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StringDistances.jl
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add max_dist as part field for Levenshtein

pull/39/head
matthieugomez 2020-11-07 11:46:47 -08:00
parent 6b32f2dd6d
commit a53c7a9d2f
2 changed files with 29 additions and 23 deletions
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43
src/distances/edit.jl
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@ -57,16 +57,18 @@ Creates the Levenshtein distance
The Levenshtein distance is the minimum number of operations (consisting of insertions, deletions,
substitutions of a single character) required to change one string into the other.
"""
struct Levenshtein <: Metric end
struct Levenshtein{V} <: Metric where {V <: Union{Integer, Nothing}}
max_dist::V
end
Levenshtein() = Levenshtein(nothing)
## Source: http://blog.softwx.net/2014/12/optimizing-levenshtein-algorithm-in-c.html
# Return max_dist + 1 if distance higher than max_dist
# to differentiate distance equal to max_dist or not, which is important for find fctions.
function (dist::Levenshtein)(s1, s2, max_dist::Union{Integer, Nothing} = nothing)
function (dist::Levenshtein)(s1, s2)
((s1 === missing) | (s2 === missing)) && return missing
s1, s2 = reorder(s1, s2)
len1, len2 = length(s1), length(s2)
max_dist !== nothing && len2 - len1 > max_dist && return max_dist + 1
dist.max_dist !== nothing && len2 - len1 > dist.max_dist && return dist.max_dist + 1
# prefix common to both strings can be ignored
k = common_prefix(s1, s2)
k == len1 && return len2 - k
@ -77,19 +79,19 @@ function (dist::Levenshtein)(s1, s2, max_dist::Union{Integer, Nothing} = nothing
for (i1, ch1) in enumerate(s1)
i1 <= k && continue
left = current = i1 - k - 1
max_dist !== nothing && (value_lb = left - 1)
dist.max_dist !== nothing && (value_lb = left - 1)
for (i2, ch2) in enumerate(s2)
i2 <= k && continue
above, current, left = current, left, v[i2 - k]
if ch1 != ch2
current = min(current, above, left) + 1
end
max_dist !== nothing && (value_lb = min(value_lb, left))
dist.max_dist !== nothing && (value_lb = min(value_lb, left))
v[i2 - k] = current
end
max_dist !== nothing && value_lb > max_dist && return max_dist + 1
dist.max_dist !== nothing && value_lb > dist.max_dist && return dist.max_dist + 1
end
max_dist !== nothing && current > max_dist && return max_dist + 1
dist.max_dist !== nothing && current > dist.max_dist && return dist.max_dist + 1
return current
end
@ -109,23 +111,26 @@ uses the optimal string alignment algorithm. In particular, the restricted dista
the triangle inequality.
"""
struct DamerauLevenshtein <: SemiMetric end
struct DamerauLevenshtein{V} <: SemiMetric where {V <: Union{Integer, Nothing}}
max_dist::V
end
DamerauLevenshtein() = DamerauLevenshtein(nothing)
## http://blog.softwx.net/2015/01/optimizing-damerau-levenshtein_15.html
# Return max_dist + 1 if distance higher than max_dist
function (dist::DamerauLevenshtein)(s1, s2, max_dist::Union{Integer, Nothing} = nothing)
function (dist::DamerauLevenshtein)(s1, s2)
((s1 === missing) | (s2 === missing)) && return missing
s1, s2 = reorder(s1, s2)
len1, len2 = length(s1), length(s2)
max_dist !== nothing && len2 - len1 > max_dist && return max_dist + 1
dist.max_dist !== nothing && len2 - len1 > dist.max_dist && return dist.max_dist + 1
# prefix common to both strings can be ignored
k = common_prefix(s1, s2)
k == len1 && return len2 - k
v = collect(1:(len2-k))
w = similar(v)
if max_dist !== nothing
if dist.max_dist !== nothing
i2_start = 0
i2_end = max_dist
i2_end = dist.max_dist
end
prevch1, prevch2 = first(s1), first(s2)
current = 0
@ -134,16 +139,16 @@ function (dist::DamerauLevenshtein)(s1, s2, max_dist::Union{Integer, Nothing} =
left = i1 - k - 1
current = left + 1
nextTransCost = 0
if max_dist !== nothing
i2_start += (i1 - k - 1 > max_dist - (len2 - len1)) ? 1 : 0
if dist.max_dist !== nothing
i2_start += (i1 - k - 1 > dist.max_dist - (len2 - len1)) ? 1 : 0
i2_end += (i2_end < len2) ? 1 : 0
end
for (i2, ch2) in enumerate(s2)
if i2 <= k
prevch2 = ch2
elseif (max_dist !== nothing) && ((i2 - k - 1 < i2_start) | (i2 - k - 1 >= i2_end))
elseif (dist.max_dist !== nothing) && ((i2 - k - 1 < i2_start) | (i2 - k - 1 >= i2_end))
# no need to look beyond window of lower right diagonal - max distance cells
#lower right diag is i1 - (len2 - len1)) and the upper left diagonal + max_dist cells (upper left is i1)
#lower right diag is i1 - (len2 - len1)) and the upper left diagonal + dist.max_dist cells (upper left is i1)
prevch2 = ch2
else
above, current, left = current, left, v[i2 - k]
@ -161,10 +166,10 @@ function (dist::DamerauLevenshtein)(s1, s2, max_dist::Union{Integer, Nothing} =
prevch2 = ch2
end
end
max_dist !== nothing && v[i1 - k + len2 - len1] > max_dist && return max_dist + 1
dist.max_dist !== nothing && v[i1 - k + len2 - len1] > dist.max_dist && return dist.max_dist + 1
prevch1 = ch1
end
max_dist !== nothing && current > max_dist && return max_dist + 1
dist.max_dist !== nothing && current > dist.max_dist && return dist.max_dist + 1
return current
end

9
src/normalize.jl
View File

@ -1,6 +1,7 @@
struct Normalize{S <: SemiMetric} <: SemiMetric
struct Normalize{S <: SemiMetric, V <: Union{Float64, nothing}} <: SemiMetric
dist::S
max_dist::V
end
"""
@ -8,8 +9,8 @@ end
Normalize a metric, so that `evaluate` always return a Float64 between 0 and 1
"""
normalize(dist::SemiMetric) = Normalize(dist)
normalize(dist::Normalize) = dist
normalize(dist::SemiMetric, max_dist = 1.0) = Normalize(dist, max_dist)
normalize(dist::Normalize, max_dist = 1.0) = Normalize(dist.dist, max_dist)
# A normalized distance is between 0 and 1, and accept a third argument, max_dist.
@ -18,7 +19,7 @@ function (dist::Normalize{<: Union{Levenshtein, DamerauLevenshtein}})(s1, s2, ma
s1, s2 = reorder(s1, s2)
len1, len2 = length(s1), length(s2)
len2 == 0 && return 1.0
d = dist.dist(s1, s2, ceil(Int, len2 * max_dist))
d = typeof(dist.dist)(ceil(Int, len2 * max_dist))(s1, s2)
out = d / len2
out > max_dist ? 1.0 : out
end