Remove MvDiscreteNonParametric (#169)

* Remove `MvDiscreteNonParametric`

* Bump version

Co-authored-by: Hong Ge <[email protected]>

Author: devmotion

src/DistributionsAD.jl

@@ -53,7 +53,6 @@ include("arraydist.jl")
 include("filldist.jl")
 include("univariate.jl")
 include("multivariate.jl")
-include("mvcategorical.jl")
 include("matrixvariate.jl")
 include("flatten.jl")
 

src/mvcategorical.jl

@@ -342,17 +342,4 @@ function isprobvec(p::SubArray{<:TrackedReal, 1, <:TrackedArray{<:Real}})
     pdata = value(p)
     all(x -> x ≥ zero(x), pdata) && isapprox(sum(pdata), one(eltype(pdata)), atol = 1e-6)
 end
-
-_mv_categorical_logpdf(ps::TrackedMatrix, x) = track(_mv_categorical_logpdf, ps, x)
-@grad function _mv_categorical_logpdf(ps, x)
-    ps_data = value(ps)
-    probs = view(ps_data, x, :)
-    ps_grad = zero(ps_data)
-    sum(log, probs), Δ -> begin
-        ps_grad .= 0
-        ps_grad[x,:] .= Δ ./ probs
-        return (ps_grad, nothing)
-    end
-end
-
 end

src/reversediff.jl

@@ -349,18 +349,6 @@ function Distributions.logpdf(
     end
 end
 
-## Categorical ##
-
-function Distributions.DiscreteNonParametric{T,P,Ts,Ps}(
-    vs::Ts,
-    ps::Ps;
-    check_args=true,
-) where {T<:Real,P<:Real,Ts<:AbstractVector{T},Ps<:TrackedArray{P, 1, <:SubArray{P, 1}}}
-    cps = ps[:]
-    return DiscreteNonParametric{T,P,Ts,typeof(cps)}(vs, cps; check_args = check_args)
-end
-
-
 ## Dirichlet ##
 
 Distributions.Dirichlet(alpha::TrackedVector) = TuringDirichlet(alpha)
@@ -559,22 +547,6 @@ end
 # zero mean,, constant variance
 MvLogNormal(d::Int, σ::TrackedReal{<:Real}) = TuringMvLogNormal(TuringMvNormal(d, σ))
 
-
-## MvCategorical ##
-
-_mv_categorical_logpdf(ps::Tracker.TrackedMatrix, x) = Tracker.track(_mv_categorical_logpdf, ps, x)
-Tracker.@grad function _mv_categorical_logpdf(ps, x)
-    ps_data = Tracker.data(ps)
-    probs = view(ps_data, x, :)
-    ps_grad = zero(ps_data)
-    sum(log, probs), Δ -> begin
-        ps_grad .= 0
-        ps_grad[x,:] .= Δ ./ probs
-        return (ps_grad, nothing)
-    end
-end
-
-
 ## Wishart ##
 
 function Distributions._logpdf(d::Wishart, X::TrackedMatrix)

src/tracker.jl

@@ -54,23 +54,3 @@ end
 quantile(d::TuringPoissonBinomial, x::Float64) = quantile(Categorical(d.pmf), x) - 1
 Base.minimum(d::TuringPoissonBinomial) = 0
 Base.maximum(d::TuringPoissonBinomial) = length(d.p)
-
-
-## Categorical ##
-
-function Base.convert(
-    ::Type{Distributions.DiscreteNonParametric{T,P,Ts,Ps}},
-    d::Distributions.DiscreteNonParametric{T,P,Ts,Ps},
-) where {T<:Real,P<:Real,Ts<:AbstractVector{T},Ps<:AbstractVector{P}}
-    DiscreteNonParametric{T,P,Ts,Ps}(support(d), probs(d), check_args=false)
-end
-
-# Fix SubArray support
-function Distributions.DiscreteNonParametric{T,P,Ts,Ps}(
-    vs::Ts,
-    ps::Ps;
-    check_args=true,
-) where {T<:Real, P<:Real, Ts<:AbstractVector{T}, Ps<:SubArray{P, 1}}
-    cps = ps[:]
-    return DiscreteNonParametric{T,P,Ts,typeof(cps)}(vs, cps; check_args = check_args)
-end