Merge 11ef5d3 into 0c4d0a5

JuliaStats · Feb 27, 2019 · 642099d · 642099d
2 parents 0c4d0a5 + 11ef5d3
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diff --git a/src/StatsBase.jl b/src/StatsBase.jl
@@ -1,27 +1,28 @@
 __precompile__()
 
 module StatsBase
-    import Base: length, isempty, eltype, values, sum, show, maximum, minimum, extrema
-    import Base.Cartesian: @nloops, @nref, @nextract
-    using Base: @irrational, @propagate_inbounds
-    import DataStructures: heapify!, heappop!, percolate_down!
-    using SortingAlgorithms
-    using Missings
-
-    using Statistics
-    using LinearAlgebra
-    using Random
-    using Printf
-    using SparseArrays
-    import Random: rand, rand!
-    import LinearAlgebra: BlasReal, BlasFloat
-    import Statistics: mean, mean!, var, varm, varm!, std, stdm, cov, covm,
-                       cor, corm, cov2cor!, unscaled_covzm, quantile, sqrt!,
-                       median, middle
+
+import Base: length, isempty, eltype, values, sum, show, maximum, minimum, extrema
+import Base.Cartesian: @nloops, @nref, @nextract
+using Base: @irrational, @propagate_inbounds
+import DataStructures: heapify!, heappop!, percolate_down!
+using SortingAlgorithms
+using Missings
+
+using Statistics
+using LinearAlgebra
+using Random
+using Printf
+using SparseArrays
+import Random: rand, rand!
+import LinearAlgebra: BlasReal, BlasFloat
+import Statistics: mean, mean!, var, varm, varm!, std, stdm, cov, covm,
+                   cor, corm, cov2cor!, unscaled_covzm, quantile, sqrt!,
+                   median, middle
 
     ## tackle compatibility issues
 
-    export
+export
 
     ## weights
     AbstractWeights,    # abstract type to represent any weight vector
@@ -207,30 +208,30 @@ module StatsBase
     ZScoreTransform,       # the type to represent a z-score data transformation
     UnitRangeTransform     # the type to represent a 0-1 data transformation
 
-    # source files
-
-    include("common.jl")
-    include("weights.jl")
-    include("moments.jl")
-    include("scalarstats.jl")
-    include("robust.jl")
-    include("deviation.jl")
-    include("cov.jl")
-    include("counts.jl")
-    include("ranking.jl")
-    include("toeplitzsolvers.jl")
-    include("rankcorr.jl")
-    include("signalcorr.jl")
-    include("partialcor.jl")
-    include("empirical.jl")
-    include("hist.jl")
-    include("misc.jl")
-
-    include("sampling.jl")
-    include("statmodels.jl")
-
-    include("transformations.jl")
-
-    include("deprecates.jl")
+# source files
+
+include("common.jl")
+include("weights.jl")
+include("moments.jl")
+include("scalarstats.jl")
+include("robust.jl")
+include("deviation.jl")
+include("cov.jl")
+include("counts.jl")
+include("ranking.jl")
+include("toeplitzsolvers.jl")
+include("rankcorr.jl")
+include("signalcorr.jl")
+include("partialcor.jl")
+include("empirical.jl")
+include("hist.jl")
+include("misc.jl")
+
+include("sampling.jl")
+include("statmodels.jl")
+
+include("transformations.jl")
+
+include("deprecates.jl")
 
 end # module
diff --git a/src/scalarstats.jl b/src/scalarstats.jl
@@ -438,7 +438,8 @@ zscore(X::AbstractArray{<:Real}, dim::Int) = ((μ, σ) = mean_and_std(X, dim); z
 Compute the entropy of a collection of probabilities `p`, optionally specifying a real number
 `b` such that the entropy is scaled by `1/log(b)`.
 """
-entropy(p) = -sum(pi -> pi * log(pi), p)
+entropy(p) = -sum(pᵢ -> iszero(pᵢ) ? zero(pᵢ) : pᵢ * log(pᵢ), p)
+
 entropy(p, b::Real) = entropy(p) / log(b)
 
 """

diff --git a/test/scalarstats.jl b/test/scalarstats.jl
@@ -119,6 +119,8 @@ it = (xᵢ for xᵢ in x)
 
 @test entropy([0.5, 0.5],2)       ≈ 1.0
 @test entropy([0.2, 0.3, 0.5], 2) ≈ 1.4854752972273344
+@test entropy([0.2, 0.3, 0.5], 2) ≈ 1.4854752972273344
+@test entropy([1.0, 0.0]) ≈ 0.0
 
 ##### Renyi entropies
 # Generate a random probability distribution