BranchesTags

Latest commit

History

48 Commits

Folders and files

NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py

Repository files navigation

PyMarkovChain

PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.
To use it, you can simply do

from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk. This entails that you have to use the same version of python to store the data and to restore the data, as pickle is one of those things that have changed from python2 to python3.

See also code on github and PyPI page. To install, pip install PyMarkovChain

About

Simple markov chain implementation in python

Resources

Readme
License
Activity

Stars

97 stars

Watchers

6 watching

Forks

16 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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" + '

Skip to content
BranchesTags
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
BranchesTags
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 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('^' + ".*" + '

Skip to content
BranchesTags
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 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
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 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
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 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
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 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); }
})();
})();

Skip to content
BranchesTags
Latest commit
History
48 Commits
Folders and files
NameName
Last commit message
Last commit date
pymarkovchain
pymarkovchain
CHANGES.txt
CHANGES.txt
LICENSE.txt
LICENSE.txt
MANIFEST.in
MANIFEST.in
README.md
README.md
setup.py
setup.py
Repository files navigation
PyMarkovChain
PyMarkovChain supplies an easy-to-use implementation of a markov chain text generator.

To use it, you can simply do
from pymarkovchain import MarkovChain
# Create an instance of the markov chain, tell it where to load / save its database
mc = MarkovChain("./markov")
# generate the markov chain's language model
mc.generateDatabase("This is a string of Text. It won't generate an interesting database though.")
mc.generateString()

To store its data, PyMarkovChain simply uses pickle to dump all of its data to disk.
This entails that you have to use the same version of python to store the data and to
restore the data, as pickle is one of those things that have changed from python2 to python3.
See also code on github and PyPI page.
To install, pip install PyMarkovChain
About
Simple markov chain implementation in python
Resources
Readme
License
Activity
Stars
97 stars
Watchers
6 watching
Forks
16 forks
Report repository
Releases
Packages
Used by
Contributors
Languages