Merge pull request #19 from JuliaPOMDP/uniform

added a uniform distribution over a collection of objects

docs/src/distributions.md

@@ -23,3 +23,10 @@ BoolDistribution
 ```@docs
 Deterministic
 ```
+
+## Uniform
+
+```@docs
+Uniform
+UnsafeUniform
+```

src/POMDPModelTools.jl

@@ -72,6 +72,11 @@ export
     Deterministic
 include("distributions/deterministic.jl")
 
+export
+    Uniform,
+    UnsafeUniform
+include("distributions/uniform.jl")
+
 # convenient implementations
 include("convenient_implementations.jl")
 

src/distributions/uniform.jl

@@ -0,0 +1,62 @@
+struct Uniform{T<:AbstractSet}
+    set::T
+end
+
+"""
+    Uniform(collection)
+
+Create a uniform categorical distribution over a collection of objects.
+
+The objects in the collection must be unique (this is tested on construction), and will be stored in a `Set`. To avoid this overhead, use `UnsafeUniform`.
+"""
+function Uniform(c)
+    set = Set(c)
+    if length(c) > length(set)
+        error("""
+              Error constructing Uniform($c).
+
+              Objects must be unique (that is, length(Set(c)) == length(c)).
+              """
+             )
+    end
+    return Uniform(set)
+end
+
+support(d::Uniform) = d.set
+sampletype(::Type{Uniform{T}}) where T = eltype(T)
+
+function pdf(d::Uniform, s)
+    if s in d.set
+        return 1.0/length(d.set)
+    else
+        return 0.0
+    end
+end
+
+"""
+    UnsafeUniform(collection)
+
+Create a uniform categorical distribution over a collection of objects.
+
+No checks are performed to ensure uniqueness or check whether an object is actually in the set when evaluating the pdf.
+"""
+struct UnsafeUniform{T}
+    collection::T
+end
+
+pdf(d::UnsafeUniform, s) = 1.0/length(d.collection)
+support(d::UnsafeUniform) = d.collection
+sampletype(::Type{UnsafeUniform{T}}) where T = eltype(T)
+
+# Common Implementations
+
+const Unif = Union{Uniform,UnsafeUniform}
+
+rand(rng::AbstractRNG, d::Unif) = rand(rng, support(d))
+mean(d::Unif) = mean(support(d))
+mode(d::Unif) = mode(support(d))
+
+function weighted_iterator(d::Unif)
+    p = 1.0/length(support(d))
+    return (x=>p for x in support(d))
+end

test/runtests.jl

@@ -6,6 +6,7 @@ using Test
 using Pkg
 using POMDPSimulators
 using POMDPPolicies
+import Distributions.Categorical
 
 @testset "ordered" begin
     include("test_ordered_spaces.jl")
@@ -30,6 +31,9 @@ end
 @testset "deterministic" begin
     include("test_deterministic.jl")
 end
+@testset "uniform" begin
+    include("test_uniform.jl")
+end
 @testset "terminalstate" begin
     include("test_terminal_state.jl")
 end

test/test_uniform.jl

@@ -0,0 +1,51 @@
+d = Uniform([1])
+
+@test rand(d) == 1
+@test rand(MersenneTwister(4), d) == 1
+@test collect(support(d)) == [1]
+@test sampletype(d) == typeof(1)
+@test sampletype(typeof(d)) == typeof(1)
+@test pdf(d, 0) == 0.0
+@test pdf(d, 1) == 1.0
+@test mode(d) == 1
+@test mean(d) == 1
+@test typeof(mean(d)) == typeof(mean([1]))
+@test collect(weighted_iterator(d)) == [1=>1.0]
+
+d2 = Uniform((:symbol,))
+@test rand(d2) == :symbol
+@test rand(MersenneTwister(4), d2) == :symbol
+@test collect(support(d2)) == [:symbol]
+@test sampletype(d2) == typeof(:symbol)
+@test sampletype(typeof(d2)) == typeof(:symbol)
+@test pdf(d2, :another) == 0.0
+@test pdf(d2, :symbol) == 1.0
+@test mode(d2) == :symbol
+@test collect(weighted_iterator(d2)) == [:symbol=>1.0]
+
+# uniqueness test
+@test_throws ErrorException Uniform((:symbol, :symbol))
+
+d3 = UnsafeUniform([1])
+
+@test rand(d3) == 1
+@test rand(MersenneTwister(4), d3) == 1
+@test collect(support(d3)) == [1]
+@test sampletype(d3) == typeof(1)
+@test sampletype(typeof(d3)) == typeof(1)
+@test pdf(d3, 1) == 1.0
+@test mean(d3) == 1
+@test mode(d3) == 1
+@test typeof(mean(d3)) == typeof(mean([1]))
+@test collect(weighted_iterator(d3)) == [1=>1.0]
+
+d4 = UnsafeUniform((:symbol,))
+@test rand(d4) == :symbol
+@test rand(MersenneTwister(4), d4) == :symbol
+@test collect(support(d4)) == [:symbol]
+@test sampletype(d4) == typeof(:symbol)
+@test sampletype(typeof(d4)) == typeof(:symbol)
+# @test pdf(d4, :another) == 0.0 # this will not work
+@test pdf(d4, :symbol) == 1.0
+@test mode(d4) == :symbol
+@test collect(weighted_iterator(d4)) == [:symbol=>1.0]

test/test_weighted_iteration.jl

@@ -1,5 +1,4 @@
 let
-    using Distributions
     dist = Categorical([0.4, 0.5, 0.1])
     c = collect(weighted_iterator(dist))
     @test c == [1=>0.4, 2=>0.5, 3=>0.1]