Added Median of Medians Algorithm#9864

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Merged
chriso345 merged 5 commits into from
Oct 8, 2023
Merged

Added Median of Medians Algorithm #9864

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82a8273
Added Median of Medians Algorithm
coderbeta1 Oct 5, 2023
68a3fdb
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 5, 2023
c767c66
Update median_of_medians.py as per requested changes
coderbeta1 Oct 6, 2023
016dabb
Merge branch 'TheAlgorithms:master' into mediansAlgo
coderbeta1 Oct 6, 2023
328a2df
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 6, 2023
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107 changes: 107 additions & 0 deletions searches/median_of_medians.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
"""
Return the median of the input list
:param arr: Array to find median of
:return: median of arr

>>> median_of_five([2, 4, 5, 7, 899])
5
>>> median_of_five([5, 7, 899, 54, 32])
32
>>> median_of_five([5, 4, 3, 2])
4
>>> median_of_five([3, 5, 7, 10, 2])
5
"""
arr = sorted(arr)
return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
"""
Return a pivot to partition data on by calculating
Median of medians of input data
:param arr: The data to be checked (a list)
:return: median of medians of input array

>>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
54
>>> median_of_medians([5, 7, 899, 54, 32])
32
>>> median_of_medians([5, 4, 3, 2])
4
>>> median_of_medians([3, 5, 7, 10, 2, 12])
12
"""

if len(arr) <= 5:
return median_of_five(arr)
medians = []
i = 0
while i < len(arr):
if (i + 4) <= len(arr):
medians.append(median_of_five(arr[i:].copy()))
else:
medians.append(median_of_five(arr[i : i + 5].copy()))
i += 5
return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
"""
Two way partition the data into smaller and greater lists,
in relationship to the pivot
:param arr: The data to be searched (a list)
:param target: The rank to be searched
:return: element at rank target

>>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
32
>>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
2
>>> quick_select([5, 4, 3, 2], 2)
3
>>> quick_select([3, 5, 7, 10, 2, 12], 3)
5
"""

# Invalid Input
if target > len(arr):
return -1

# x is the estimated pivot by median of medians algorithm
x = median_of_medians(arr)
left = []
right = []
check = False
for i in range(len(arr)):
if arr[i] < x:
left.append(arr[i])
elif arr[i] > x:
right.append(arr[i])
elif arr[i] == x and not check:
check = True
else:
right.append(arr[i])
rank_x = len(left) + 1
if rank_x == target:
answer = x
elif rank_x > target:
answer = quick_select(left, target)
elif rank_x < target:
answer = quick_select(right, target - rank_x)
return answer


print(median_of_five([5, 4, 3, 2]))
, '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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Added Median of Medians Algorithm#9864

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 Merged

chriso345
merged 5 commits into

from

Oct 8, 2023
 Merged


Added Median of Medians Algorithm
#9864
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5 commits


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82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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107 changes: 107 additions & 0 deletions
searches/median_of_medians.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))

, '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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Added Median of Medians Algorithm#9864

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 Merged

chriso345
merged 5 commits into

from

Oct 8, 2023
 Merged


Added Median of Medians Algorithm
#9864
 Changes from all commits
Commits

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5 commits


Select commit
Hold shift + click to select a range
82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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107 changes: 107 additions & 0 deletions
searches/median_of_medians.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))

, '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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Added Median of Medians Algorithm#9864

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 Merged

chriso345
merged 5 commits into

from

Oct 8, 2023
 Merged


Added Median of Medians Algorithm
#9864
 Changes from all commits
Commits

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5 commits


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82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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107 changes: 107 additions & 0 deletions
searches/median_of_medians.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))

, '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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Added Median of Medians Algorithm#9864

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 Merged

chriso345
merged 5 commits into

from

Oct 8, 2023
 Merged


Added Median of Medians Algorithm
#9864
 Changes from all commits
Commits

Show all changes


5 commits


Select commit
Hold shift + click to select a range
82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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searches/median_of_medians.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))

, '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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Oct 8, 2023
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Added Median of Medians Algorithm
#9864
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82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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107 changes: 107 additions & 0 deletions
searches/median_of_medians.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))

, '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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Added Median of Medians Algorithm#9864

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 Merged

chriso345
merged 5 commits into

from

Oct 8, 2023
 Merged


Added Median of Medians Algorithm
#9864
 Changes from all commits
Commits

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5 commits


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82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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107 changes: 107 additions & 0 deletions
searches/median_of_medians.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))

, '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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Added Median of Medians Algorithm#9864

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 Merged

chriso345
merged 5 commits into

from

Oct 8, 2023
 Merged


Added Median of Medians Algorithm
#9864
 Changes from all commits
Commits

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5 commits


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82a8273

Added Median of Medians Algorithm


coderbeta1 Oct 5, 2023
68a3fdb

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 5, 2023
c767c66

Update median_of_medians.py as per requested changes


coderbeta1 Oct 6, 2023
016dabb

Merge branch 'TheAlgorithms:master' into mediansAlgo


coderbeta1 Oct 6, 2023
328a2df

[pre-commit.ci] auto fixes from pre-commit.com hooks


pre-commit-ci[bot] Oct 6, 2023

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107 changes: 107 additions & 0 deletions
searches/median_of_medians.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -0,0 +1,107 @@
"""
A Python implementation of the Median of Medians algorithm
to select pivots for quick_select, which is efficient for
calculating the value that would appear in the index of a
list if it would be sorted, even if it is not already
sorted. Search in time complexity O(n) at any rank
deterministically
https://en.wikipedia.org/wiki/Median_of_medians
"""


def median_of_five(arr: list) -> int:
 """
 Return the median of the input list
 :param arr: Array to find median of
 :return: median of arr

 >>> median_of_five([2, 4, 5, 7, 899])
 5
 >>> median_of_five([5, 7, 899, 54, 32])
 32
 >>> median_of_five([5, 4, 3, 2])
 4
 >>> median_of_five([3, 5, 7, 10, 2])
 5
 """
 arr = sorted(arr)
 return arr[len(arr) // 2]


def median_of_medians(arr: list) -> int:
 """
 Return a pivot to partition data on by calculating
 Median of medians of input data
 :param arr: The data to be checked (a list)
 :return: median of medians of input array

 >>> median_of_medians([2, 4, 5, 7, 899, 54, 32])
 54
 >>> median_of_medians([5, 7, 899, 54, 32])
 32
 >>> median_of_medians([5, 4, 3, 2])
 4
 >>> median_of_medians([3, 5, 7, 10, 2, 12])
 12
 """

 if len(arr) <= 5:
 return median_of_five(arr)
 medians = []
 i = 0
 while i < len(arr):
 if (i + 4) <= len(arr):
 medians.append(median_of_five(arr[i:].copy()))
 else:
 medians.append(median_of_five(arr[i : i + 5].copy()))
 i += 5
 return median_of_medians(medians)


def quick_select(arr: list, target: int) -> int:
 """
 Two way partition the data into smaller and greater lists,
 in relationship to the pivot
 :param arr: The data to be searched (a list)
 :param target: The rank to be searched
 :return: element at rank target

 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 5)
 32
 >>> quick_select([2, 4, 5, 7, 899, 54, 32], 1)
 2
 >>> quick_select([5, 4, 3, 2], 2)
 3
 >>> quick_select([3, 5, 7, 10, 2, 12], 3)
 5
 """

 # Invalid Input
 if target > len(arr):
 return -1

 # x is the estimated pivot by median of medians algorithm
 x = median_of_medians(arr)
 left = []
 right = []
 check = False
 for i in range(len(arr)):
 if arr[i] < x:
 left.append(arr[i])
 elif arr[i] > x:
 right.append(arr[i])
 elif arr[i] == x and not check:
 check = True
 else:
 right.append(arr[i])
 rank_x = len(left) + 1
 if rank_x == target:
 answer = x
 elif rank_x > target:
 answer = quick_select(left, target)
 elif rank_x < target:
 answer = quick_select(right, target - rank_x)
 return answer


print(median_of_five([5, 4, 3, 2]))