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ealdent edited this page Sep 13, 2010 · 14 revisions

Latent Dirichlet Allocation – Ruby Wrapper

This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.

The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.

Example usage:



require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic

See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.

References

Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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ealdent edited this page Sep 13, 2010
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Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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(function(){
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ealdent edited this page Sep 13, 2010
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14 revisions
Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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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");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ealdent edited this page Sep 13, 2010
·
14 revisions
Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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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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ealdent edited this page Sep 13, 2010
·
14 revisions
Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ealdent edited this page Sep 13, 2010
·
14 revisions
Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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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");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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ealdent edited this page Sep 13, 2010
·
14 revisions
Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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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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ealdent edited this page Sep 13, 2010
·
14 revisions
Latent Dirichlet Allocation – Ruby Wrapper
This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatic cluster documents into topics. The number of topics are chosen beforehand and the topics found are usually fairly intuitive. Details of the implementation can be found in the paper by Blei, Ng, and Jordan.
The original C code relied on files for the input and output. We felt it was necessary to depart from that model and use Ruby objects for these steps instead. The only file necessary will be the data file (in a format similar to that used by SVM^light^. Optionally you may need a vocabulary file to be able to extract the words belonging to topics.
Example usage:


require ‘lda’

lda = Lda::Lda.new # create an Lda object for training

corpus = Lda::Corpus.new(“data/data_file.dat”)

lda.corpus = corpus

lda.em(“random”) # run EM algorithm using random starting points

lda.load_vocabulary(“data/vocab.txt”)

lda.print_topics(20) # print the topic 20 words per topic
See the rdocs for further information. If you have general questions about Latent Dirichlet Allocation, I urge you to use the topic models mailing list, since the people who monitor that are very knowledgeable. If you find any problems specific to lda-ruby, please post an issue on the Github project.
References
Blei, David M., Ng, Andrew Y., and Jordan, Michael I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research. 3 (Mar. 2003), 993-1022. pdf
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