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LibSVMsharp

LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines. This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.

For more information visit the official libsvm webpage.

How to Install

To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:

PM> Install-Package LibSVMsharp

License

LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.

Example Codes

Simple Classification

SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);

Simple Classification with Extension Methods

SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);

Simple Regression

SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);

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C# wrapper of LibSVM

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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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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
About
C# wrapper of LibSVM
Resources
Readme
MIT license
Activity
Stars
92 stars
Watchers
15 watching
Forks
73 forks
Report repository
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Used by
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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('^' + ".*" + '

Skip to content
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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
About
C# wrapper of LibSVM
Resources
Readme
MIT license
Activity
Stars
92 stars
Watchers
15 watching
Forks
73 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
About
C# wrapper of LibSVM
Resources
Readme
MIT license
Activity
Stars
92 stars
Watchers
15 watching
Forks
73 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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" + '

Skip to content
BranchesTags
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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
About
C# wrapper of LibSVM
Resources
Readme
MIT license
Activity
Stars
92 stars
Watchers
15 watching
Forks
73 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
About
C# wrapper of LibSVM
Resources
Readme
MIT license
Activity
Stars
92 stars
Watchers
15 watching
Forks
73 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
About
C# wrapper of LibSVM
Resources
Readme
MIT license
Activity
Stars
92 stars
Watchers
15 watching
Forks
73 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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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LibSVMsharp
LibSVMsharp is a simple and easy-to-use C# wrapper for Support Vector Machines.
This library uses LibSVM version 3.23 with x64 support, released on 15th of July in 2018.
For more information visit the official libsvm webpage.
How to Install
To install LibSVMsharp, download the Nuget package or run the following command in the Package Manager Console:
PM> Install-Package LibSVMsharp
License
LibSVMsharp is released under the MIT License and libsvm is released under the modified BSD Lisence which is compatible with many free software licenses such as GPL.
Example Codes
Simple Classification
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();parameter.Type=SVMType.C_SVC;parameter.Kernel=SVMKernelType.RBF;parameter.C=1;parameter.Gamma=1;SVMModelmodel=SVM.Train(problem,parameter);double[]target=newdouble[testProblem.Length];for(inti=0;i<testProblem.Length;i++)target[i]=SVM.Predict(model,testProblem.X[i]);doubleaccuracy=SVMHelper.EvaluateClassificationProblem(testProblem,target);
Simple Classification with Extension Methods
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doubleaccuracy=testProblem.EvaluateClassificationProblem(target);
Simple Regression
SVMProblemproblem=SVMProblemHelper.Load(@"dataset_path.txt");SVMProblemtestProblem=SVMProblemHelper.Load(@"test_dataset_path.txt");SVMParameterparameter=newSVMParameter();SVMModelmodel=problem.Train(parameter);double[]target=testProblem.Predict(model);doublecorrelationCoeff;doublemeanSquaredErr=testProblem.EvaluateRegressionProblem(target,outcorrelationCoeff);
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C# wrapper of LibSVM
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