Added rand function to generaldiscretenonparametric, but _rand! is no…

…t working.

src/multivariate/generaldiscretenonparametric.jl

@@ -0,0 +1,31 @@
+struct GeneralDiscreteNonParametric{VF,T,P <: Real,Ts <: AbstractVector{T},Ps <: AbstractVector{P},} <: Distribution{VF,Discrete}
+    support::Ts
+    p::Ps
+
+    function GeneralDiscreteNonParametric{VF,T,P,Ts,Ps}(
+        support::Ts,
+        p::Ps;
+        check_args=true,
+    ) where {VF,T,P <: Real,Ts <: AbstractVector{T},Ps <: AbstractVector{P}}
+        if check_args
+            length(support) == length(p) ||
+                error("length of `support` and `p` must be equal")
+            isprobvec(p) || error("`p` must be a probability vector")
+            allunique(support) || error("`support` must contain only unique values")
+        end
+        new{VF,T,P,Ts,Ps}(support, p)
+    end
+end
+
+function rand(rng::AbstractRNG, d::GeneralDiscreteNonParametric)
+    x = support(d)
+    p = probs(d)
+    n = length(p)
+    draw = rand(rng, float(eltype(p)))
+    cp = p[1]
+    i = 1
+    while cp <= draw && i < n
+        @inbounds cp += p[i +=1]
+    end
+    return x[i]
+end

src/multivariate/mvdiscretenonparametric.jl

@@ -51,31 +51,29 @@ Get the vector of probabilities associated with the support of `d`.
 probs(d::MvDiscreteNonParametric) = d.p
 
 
-# It would be more intuitive if length was the
-#
 Base.length(d::MvDiscreteNonParametric) = length(first(d.support))
 Base.size(d::MvDiscreteNonParametric) = (length(d), length(d.support))
 
-function _rand!(
-    rng::AbstractRNG,
-    d::MvDiscreteNonParametric,
-    x::AbstractVector{T},
-) where {T<:Real}
-
-    length(x) == length(d) || throw(DimensionMismatch("Invalid argument dimension."))
-    s = d.support
-    p = d.p
-
-    n = length(p)
-    draw = Base.rand(rng, float(eltype(p)))
-    cp = p[1]
-    i = 1
-    while cp <= draw && i < n
-        @inbounds cp += p[i+=1]
-    end
-    copyto!(x, s[i])
-    return x
-end
+# function _rand!(
+#     rng::AbstractRNG,
+#     d::MvDiscreteNonParametric,
+#     x::AbstractVector{T},
+# ) where {T<:Real}
+
+#     length(x) == length(d) || throw(DimensionMismatch("Invalid argument dimension."))
+#     s = d.support
+#     p = d.p
+
+#     n = length(p)
+#     draw = Base.rand(rng, float(eltype(p)))
+#     cp = p[1]
+#     i = 1
+#     while cp <= draw && i < n
+#         @inbounds cp += p[i+=1]
+#     end
+#     copyto!(x, s[i])
+#     return x
+# end
 
 function _logpdf(d::MvDiscreteNonParametric, x::AbstractVector{T}) where {T<:Real}
     s = support(d)

src/univariate/discrete/discretenonparametric.jl

@@ -69,19 +69,6 @@ Base.isapprox(c1::D, c2::D) where D<:DiscreteNonParametric =
 
 # Sampling
 
-function rand(rng::AbstractRNG, d::DiscreteNonParametric)
-    x = support(d)
-    p = probs(d)
-    n = length(p)
-    draw = rand(rng, float(eltype(p)))
-    cp = p[1]
-    i = 1
-    while cp <= draw && i < n
-        @inbounds cp += p[i +=1]
-    end
-    return x[i]
-end
-
 sampler(d::DiscreteNonParametric) =
     DiscreteNonParametricSampler(support(d), probs(d))