rmv NormalizedStringMetric

src/StringDistances.jl

@@ -3,9 +3,7 @@ module StringDistances
 using Distances
 import StatsAPI: pairwise, pairwise!
 abstract type StringSemiMetric <: SemiMetric end
-abstract type NormalizedStringSemiMetric <: StringSemiMetric end
 abstract type StringMetric <: Metric end
-(dist::NormalizedStringSemiMetric)(s1, s2; max_dist = 1.0) = dist(s1, s2)
 (dist::Union{StringSemiMetric, StringMetric})(s1, s2; max_dist = nothing) = dist(s1, s2)
 
 function Distances.result_type(dist::Union{StringSemiMetric, StringMetric}, s1::Type, s2::Type)

src/normalize.jl

@@ -15,24 +15,25 @@ julia> StringDistances.Normalized(Levenshtein())(s1, s2)
 0.8064 
 ```
 """
-struct Normalized{T <: Union{StringSemiMetric, StringMetric}} <: NormalizedStringSemiMetric
+struct Normalized{T <: Union{StringSemiMetric, StringMetric}} <: StringSemiMetric
     dist::T
 end
 Normalized(dist::Union{StringSemiMetric, StringMetric}) = Normalized{typeof(dist)}(dist)
 Normalized(dist::Normalized) = dist
 
-
 # this basically says that all distances are considered to be normalized by default
 function (dist::Normalized)(s1, s2; max_dist = 1.0)
-    dist.dist(s1, s2; max_dist = max_dist)
+    out = dist.dist(s1, s2; max_dist = max_dist)
+    max_dist !== nothing && out > max_dist && return 1.0
+    return out
 end
 
-
-
 function (dist::Normalized{<:Union{Hamming, DamerauLevenshtein}})(s1, s2; max_dist = 1.0)
     (s1 === missing) | (s2 === missing) && return missing
     isempty(s1) && isempty(s2) && return 0.0
-    out = dist.dist(s1, s2) / length(s2)
+    s1, s2 = reorder(s1, s2)
+    len1, len2 = length(s1), length(s2)
+    out = dist.dist(s1, s2) / len2
     max_dist !== nothing && out > max_dist && return 1.0
     return out
 end
@@ -42,7 +43,7 @@ function (dist::Normalized{<:Union{Levenshtein, OptimalStringAlignement}})(s1, s
     isempty(s1) && isempty(s2) && return 0.0
     s1, s2 = reorder(s1, s2)
     len1, len2 = length(s1), length(s2)
-    if max_dist === nothing || max_dist == 1.0
+    if max_dist == 1.0
         d = dist.dist(s1, s2)
     else
         d = dist.dist(s1, s2; max_dist = ceil(Int, len2 * max_dist))