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Text classification - alghoritms comprasion.

Project for machine learning classes at university.
Subject: Systems analysis and decision support methods in Computer Science.

Overview

The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.

  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.

Goal:

Classify each text into one of four topic-related groups based of words present in the text.
Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.
In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.

Results

As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.

Postscript

alt text
Stay tuned for my upcoming projects!

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Mashine learning alghoritms for university classes

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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
Stars
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0 forks
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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
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0 forks
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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
Stars
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0 forks
Report repository
Releases
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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
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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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Text classification - alghoritms comprasion.
Project for machine learning classes at university.

Subject: Systems analysis and decision support methods in Computer Science.
Overview
The lecturers implemented unit tests and other files. The task was to implement missing machine learning alghoritms in content.py file, using only numpy library.
  • to see results, run main.py script,
  • to read more about Naive Bayes visit wikipedia,
  • to read more about k-NN visit (no surprise...) wikipedia.
Goal:
Classify each text into one of four topic-related groups based of words present in the text.

Since we assume that words in one text are unrelated the algorithm is called Naive Bayes.

In case of k-NN alghoritm we measure the distance as the number of words that two text differ by.
Results
As the result of main.py you should see a buch of charts comparing the tested parameters and alghoritms.
Postscript
alt text

Stay tuned for my upcoming projects!
About
Mashine learning alghoritms for university classes
Resources
Readme
Activity
Stars
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Report repository
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