Add distances, divergences, and deviations from StatsBase

src/Distances.jl

@@ -32,6 +32,7 @@ export
     CorrDist,
     ChiSqDist,
     KLDivergence,
+    GenKLDivergence,
     JSDivergence,
     RenyiDivergence,
     SpanNormDist,
@@ -46,6 +47,11 @@ export
     BhattacharyyaDist,
     HellingerDist,
 
+    MeanAbsDeviation,
+    MeanSqDeviation,
+    RMSDeviation,
+    NormRMSDeviation,
+
     # convenient functions
     euclidean,
     sqeuclidean,
@@ -61,6 +67,7 @@ export
     corr_dist,
     chisq_dist,
     kl_divergence,
+    gkl_divergence,
     js_divergence,
     renyi_divergence,
     spannorm_dist,
@@ -73,7 +80,11 @@ export
     sqmahalanobis,
     mahalanobis,
     bhattacharyya,
-    hellinger
+    hellinger,
+
+    msd,
+    rmsd,
+    nrmsd
 
 include("common.jl")
 include("generic.jl")

src/metrics.jl

@@ -24,6 +24,7 @@ type CorrDist <: SemiMetric end
 
 type ChiSqDist <: SemiMetric end
 type KLDivergence <: PreMetric end
+type GenKLDivergence <: PreMetric end
 
 immutable RenyiDivergence{T <: Real} <: PreMetric
     p::T # order of power mean (order of divergence - 1)
@@ -37,10 +38,10 @@ immutable RenyiDivergence{T <: Real} <: PreMetric
         is_zero = q ≈ zero(T)
         is_one = q ≈ one(T)
         is_inf = isinf(q)
-        
+
         # Only positive Rényi divergences are defined
         !is_zero && q < zero(T) && throw(ArgumentError("Order of Rényi divergence not legal, $(q) < 0."))
-        
+
         new(q - 1, !(is_zero || is_one || is_inf), is_zero, is_one, is_inf)
     end
 end
@@ -50,8 +51,15 @@ type JSDivergence <: SemiMetric end
 
 type SpanNormDist <: SemiMetric end
 
+# Deviations are handled separately from the other distances/divergences and
+# are excluded from `UnionMetrics`
+type MeanAbsDeviation <: Metric end
+type MeanSqDeviation <: SemiMetric end
+type RMSDeviation <: Metric end
+type NormRMSDeviation <: Metric end
+
 
-typealias UnionMetrics Union{Euclidean, SqEuclidean, Chebyshev, Cityblock, Minkowski, Hamming, Jaccard, RogersTanimoto, CosineDist, CorrDist, ChiSqDist, KLDivergence, RenyiDivergence, JSDivergence, SpanNormDist}
+typealias UnionMetrics Union{Euclidean, SqEuclidean, Chebyshev, Cityblock, Minkowski, Hamming, Jaccard, RogersTanimoto, CosineDist, CorrDist, ChiSqDist, KLDivergence, RenyiDivergence, JSDivergence, SpanNormDist, GenKLDivergence}
 
 ###########################################################
 #
@@ -163,6 +171,11 @@ chisq_dist(a::AbstractArray, b::AbstractArray) = evaluate(ChiSqDist(), a, b)
 @inline eval_reduce(::KLDivergence, s1, s2) = s1 + s2
 kl_divergence(a::AbstractArray, b::AbstractArray) = evaluate(KLDivergence(), a, b)
 
+# GenKLDivergence
+@inline eval_op(::GenKLDivergence, ai, bi) = ai > 0 ? ai * log(ai / bi) - ai + bi : bi
+@inline eval_reduce(::GenKLDivergence, s1, s2) = s1 + s2
+gkl_divergence(a::AbstractArray, b::AbstractArray) = evaluate(GenKLDivergence(), a, b)
+
 # RenyiDivergence
 function eval_start{T<:AbstractFloat}(::RenyiDivergence, a::AbstractArray{T}, b::AbstractArray{T})
     zero(T), zero(T)
@@ -289,6 +302,23 @@ end
 end
 rogerstanimoto{T <: Bool}(a::AbstractArray{T}, b::AbstractArray{T}) = evaluate(RogersTanimoto(), a, b)
 
+# Deviations
+
+evaluate(::MeanAbsDeviation, a, b) = cityblock(a, b) / length(a)
+
+evaluate(::MeanSqDeviation, a, b) = sqeuclidean(a, b) / length(a)
+msd(a, b) = evaluate(MeanSqDeviation(), a, b)
+
+evaluate(::RMSDeviation, a, b) = sqrt(evaluate(MeanSqDeviation(), a, b))
+rmsd(a, b) = evaluate(RMSDeviation(), a, b)
+
+function evaluate(::NormRMSDeviation, a, b)
+    amin, amax = extrema(a)
+    return evaluate(RMSDeviation(), a, b) / (amax - amin)
+end
+nrmsd(a, b) = evaluate(NormRMSDeviation(), a, b)
+
+
 ###########################################################
 #
 #  Special method

test/test_dists.jl

@@ -63,7 +63,12 @@ for (x, y) in (([4., 5., 6., 7.], [3., 9., 8., 1.]),
     @test spannorm_dist(x, x) == 0.
     @test spannorm_dist(x, y) == maximum(x - vec(y)) - minimum(x - vec(y))
 
+    @test gkl_divergence(x, y) ≈ sum(i -> x[i] * log(x[i] / y[i]) - x[i] + y[i], 1:length(x))
 
+    @test evaluate(MeanAbsDeviation(), x, y) ≈ mean(Float64[abs(x[i] - y[i]) for i in 1:length(x)])
+    @test msd(x, y) ≈ mean(Float64[abs2(x[i] - y[i]) for i in 1:length(x)])
+    @test rmsd(x, y) ≈ sqrt(msd(x, y))
+    @test nrmsd(x, y) ≈ sqrt(msd(x, y)) / (maximum(x) - minimum(x))
 
     w = ones(4)
     @test sqeuclidean(x, y) ≈ wsqeuclidean(x, y, w)
@@ -101,7 +106,7 @@ w = rand(size(a))
 p = r = rand(12)
 p[p .< 0.3] = 0.0
 scale = sum(p) / sum(r)
-r /= sum(r)    
+r /= sum(r)
 p /= sum(p)
 q = rand(12)
 q /= sum(q)
@@ -119,14 +124,14 @@ end
 @test renyi_divergence(p, p, rand()) ≈ 0
 @test renyi_divergence(p, p, 1.0 + rand()) ≈ 0
 @test renyi_divergence(p, p, Inf) ≈ 0
-@test renyi_divergence(p, r, 0) ≈ -log(scale)    
-@test renyi_divergence(p, r, 1) ≈ -log(scale)    
-@test renyi_divergence(p, r, rand()) ≈ -log(scale)    
+@test renyi_divergence(p, r, 0) ≈ -log(scale)
+@test renyi_divergence(p, r, 1) ≈ -log(scale)
+@test renyi_divergence(p, r, rand()) ≈ -log(scale)
 @test renyi_divergence(p, r, Inf) ≈ -log(scale)
 @test isinf(renyi_divergence([0.0, 0.5, 0.5], [0.0, 1.0, 0.0], Inf))
 @test renyi_divergence([0.0, 1.0, 0.0], [0.0, 0.5, 0.5], Inf) ≈ log(2.0)
 @test renyi_divergence(p, q, 1) ≈ kl_divergence(p, q)
-    
+
 pm = (p + q) / 2
 jsv = kl_divergence(p, pm) / 2 + kl_divergence(q, pm) / 2
 @test js_divergence(p, p) ≈ 0.0