Levenshtypo 1.1.0
See the version list below for details.
dotnet add package Levenshtypo --version 1.1.0
NuGet\Install-Package Levenshtypo -Version 1.1.0
<PackageReference Include="Levenshtypo" Version="1.1.0" />
paket add Levenshtypo --version 1.1.0
#r "nuget: Levenshtypo, 1.1.0"
// Install Levenshtypo as a Cake Addin #addin nuget:?package=Levenshtypo&version=1.1.0 // Install Levenshtypo as a Cake Tool #tool nuget:?package=Levenshtypo&version=1.1.0
Levenshtypo - a .NET fuzzy matching string dictionary
Levenshtypo is a library which allows you to search large data sets by fuzzy matching the key strings.
The dataset is loaded upfront as a sequence of key-value pairs. Once loaded it allows searching for the values which are up to a certain Levenshtein Distance away from a query string.
Levenshtein Distance is the number of character insertions, deletions or substitutions required to transform one string into another.
Planned Work (Coming Soon)
- Automaton to return edit distance
- State Serialization logic
- Preserialized state machines offered on GitHub
Installation
Install via Nuget.
Getting Started
// Start with a dataset
IEnumerable<KeyValuePair<string, object>> dataset = ...;
// Index the dataset in a levenshtrie. The levenshtrie should be stored for re-use.
Levenshtrie<object> levenshtrie = Levenshtrie<object>.Create(dataset);
// Search the dataset for keys with edit distance 2 from "hello"
object[] results = levenshtrie.Search("hello", 2);
Samples
These samples and more can be found in the samples directory.
<details> <summary>Suggest similar words</summary>
public class TypoSuggestion
{
private readonly Levenshtrie<string> _trie;
public TypoSuggestion(IEnumerable<string> words)
{
_trie = Levenshtrie<string>.Create(
words.Select(w => new KeyValuePair<string, string>(w, w)),
ignoreCase: true);
}
public string[] GetSimilarWords(string word)
{
return _trie.Search(word, maxEditDistance: 2);
}
}
</details>
<details> <summary>Find whether a string matches blacklist</summary>
public class BlacklistDetection
{
private readonly Levenshtrie<string> _trie;
public BlacklistDetection(IEnumerable<string> blacklist)
{
_trie = Levenshtrie<string>.Create(
blacklist.Select(w => new KeyValuePair<string, string>(w, w)),
ignoreCase: true);
}
public bool IsBlacklisted(string word)
{
return _trie.Search(word, maxEditDistance: 1).Contains(word);
}
}
</details>
</details>
<details> <summary>Quickly check whether a list of strings matches an input</summary>
// Benchmarks below show that a naive implementation,
// even if it is well written, is 10x slower than using
// an automaton.
// Benchmark run against English language dataset.
//
// | Method | Mean | Error | StdDev | Allocated |
// |-----------------|-----------:|----------:|----------:|----------:|
// | Using_naive | 103.190 ms | 1.4706 ms | 1.3756 ms | 214 B |
// | Using_automaton | 8.161 ms | 0.0469 ms | 0.0439 ms | 12 B |
public static string[] Search(string searchWord, string[] against)
{
var automaton = LevenshtomatonFactory.Instance.Construct(searchWord, maxEditDistance: 2);
var results = new List<string>();
foreach (var word in against)
{
// Naive version would be:
// bool matches = LevenshteinDistance.Levenshtein(searchWord, word) <= 2;
// Automaton version is:
bool matches = automaton.Matches(word);
if (matches)
{
results.Add(word);
}
}
return results.ToArray();
}
</details>
Limitations
- No lookup by UTF8 byte arrays.
- No support for surrogate character pairs.
- Only ordinal character comparison, whether case sensitive or insensitive.
- Maximum Levenshtein Distance of 3.
Performance
The English Language dataset used in the benchmarks contains approximately 465,000 words.
<details> <summary>Search all English Language with a fuzzy key</summary>
- Naive: Compute Levenshtein Distance against all words.
- Levenshtypo: This library.
- Dictionary: .NET Dictionary which only works for distance of 0.
BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.3880/23H2/2023Update/SunValley3)
AMD Ryzen 9 5950X, 1 CPU, 32 logical and 16 physical cores
.NET SDK 8.0.400-preview.0.24324.5
[Host] : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
DefaultJob : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
Method | Mean | Error | StdDev | Gen0 | Allocated |
---|---|---|---|---|---|
Distance0_Dictionary | 8.623 ns | 0.0761 ns | 0.0712 ns | - | - |
Distance0_Levenshtypo | 597.182 ns | 2.3004 ns | 1.7960 ns | 0.0124 | 208 B |
Distance1_Levenshtypo | 22,879.582 ns | 149.3766 ns | 139.7270 ns | - | 424 B |
Distance2_Levenshtypo | 305,240.260 ns | 2,498.8835 ns | 2,337.4572 ns | - | 1832 B |
Distance3_Levenshtypo | 1,690,603.294 ns | 11,989.1677 ns | 11,214.6749 ns | - | 17905 B |
Distance0_Naive | 862,346.973 ns | 10,007.3755 ns | 8,871.2777 ns | - | 89 B |
Distance1_Naive | 98,747,597.143 ns | 564,828.7729 ns | 500,705.9951 ns | - | 2770 B |
Distance2_Naive | 98,188,072.000 ns | 638,972.9260 ns | 597,695.6714 ns | - | 822 B |
Distance3_Naive | 99,317,118.889 ns | 1,241,670.8616 ns | 1,161,459.6944 ns | - | 4443 B |
</details>
<details> <summary>Load all English Language dataset</summary>
- Levenshtypo: This library.
- Dictionary: .NET Dictionary for comparison.
BenchmarkDotNet v0.13.12, Windows 11 (10.0.22631.3880/23H2/2023Update/SunValley3)
AMD Ryzen 9 5950X, 1 CPU, 32 logical and 16 physical cores
.NET SDK 8.0.400-preview.0.24324.5
[Host] : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
DefaultJob : .NET 8.0.6 (8.0.624.26715), X64 RyuJIT AVX2
Method | Mean | Error | StdDev | Gen0 | Gen1 | Gen2 | Allocated |
---|---|---|---|---|---|---|---|
English_Dictionary | 32,450.80 μs | 647.413 μs | 770.700 μs | 781.2500 | 781.2500 | 781.2500 | 35524.19 KB |
English_Levenshtypo | 282,953.40 μs | 4,376.502 μs | 4,093.783 μs | 27000.0000 | 6000.0000 | 2000.0000 | 527682.66 KB |
</details>
References
The algorithm in this library is based on the 2002 paper Fast String Correction with Levenshtein-Automata by Klaus Schulz and Stoyan Mihov.
I used the following blog posts to further help understand the algorithm.
- http://blog.notdot.net/2010/07/Damn-Cool-Algorithms-Levenshtein-Automata
- https://fulmicoton.com/posts/levenshtein/
I used the following repository to obtain the list of English words, used in tests.
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 is compatible. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
.NET Core | netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
.NET Standard | netstandard2.1 is compatible. |
MonoAndroid | monoandroid was computed. |
MonoMac | monomac was computed. |
MonoTouch | monotouch was computed. |
Tizen | tizen60 was computed. |
Xamarin.iOS | xamarinios was computed. |
Xamarin.Mac | xamarinmac was computed. |
Xamarin.TVOS | xamarintvos was computed. |
Xamarin.WatchOS | xamarinwatchos was computed. |
-
.NETStandard 2.1
- No dependencies.
-
net8.0
- No dependencies.
NuGet packages
This package is not used by any NuGet packages.
GitHub repositories
This package is not used by any popular GitHub repositories.