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### Initial port of material from my old branch

```New functions:
* entropy()
* binaryentropy()
* modes()

New distributions:
* BetaPrime
* Erlang
* Triangular```
parent e8eec01b
 ... ... @@ -21,6 +21,7 @@ Many distribution types also provide useful theoretical information about the di As of v0.0.0, the following distributions have been implemented: * Arcsine * Bernoulli * Beta * Binomial ... ... @@ -28,30 +29,38 @@ As of v0.0.0, the following distributions have been implemented: * Cauchy * Chisq * Dirichlet * DiscreteUniform * Exponential * FDist * Gamma * Geometric * HyperGeometric * Laplace * Logistic * logNormal * MixtureModel * Multinomial * MultivariateNormal * NegativeBinomial * NoncentralBeta * NoncentralChisq * NoncentralF * NoncentralT * Normal * Pareto * Poisson * Rayleigh * TDist * Uniform * Weibull ## Simple Examples using Distributions x = rand(Normal(0.0, 1.0), 10_000) mean(x) d = Beta(1.0, 9.0) pdf(d, 0.9) quantile(d, 0.1) ... ...
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src/show.jl 0 → 100644
 ## ## ## String representations ## ## function show(io::IO, d::Distribution) print(io, @sprintf "%s distribution\n" typeof(d)) for parameter in typeof(d).names if isa(d.(parameter), AbstractArray) param = strcat(ucfirst(string(parameter)), ":\n", d.(parameter), "\n") else param = strcat(ucfirst(string(parameter)), ": ", d.(parameter), "\n") end print(io, param) end m = mean(d) if isa(m, AbstractArray) print(io, strcat("Mean:\n", m, "\n")) else print(io, strcat("Mean: ", m, "\n")) end v = var(d) if isa(v, AbstractArray) print(io, strcat("Variance:\n", v)) else print(io, strcat("Variance: ", v)) end end
 ... ... @@ -43,9 +43,7 @@ end # Additional tests on the Multinomial and Dirichlet constructors d = Multinomial(1, [0.5, 0.4, 0.1]) d = Multinomial(1, 3) d = Multinomial(3) d = Multinomial(1, [0.6; 0.4]) d = Multinomial(1, [0.6; 0.4]') d = Multinomial(2) mean(d) var(d) @assert insupport(d, [1, 0]) ... ... @@ -64,8 +62,6 @@ rand!(d, A) d = Dirichlet([1.0, 2.0, 1.0]) d = Dirichlet(3) d = Dirichlet([1.0; 2.0; 1.0]) d = Dirichlet([1.0; 2.0; 1.0]') mean(d) var(d) insupport(d, [0.1, 0.8, 0.1]) ... ... @@ -78,7 +74,7 @@ rand!(d, A) d = Categorical([0.25, 0.5, 0.25]) d = Categorical(3) d = Categorical([0.25; 0.5; 0.25]) d = Categorical([0.25, 0.5, 0.25]) @assert !insupport(d, 0) @assert insupport(d, 1) ... ...
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