add support for fitting Exponential distribution

README.md

@@ -28,5 +28,6 @@ Currently, support for the following distributios are implemented:
 - Normal
 - Lognormal
 - Logitnormal 
+- Exponential 
 
 See [Documentation](https://bgctw.github.io/DistributionFits.jl/dev)

src/univariate/continuous/exponential.jl

@@ -0,0 +1,26 @@
+function fit(::Type{Exponential}, m::AbstractMoments)
+    # https://en.wikipedia.org/wiki/Exponential_distribution
+    n_moments(m) >= 1 || error("Need mean to estimate exponential")
+    return Exponential(mean(m))
+end
+
+function fit(::Type{Exponential}, lower::QuantilePoint, upper::QuantilePoint)
+    # return average for the two quantiles
+    θ_lower = -lower.q/log(1-lower.p)
+    θ_upper = -upper.q/log(1-upper.p)
+    θ = (θ_lower + θ_upper)/2
+    Exponential(θ)
+end
+
+function fit_mean_quantile(::Type{Exponential}, mean::Real, qp::QuantilePoint)
+    # only fit to mean
+    fit(Type{Exponential}, AbstractMoments(mean))
+end
+
+function fit_mode_quantile(::Type{Exponential}, mode::Real, qp::QuantilePoint)
+    # ignore mode (its always at 0)
+    θ = -qp.q/log(1-qp.p)
+    Exponential(θ)
+end
+
+

src/univariates.jl

@@ -29,7 +29,7 @@ const continuous_distributions = [
     # "chi",
     # "cosine",
     # "epanechnikov",
-    # "exponential",
+     "exponential",
     # "fdist",
     # "frechet",
     # "gamma", "erlang",

test/exponential.jl

@@ -0,0 +1,41 @@
+
+@testset "fit moments" begin
+    D = Exponential(2.1)
+    M = Moments(mean(D))
+    Dfit = fit(Exponential, M)
+    @test D ≈ Dfit
+    # handle not given any moment
+    @test_throws Exception fit(Exponential, Moments())
+end;
+
+@testset "fit to quantilepoint and mode - ignore mode" begin
+    d = Exponential(0.8)
+    qp = @qp(quantile(d,0.95),0.95)
+    dfit = fit_mode_quantile(Exponential, mode(d), qp)
+    @test dfit ≈ d
+    dfit = fit(Exponential, mode(d), qp, Val(:mode))
+    @test dfit ≈ d
+    # with lower quantile
+    qp = @qp(quantile(d,0.025),0.025)
+    dfit = fit_mode_quantile(Exponential, mode(d), qp)
+    @test dfit ≈ d
+end;
+
+@testset "fit two quantiles same" begin
+    qpl = @qp_m(3)
+    d = fit(Exponential, qpl, qpl);
+    @test quantile.(d, [qpl.p]) ≈ [qpl.q]
+end;
+
+@testset "fit two quantiles" begin
+    qpl = @qp_m(3)
+    qpu = @qp_u(5)
+    d = fit(Exponential, qpl, qpu);
+    #plot(d); 
+    #plot!(fit_mode_quantile(Exponential, NaN, qpl))
+    #plot!(fit_mode_quantile(Exponential, NaN, qpu))
+    #vline!([3,5])
+    @test quantile(d, qpl.p) < qpl.q
+    @test quantile(d, qpu.p) > qpu.q
+end;
+

test/runtests.jl

@@ -16,6 +16,7 @@ const tests = [
     "normal",
     "lognormal",
     "logitnormal",
+    "exponential",
 ]
 #tests = ["logitnormal"]