Optimize vMF sampling

src/samplers/vonmisesfisher.jl

@@ -6,47 +6,49 @@ struct VonMisesFisherSampler
     b::Float64
     x0::Float64
     c::Float64
-    Q::Matrix{Float64}
+    v::Vector{Float64}
 end
 
 function VonMisesFisherSampler(μ::Vector{Float64}, κ::Float64)
     p = length(μ)
     b = _vmf_bval(p, κ)
     x0 = (1.0 - b) / (1.0 + b)
     c = κ * x0 + (p - 1) * log1p(-abs2(x0))
-    Q = _vmf_rotmat(μ)
-    VonMisesFisherSampler(p, κ, b, x0, c, Q)
+    v = _vmf_householder_vec(μ)
+    VonMisesFisherSampler(p, κ, b, x0, c, v)
 end
 
-function _rand!(rng::AbstractRNG, spl::VonMisesFisherSampler,
-                x::AbstractVector, t::AbstractVector)
+@inline function _vmf_rot!(v::AbstractVector, x::AbstractVector)
+    # rotate
+    scale = 2.0 * (v' * x)
+    @. x -= (scale * v)
+    return x
+end
+
+
+function _rand!(rng::AbstractRNG, spl::VonMisesFisherSampler, x::AbstractVector)
     w = _vmf_genw(rng, spl)
     p = spl.p
-    t[1] = w
+    x[1] = w
     s = 0.0
-    for i = 2:p
-        t[i] = ti = randn(rng)
-        s += abs2(ti)
+    @inbounds for i = 2:p
+        x[i] = xi = randn(rng)
+        s += abs2(xi)
     end
 
-    # normalize t[2:p]
+    # normalize x[2:p]
     r = sqrt((1.0 - abs2(w)) / s)
-    for i = 2:p
-        t[i] *= r
+    @inbounds for i = 2:p
+        x[i] *= r
     end
 
-    # rotate
-    mul!(x, spl.Q, t)
-    return x
+    return _vmf_rot!(spl.v, x)
 end
 
-_rand!(rng::AbstractRNG, spl::VonMisesFisherSampler, x::AbstractVector) =
-    _rand!(rng, spl, x, Vector{Float64}(undef, length(x)))
 
 function _rand!(rng::AbstractRNG, spl::VonMisesFisherSampler, x::AbstractMatrix)
-    t = Vector{Float64}(undef, size(x, 1))
-    for j = 1:size(x, 2)
-        _rand!(rng, spl, view(x,:,j), t)
+    @inbounds for j in axes(x, 2)
+        _rand!(rng, spl, view(x,:,j))
     end
     return x
 end
@@ -56,12 +58,13 @@ end
 
 _vmf_bval(p::Int, κ::Real) = (p - 1) / (2.0κ + sqrt(4 * abs2(κ) + abs2(p - 1)))
 
-function _vmf_genw(rng::AbstractRNG, p, b, x0, c, κ)
-    # generate the W value -- the key step in simulating vMF
-    #
-    #   following movMF's document
-    #
+function _vmf_genw3(rng::AbstractRNG, p, b, x0, c, κ)
+    ξ = rand(rng)
+    w = 1.0 + (log(ξ + (1.0 - ξ)*exp(-2κ))/κ)
+    return w::Float64
+end
 
+function _vmf_genwp(rng::AbstractRNG, p, b, x0, c, κ)
     r = (p - 1) / 2.0
     betad = Beta(r, r)
     z = rand(rng, betad)
@@ -73,50 +76,35 @@ function _vmf_genw(rng::AbstractRNG, p, b, x0, c, κ)
     return w::Float64
 end
 
+# generate the W value -- the key step in simulating vMF
+#
+#   following movMF's document for the p != 3 case
+#   and Wenzel Jakob's document for the p == 3 case
+function _vmf_genw(rng::AbstractRNG, p, b, x0, c, κ)
+    if p == 3
+        return _vmf_genw3(rng, p, b, x0, c, κ)
+    else
+        return _vmf_genwp(rng, p, b, x0, c, κ)
+    end
+end
+
+
 _vmf_genw(rng::AbstractRNG, s::VonMisesFisherSampler) =
     _vmf_genw(rng, s.p, s.b, s.x0, s.c, s.κ)
 
-function _vmf_rotmat(u::Vector{Float64})
-    # construct a rotation matrix Q
-    # s.t. Q * [1,0,...,0]^T --> u
-    #
-    # Strategy: construct a full-rank matrix
-    # with first column being u, and then
-    # perform QR factorization
-    #
-
-    p = length(u)
-    A = zeros(p, p)
-    copyto!(view(A,:,1), u)
-
-    # let k the be index of entry with max abs
-    k = 1
-    a = abs(u[1])
-    for i = 2:p
-        @inbounds ai = abs(u[i])
-        if ai > a
-            k = i
-            a = ai
-        end
-    end
+function _vmf_householder_vec(μ::Vector{Float64})
+    # assuming μ is a unit-vector (which it should be)
+    #  can compute v in a single pass over μ
 
-    # other columns of A will be filled with
-    # indicator vectors, except the one
-    # that activates the k-th entry
-    i = 1
-    for j = 2:p
-        if i == k
-            i += 1
-        end
-        A[i, j] = 1.0
-    end
+    p = length(μ)
+    v = similar(μ)
+    v[1] = μ[1] - 1.0
+    s = sqrt(-2*v[1])
+    v[1] /= s
 
-    # perform QR factorization
-    Q = Matrix(qr!(A).Q)
-    if dot(view(Q,:,1), u) < 0.0  # the first column was negated
-        for i = 1:p
-            @inbounds Q[i,1] = -Q[i,1]
-        end
+    @inbounds for i in 2:p
+        v[i] = μ[i] / s
     end
-    return Q
+
+    return v
 end

test/vonmisesfisher.jl

@@ -22,6 +22,55 @@ function gen_vmf_tdata(n::Int, p::Int,
     return X
 end
 
+function test_vmf_rot(p::Int, rng::Union{AbstractRNG, Missing} = missing)
+    if ismissing(rng)
+        μ = randn(p)
+        x = randn(p)
+    else
+        μ = randn(rng, p)
+        x = randn(rng, p)
+    end
+    κ = norm(μ)
+    μ = μ ./ κ
+
+    s = Distributions.VonMisesFisherSampler(μ, κ)
+    v = μ - vcat(1, zeros(p-1))
+    H = I - 2*v*v'/(v'*v)
+
+    @test Distributions._vmf_rot!(s.v, copy(x)) ≈ (H*x)
+
+end
+
+
+
+function test_genw3(κ::Real, ns::Int, rng::Union{AbstractRNG, Missing} = missing)
+    p = 3
+
+    if ismissing(rng)
+        μ = randn(p)
+    else
+        μ = randn(rng, p)
+    end
+    μ = μ ./ norm(μ)
+
+    s = Distributions.VonMisesFisherSampler(μ, float(κ))
+
+    genw3_res = [Distributions._vmf_genw3(rng, s.p, s.b, s.x0, s.c, s.κ) for _ in 1:ns]
+    genwp_res = [Distributions._vmf_genwp(rng, s.p, s.b, s.x0, s.c, s.κ) for _ in 1:ns]
+
+    @test isapprox(mean(genw3_res), mean(genwp_res), atol=0.01)
+    @test isapprox(std(genw3_res), std(genwp_res), atol=0.01/κ)
+
+    # test mean and stdev against analytical formulas
+    coth_κ = coth(κ)
+    mean_w = coth_κ - 1/κ
+    var_w = 1 - coth_κ^2 + 1/κ^2
+
+    @test isapprox(mean(genw3_res), mean_w, atol=0.01)
+    @test isapprox(std(genw3_res), sqrt(var_w), atol=0.01/κ)
+end
+
+
 function test_vonmisesfisher(p::Int, κ::Real, n::Int, ns::Int,
                              rng::Union{AbstractRNG, Missing} = missing)
     if ismissing(rng)
@@ -65,6 +114,7 @@ function test_vonmisesfisher(p::Int, κ::Real, n::Int, ns::Int,
         x = rand(rng, d)
     end
     @test norm(x) ≈ 1.0
+    @test insupport(d, x)
 
     if ismissing(rng)
         X = rand(d, n)
@@ -73,6 +123,7 @@ function test_vonmisesfisher(p::Int, κ::Real, n::Int, ns::Int,
     end
     for i = 1:n
         @test norm(X[:,i]) ≈ 1.0
+        @test insupport(d, X[:,i])
     end
 
     # MLE
@@ -118,5 +169,12 @@ ns = 10^6
                                                                            (5, 2.0),
                                                                            (2, 2)]
         test_vonmisesfisher(p, κ, n, ns, rng)
+        test_vmf_rot(p, rng)
+    end
+
+    if !ismissing(rng)
+        @testset "Testing genw with $key at (3, $κ)" for κ in [0.1, 0.5, 1.0, 2.0, 5.0]
+            test_genw3(κ, ns, rng)
+        end
     end
 end