Kdm/univariateboundaryfix

src/testutils.jl

@@ -268,15 +268,61 @@ function test_support(d::UnivariateDistribution, vs::AbstractVector)
     end
     @test all(insupport(d, vs))
 
+    @test lowerboundary(d) == :closed || lowerboundary(d) == :open
+    @test upperboundary(d) == :closed || upperboundary(d) == :open
+
+    sp = support(d)
+    if isa(d,ContinuousUnivariateDistribution)
+        @test minimum(sp) == minimum(d)
+        @test maximum(sp) == maximum(d)
+        @test lowerboundary(sp) == lowerboundary(d)
+        @test upperboundary(sp) == upperboundary(d)
+        @test lowercomparator(sp) == lowercomparator(d)
+        @test uppercomparator(sp) == uppercomparator(d)
+    end
+
+    if isa(d,DiscreteUnivariateDistribution)
+        @test (isfinite(minimum(d)) && minimum(d) == minimum(sp)) || (minimum(sp) == typemin(Int))
+        @test (isfinite(minimum(d)) && maximum(d) == maximum(sp)) || (maximum(sp) == typemax(Int))
+    end
+
     if islowerbounded(d)
         @test isfinite(minimum(d))
-        @test insupport(d, minimum(d))
+        @test (lowerboundary(d) == :closed && insupport(d, minimum(d))) || (lowerboundary(d) == :open && !insupport(d, minimum(d)))
         @test !insupport(d, minimum(d)-1)
+        if lowerboundary(d) == :open
+            @test pdf(d,minimum(d)) == 0.0
+            @test logpdf(d,minimum(d)) == -Inf
+            @test cdf(d,minimum(d)) == 0.0
+            @test logcdf(d,minimum(d)) == -Inf
+            @test ccdf(d,minimum(d)) == 1.0
+            @test logccdf(d,minimum(d)) == 0.0
+        end
+        @test pdf(d,minimum(d)-1) == 0.0
+        @test logpdf(d,minimum(d)-1) == -Inf
+        @test cdf(d,minimum(d)-1) == 0.0
+        @test logcdf(d,minimum(d)-1) == -Inf
+        @test ccdf(d,minimum(d)-1) == 1.0
+        @test logccdf(d,minimum(d)-1) == 0.0
     end
     if isupperbounded(d)
         @test isfinite(maximum(d))
-        @test insupport(d, maximum(d))
+        @test (upperboundary(d) == :closed && insupport(d, maximum(d))) || (upperboundary(d) == :open && !insupport(d, maximum(d)))
         @test !insupport(d, maximum(d)+1)
+        if upperboundary(d) == :open
+            @test pdf(d,maximum(d)) == 0.0
+            @test logpdf(d,maximum(d)) == -Inf
+        end
+        @test cdf(d,maximum(d)) == 1.0
+        @test ccdf(d,maximum(d)) == 0.0
+        @test logcdf(d,maximum(d)) == 0.0
+        @test logccdf(d,maximum(d)) == -Inf
+        @test pdf(d,maximum(d)+1) == 0.0
+        @test logpdf(d,maximum(d)+1) == -Inf
+        @test cdf(d,maximum(d)+1) == 1.0
+        @test logcdf(d,maximum(d)+1) == 0.0
+        @test ccdf(d,maximum(d)+1) == 0.0
+        @test logccdf(d,maximum(d)+1) == -Inf
     end
 
     @test isbounded(d) == (isupperbounded(d) && islowerbounded(d))

src/univariate/continuous/betaprime.jl

@@ -11,6 +11,7 @@ immutable BetaPrime <: ContinuousUnivariateDistribution
 end
 
 @distr_support BetaPrime 0.0 Inf
+@distr_boundaries BetaPrime :open :closed
 
 #### Parameters
 
@@ -42,8 +43,12 @@ end
 #### Evaluation
 
 function logpdf(d::BetaPrime, x::Float64)
-    (α, β) = params(d)
-    (α - 1.0) * log(x) - (α + β) * log1p(x) - lbeta(α, β)
+    if insupport(d, x)
+        (α, β) = params(d)
+        (α - 1.0) * log(x) - (α + β) * log1p(x) - lbeta(α, β)
+    else
+        return -Inf
+    end
 end
 
 pdf(d::BetaPrime, x::Float64) = exp(logpdf(d, x))

src/univariate/continuous/biweight.jl

@@ -36,8 +36,8 @@ end
 
 function ccdf(d::Biweight, x::Float64)
     u = (d.μ - x) / d.σ
-    u <= -1.0 ? 1.0 :
-    u >= 1.0 ? 0.0 :
+    u <= -1.0 ? 0.0 :
+    u >= 1.0 ? 1.0 :
     0.0625 * (u + 1.0)^3 * @horner(u,8.0,-9.0,3.0)
 end
 

src/univariate/continuous/chi.jl

@@ -5,6 +5,7 @@ immutable Chi <: ContinuousUnivariateDistribution
 end
 
 @distr_support Chi 0.0 Inf
+@distr_boundaries Chi :open :closed
 
 #### Parameters
 
@@ -45,16 +46,21 @@ end
 
 pdf(d::Chi, x::Float64) = exp(logpdf(d, x))
 
-logpdf(d::Chi, x::Float64) = (ν = d.ν;
-    (1.0 - 0.5 * ν) * logtwo + (ν - 1.0) * log(x) - 0.5 * x^2 - lgamma(0.5 * ν)
-)
+function logpdf(d::Chi, x::Float64)
+    if insupport(d, x)
+        ν = d.ν
+        return (1.0 - 0.5 * ν) * logtwo + (ν - 1.0) * log(x) - 0.5 * x^2 - lgamma(0.5 * ν)
+    else
+       return -Inf
+    end
+end
 
 gradlogpdf(d::Chi, x::Float64) = x >= 0.0 ? (d.ν - 1.0) / x - x : 0.0
 
-cdf(d::Chi, x::Float64) = chisqcdf(d.ν, x^2)
-ccdf(d::Chi, x::Float64) = chisqccdf(d.ν, x^2)
-logcdf(d::Chi, x::Float64) = chisqlogcdf(d.ν, x^2)
-logccdf(d::Chi, x::Float64) = chisqlogccdf(d.ν, x^2)
+cdf(d::Chi, x::Float64) = insupport(d,x)?chisqcdf(d.ν, x^2):0.0
+ccdf(d::Chi, x::Float64) = insupport(d,x)?chisqccdf(d.ν, x^2):1.0
+logcdf(d::Chi, x::Float64) = insupport(d,x)?chisqlogcdf(d.ν, x^2):-Inf
+logccdf(d::Chi, x::Float64) = insupport(d,x)?chisqlogccdf(d.ν, x^2):0.0
 
 quantile(d::Chi, p::Float64) = sqrt(chisqinvcdf(d.ν, p))
 cquantile(d::Chi, p::Float64) = sqrt(chisqinvccdf(d.ν, p))

src/univariate/continuous/cosine.jl

@@ -52,13 +52,25 @@ end
 logpdf(d::Cosine, x::Float64) = insupport(d, x) ? log(pdf(d, x)) : -Inf
 
 function cdf(d::Cosine, x::Float64)
-    z = (x - d.μ) / d.σ
-    0.5 * (1.0 + z + sinpi(z) * invπ)
+    if insupport(d, x)
+        z = (x - d.μ) / d.σ
+        return 0.5 * (1.0 + z + sinpi(z) * invπ)
+    elseif x < minimum(d)
+        return 0.0
+    else
+        return 1.0
+    end
 end
 
 function ccdf(d::Cosine, x::Float64)
-    nz = (d.μ - x) / d.σ
-    0.5 * (1.0 + nz + sinpi(nz) * invπ)
+    if insupport(d, x)
+        nz = (d.μ - x) / d.σ
+        return 0.5 * (1.0 + nz + sinpi(nz) * invπ)
+    elseif x < minimum(d)
+        return 1.0
+    else
+        return 0.0
+    end
 end
 
 quantile(d::Cosine, p::Float64) = quantile_bisect(d, p)

src/univariate/continuous/epanechnikov.jl

@@ -39,8 +39,8 @@ end
 
 function ccdf(d::Epanechnikov, x::Float64)
     u = (d.μ - x) / d.σ
-    u <= -1 ? 1.0 :
-    u >= 1 ? 0.0 :
+    u <= -1 ? 0.0 :
+    u >= 1 ? 1.0 :
     0.5 + u * (0.75 - 0.25 * u^2)
 end
 

src/univariate/continuous/exponential.jl

@@ -38,7 +38,7 @@ pdf(d::Exponential, x::Float64) = (λ = rate(d); x < 0.0 ? 0.0 : λ * exp(-λ *
 logpdf(d::Exponential, x::Float64) =  (λ = rate(d); x < 0.0 ? -Inf : log(λ) - λ * x)
 
 cdf(d::Exponential, x::Float64) = x > 0.0 ? -expm1(-zval(d, x)) : 0.0
-ccdf(d::Exponential, x::Float64) = x > 0.0 ? exp(-zval(d, x)) : 0.0
+ccdf(d::Exponential, x::Float64) = x > 0.0 ? exp(-zval(d, x)) : 1.0
 logcdf(d::Exponential, x::Float64) = x > 0.0 ? log1mexp(-zval(d, x)) : -Inf
 logccdf(d::Exponential, x::Float64) = x > 0.0 ? -zval(d, x) : 0.0
 

src/univariate/continuous/generalizedpareto.jl

@@ -11,8 +11,7 @@ immutable GeneralizedPareto <: ContinuousUnivariateDistribution
     GeneralizedPareto() = new(1.0, 1.0, 0.0)
 end
 
-minimum(d::GeneralizedPareto) = d.μ
-maximum(d::GeneralizedPareto) = d.ξ < 0.0 ? d.μ - d.σ / d.ξ : Inf
+@distr_support GeneralizedPareto d.μ d.ξ<0.0?d.μ-d.σ/d.ξ:Inf
 
 
 #### Parameters
@@ -74,9 +73,9 @@ function logpdf(d::GeneralizedPareto, x::Float64)
     # The logpdf is log(0) outside the support range.
     p = -Inf
 
-    if x >= μ
+    if insupport(d,x)
         z = (x - μ) / σ
-        if abs(ξ) < eps()
+        if abs(ξ) < eps(x)
             p = -z - log(σ)
         elseif ξ > 0.0 || (ξ < 0.0 && x < maximum(d))
             p = (-1.0 - 1.0 / ξ) * log1p(z * ξ) - log(σ)
@@ -91,17 +90,19 @@ pdf(d::GeneralizedPareto, x::Float64) = exp(logpdf(d, x))
 function logccdf(d::GeneralizedPareto, x::Float64)
     (ξ, σ, μ) = params(d)
 
-    # The logccdf is log(0) outside the support range.
-    p = -Inf
+    p = 0.0
 
-    if x >= μ
+    if insupport(d,x)
         z = (x - μ) / σ
         if abs(ξ) < eps()
             p = -z
         elseif ξ > 0.0 || (ξ < 0.0 && x < maximum(d))
             p = (-1.0 / ξ) * log1p(z * ξ)
         end
     end
+    if x >= maximum(d)
+        p = -Inf
+    end
 
     return p
 end

src/univariate/continuous/inversegamma.jl

@@ -12,6 +12,7 @@ immutable InverseGamma <: ContinuousUnivariateDistribution
 end
 
 @distr_support InverseGamma 0.0 Inf
+@distr_boundaries InverseGamma :open :closed
 
 
 #### Parameters
@@ -55,14 +56,18 @@ end
 pdf(d::InverseGamma, x::Float64) = exp(logpdf(d, x))
 
 function logpdf(d::InverseGamma, x::Float64)
-    (α, θ) = params(d)
-    α * log(θ) - lgamma(α) - (α + 1.0) * log(x) - θ / x
+    if insupport(d, x)
+        (α, θ) = params(d)
+        return α * log(θ) - lgamma(α) - (α + 1.0) * log(x) - θ / x
+    else
+        return -Inf
+    end
 end
 
-cdf(d::InverseGamma, x::Float64) = ccdf(d.invd, 1.0 / x)
-ccdf(d::InverseGamma, x::Float64) = cdf(d.invd, 1.0 / x)
-logcdf(d::InverseGamma, x::Float64) = logccdf(d.invd, 1.0 / x)
-logccdf(d::InverseGamma, x::Float64) = logcdf(d.invd, 1.0 / x)
+cdf(d::InverseGamma, x::Float64) = insupport(d,x)?ccdf(d.invd, 1.0 / x):0.0
+ccdf(d::InverseGamma, x::Float64) = insupport(d,x)?cdf(d.invd, 1.0 / x):1.0
+logcdf(d::InverseGamma, x::Float64) = insupport(d,x)?logccdf(d.invd, 1.0 / x):-Inf
+logccdf(d::InverseGamma, x::Float64) = insupport(d,x)?logcdf(d.invd, 1.0 / x):0.0
 
 quantile(d::InverseGamma, p::Float64) = 1.0 / cquantile(d.invd, p)
 cquantile(d::InverseGamma, p::Float64) = 1.0 / quantile(d.invd, p)

src/univariate/continuous/inversegaussian.jl

@@ -11,6 +11,7 @@ immutable InverseGaussian <: ContinuousUnivariateDistribution
 end
 
 @distr_support InverseGaussian 0.0 Inf
+@distr_boundaries InverseGaussian :open :closed
 
 
 #### Parameters
@@ -39,7 +40,7 @@ end
 #### Evaluation
 
 function pdf(d::InverseGaussian, x::Float64)
-    if x > 0.0
+    if insupport(d, x)
         μ, λ = params(d)
         return sqrt(λ / (twoπ * x^3)) * exp(-λ * (x - μ)^2 / (2.0 * μ^2 * x))
     else
@@ -48,7 +49,7 @@ function pdf(d::InverseGaussian, x::Float64)
 end
 
 function logpdf(d::InverseGaussian, x::Float64)
-    if x > 0.0
+    if insupport(d, x)
         μ, λ = params(d)
         return 0.5 * (log(λ) - (log2π + 3.0 * log(x)) - λ * (x - μ)^2 / (μ^2 * x))
     else
@@ -57,7 +58,7 @@ function logpdf(d::InverseGaussian, x::Float64)
 end
 
 function cdf(d::InverseGaussian, x::Float64)
-    if x > 0.0
+    if insupport(d, x)
         μ, λ = params(d)
         u = sqrt(λ / x)
         v = x / μ
@@ -68,7 +69,7 @@ function cdf(d::InverseGaussian, x::Float64)
 end
 
 function ccdf(d::InverseGaussian, x::Float64)
-    if x > 0.0
+    if insupport(d, x)
         μ, λ = params(d)
         u = sqrt(λ / x)
         v = x / μ
@@ -79,7 +80,7 @@ function ccdf(d::InverseGaussian, x::Float64)
 end
 
 function logcdf(d::InverseGaussian, x::Float64)
-    if x > 0.0
+    if insupport(d, x)
         μ, λ = params(d)
         u = sqrt(λ / x)
         v = x / μ
@@ -92,7 +93,7 @@ function logcdf(d::InverseGaussian, x::Float64)
 end
 
 function logccdf(d::InverseGaussian, x::Float64)
-    if x > 0.0
+    if insupport(d, x)
         μ, λ = params(d)
         u = sqrt(λ / x)
         v = x / μ

src/univariate/continuous/levy.jl

@@ -8,6 +8,7 @@ immutable Levy <: ContinuousUnivariateDistribution
 end
 
 @distr_support Levy d.μ Inf
+@distr_boundaries Levy :open :open
 
 
 #### Parameters
@@ -33,19 +34,27 @@ median(d::Levy) = d.μ + d.σ / 0.4549364231195728  # 0.454... = (2.0 * erfcinv(
 #### Evaluation
 
 function pdf(d::Levy, x::Float64)
-    μ, σ = params(d)
-    z = x - μ
-    (sqrt(σ) / sqrt2π) * exp((-σ) / (2.0 * z)) / z^1.5
+    if (insupport(d,x))
+        μ, σ = params(d)
+        z = x - μ
+        return (sqrt(σ) / sqrt2π) * exp((-σ) / (2.0 * z)) / z^1.5
+    else
+        return zero(x)
+    end
 end
 
 function logpdf(d::Levy, x::Float64)
-    μ, σ = params(d)
-    z = x - μ
-    0.5 * (log(σ) - log2π - σ / z - 3.0 * log(z))
+    if (insupport(d,x))
+        μ, σ = params(d)
+        z = x - μ
+        return 0.5 * (log(σ) - log2π - σ / z - 3.0 * log(z))
+    else
+        return -Inf
+    end
 end
 
-cdf(d::Levy, x::Float64) = erfc(sqrt(d.σ / (2.0 * (x - d.μ))))
-ccdf(d::Levy, x::Float64) = erf(sqrt(d.σ / (2.0 * (x - d.μ))))
+cdf(d::Levy, x::Float64) = insupport(d,x)?erfc(sqrt(d.σ / (2.0 * (x - d.μ)))):0.0
+ccdf(d::Levy, x::Float64) = insupport(d,x)?erf(sqrt(d.σ / (2.0 * (x - d.μ)))):1.0
 
 quantile(d::Levy, p::Float64) = d.μ + d.σ / (2.0 * erfcinv(p)^2)
 cquantile(d::Levy, p::Float64) = d.μ + d.σ / (2.0 * erfinv(p)^2)