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MinHashSharp - A Robust Library for Similarity Estimation

NuGet

MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.

Installation

To incorporate MinHashSharp into your project, choose one of the following methods:

.NET CLI

dotnet add package MinHashSharp

NuGet Package Manager

Install-Package MinHashSharp

For detailed package information, visit MinHashSharp on NuGet.

Key Features

The library currently offers two classes:

MinHash: A probabilistic data structure for computing Jaccard similarity between sets.

MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.

Sample usage

strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1

Multi-threading

The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:

lock(lsh){lsh.Insert("s3",m3);}

Custom hash function

By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.

For example, if you want to use the C# default string hash function:

staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
About
A Robust Library in C# for Similarity Estimation
Topics
deduplicationdeduplication-filterlshlsh-algorithmlsh-implementationminhashstatistics
Resources
Readme
MIT license
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2 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
About
A Robust Library in C# for Similarity Estimation
Topics
deduplicationdeduplication-filterlshlsh-algorithmlsh-implementationminhashstatistics
Resources
Readme
MIT license
Activity
Stars
12 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
About
A Robust Library in C# for Similarity Estimation
Topics
deduplicationdeduplication-filterlshlsh-algorithmlsh-implementationminhashstatistics
Resources
Readme
MIT license
Activity
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Watchers
2 watching
Forks
1 fork
Report repository
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
About
A Robust Library in C# for Similarity Estimation
Topics
deduplicationdeduplication-filterlshlsh-algorithmlsh-implementationminhashstatistics
Resources
Readme
MIT license
Activity
Stars
12 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
About
A Robust Library in C# for Similarity Estimation
Topics
deduplicationdeduplication-filterlshlsh-algorithmlsh-implementationminhashstatistics
Resources
Readme
MIT license
Activity
Stars
12 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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MinHashSharp - A Robust Library for Similarity Estimation
NuGet
MinHashSharp offers a simple lightweight data structure designed to index and estimate Jaccard similarity between sets. Leveraging its robust structure, it has been successfully tested on datasets as large as 60GB, encompassing tens of millions of documents, while ensuring smooth and efficient operations.
Installation
To incorporate MinHashSharp into your project, choose one of the following methods:
.NET CLI
dotnet add package MinHashSharp
NuGet Package Manager
Install-Package MinHashSharp
For detailed package information, visit MinHashSharp on NuGet.
Key Features
The library currently offers two classes:
MinHash: A probabilistic data structure for computing Jaccard similarity between sets.
MinHashLSH: A class for supporting big-data fast querying using an approximate Jaccard similarity threshold.
Sample usage
strings1="The quick brown fox jumps over the lazy dog and proceeded to run towards the other room";strings2="The slow purple elephant runs towards the happy fox and proceeded to run towards the other room";strings3="The quick brown fox jumps over the angry dog and proceeded to run towards the other room";varm1=newMinHash(numPerm:128).Update(s1.Split());varm2=newMinHash(numPerm:128).Update(s2.Split());varm3=newMinHash(numPerm:128).Update(s3.Split());Console.WriteLine(m1.Jaccard(m2));// 0.51varlsh=newMinHashLSH(threshold:0.8,numPerm:128);lsh.Insert("s1",m1);lsh.Insert("s2",m2);Console.WriteLine(string.Join(", ",lsh.Query(m3)));// s1
Multi-threading
The library is entirely thread-safe except for the MinHashLSH.Insert function (and the custom injected hash function, if relevant). Therefore, you can create MinHash objects on multiple threads and query the same MinHashLSH object freely. If you are indexing sets on multiple threads, then just make sure to gain exclusive access to the LSH around every Insert call:
lock(lsh){lsh.Insert("s3",m3);}
Custom hash function
By default, the library uses the Farmhash function introduced by Google for efficiency. For more accurate hashes, one can inject a custom hash function into the MinHash object.
For example, if you want to use the C# default string hash function:
staticuintStringHash(strings)=>(uint)s.GetHashCode();varm=newMinHash(numPerm:128,hashFunc:StringHash).Update(s1.Split());
About
A Robust Library in C# for Similarity Estimation
Topics
deduplicationdeduplication-filterlshlsh-algorithmlsh-implementationminhashstatistics
Resources
Readme
MIT license
Activity
Stars
12 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages