 100644
--- a/src/multivariate/mvnormal.jl
+++ b/src/multivariate/mvnormal.jl
@@ -80,6 +80,8 @@ abstract type AbstractMvNormal <: ContinuousMultivariateDistribution end
 insupport(d::AbstractMvNormal, x::AbstractVector) =
     length(d) == length(x) && all(isfinite, x)
 
+minimum(d::AbstractMvNormal) = fill(eltype(d)(-Inf), length(d))
+maximum(d::AbstractMvNormal) = fill(eltype(d)(Inf), length(d))
 mode(d::AbstractMvNormal) = mean(d)
 modes(d::AbstractMvNormal) = [mean(d)]
 
diff --git a/src/multivariate/product.jl b/src/multivariate/product.jl
index 454ffada3d..cee21c55d7 100644
--- a/src/multivariate/product.jl
+++ b/src/multivariate/product.jl
@@ -40,6 +40,8 @@ var(d::Product) = var.(d.v)
 cov(d::Product) = Diagonal(var(d))
 entropy(d::Product) = sum(entropy, d.v)
 insupport(d::Product, x::AbstractVector) = all(insupport.(d.v, x))
+minimum(d::Product) = map(minimum, d.v)
+maximum(d::Product) = map(maximum, d.v)
 
 """
     product_distribution(dists::AbstractVector{<:UnivariateDistribution})
diff --git a/src/univariates.jl b/src/univariates.jl
index 73c6eddff9..c31bc9a464 100644
--- a/src/univariates.jl
+++ b/src/univariates.jl
@@ -77,27 +77,6 @@ Get the degrees of freedom.
 """
 dof(d::UnivariateDistribution)
 
-"""
-    minimum(d::UnivariateDistribution)
-
-Return the minimum of the support of `d`.
-"""
-minimum(d::UnivariateDistribution)
-
-"""
-    maximum(d::UnivariateDistribution)
-
-Return the maximum of the support of `d`.
-"""
-maximum(d::UnivariateDistribution)
-
-"""
-    extrema(d::UnivariateDistribution)
-
-Return the minimum and maximum of the support of `d` as a 2-tuple.
-"""
-extrema(d::UnivariateDistribution) = (minimum(d), maximum(d))
-
 """
     insupport(d::UnivariateDistribution, x::Any)
 
diff --git a/test/mvnormal.jl b/test/mvnormal.jl
index bb24c9e213..934ec29115 100644
--- a/test/mvnormal.jl
+++ b/test/mvnormal.jl
@@ -30,6 +30,10 @@ function test_mvnormal(g::AbstractMvNormal, n_tsamples::Int=10^6,
     vs = diag(Σ)
     @test g == typeof(g)(params(g)...)
     @test g == deepcopy(g)
+    @test minimum(g) == fill(-Inf, d)
+    @test maximum(g) == fill(Inf, d)
+    @test extrema(g) == (minimum(g), maximum(g))
+    @test isless(extrema(g)...)
 
     # test sampling for AbstractMatrix (here, a SubArray):
     if ismissing(rng)
diff --git a/test/product.jl b/test/product.jl
index 3dc0e8de36..44c3b83c4f 100644
--- a/test/product.jl
+++ b/test/product.jl
@@ -29,7 +29,7 @@ end
     N = 11
     # Construct independent distributions and `Product` distribution from these.
     ubound = rand(N)
-    ds = Uniform.(0.0, ubound)
+    ds = Uniform.(-ubound, ubound)
     x = rand.(ds)
     d_product = product_distribution(ds)
     @test d_product isa Product
@@ -43,6 +43,10 @@ end
     @test entropy(d_product) == sum(entropy.(ds))
     @test insupport(d_product, ubound) == true
     @test insupport(d_product, ubound .+ 1) == false
+    @test minimum(d_product) == -ubound
+    @test maximum(d_product) == ubound
+    @test extrema(d_product) == (-ubound, ubound)
+    @test isless(extrema(d_product)...)
 
     y = rand(d_product)
     @test y isa typeof(x)