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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta

If you want a deep dive into the theory behind the Spatial Pooler:

HTM School:https://invidio.us/watch?v=R5UoFNtv5AU

https://invidio.us/watch?v=rHvjykCIrZM

Numenta Homepage(s):https://numenta.com/https://numenta.org/

Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/

How to use this

If you are in this Folder:

fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)

The most important method in the SpatialPooler class is SpatialPooler.run(). It takes a numpy bool array, that represents a SDR and returns another numpy bool array that is the output of the SP.

The SP always learns.

More examples and documentation in the .ipynb files in the examples folders.

The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'

As you may have noted, the description of this Repository is State -> Potential Pool -> Permanence -> Activation -> State', but what does this mean? If your are more into SP Theory, you may noted that this is a picture of the SP Algorithm. First there is a State, a SDR that is also the input to the SP. And then for each column in the SP, the Algorithm first ignores those parts of the state that are not in the potential pool, then those for which the permanence is less than a threshhold. After these two extractions, the algorithm counts the active parts of the state left and tests again whether that count is greater than a threshhold. If so, the collumn upon which these steps where executed is activated and the output is activated.

About

State -> Potential Pool -> Permanence -> Activation -> State'

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

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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
htmhtm-theorynumentanumpyspatialpooler
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
0 watching
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Report repository
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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); }
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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
htmhtm-theorynumentanumpyspatialpooler
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
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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('^' + ".*" + '

Skip to content
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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
htmhtm-theorynumentanumpyspatialpooler
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
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0 forks
Report repository
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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";
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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
htmhtm-theorynumentanumpyspatialpooler
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
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Report repository
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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); }
})();
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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
htmhtm-theorynumentanumpyspatialpooler
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
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Report repository
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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); }
})();
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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
htmhtm-theorynumentanumpyspatialpooler
Resources
Readme
GPL-3.0 license
Activity
Stars
4 stars
Watchers
0 watching
Forks
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Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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Spatial Pooler - Simple and minimal implementation of the Spatial Pooler by Numenta
If you want a deep dive into the
theory behind the Spatial Pooler:
HTM School:https://invidio.us/watch?v=R5UoFNtv5AU
https://invidio.us/watch?v=rHvjykCIrZM
Numenta Homepage(s):https://numenta.com/https://numenta.org/
Blog post:https://numenta.org/blog/2016/02/19/mathmatical-formalization-of-htm-spatial-pooler/
How to use this
If you are in this Folder:
fromspatial_poolerimportSpatialPooler, overlapimportnumpyasnpsp=SpatialPooler(50,100)
output=sp.run(np.random.randn(50) >0)
print(output)
The most important
method in the
SpatialPooler class
is SpatialPooler.run().
It takes a numpy bool array,
that represents a SDR and
returns another numpy bool
array that is the output
of the SP.
The SP always learns.
More examples and documentation
in the .ipynb files in the
examples folders.
The Architecure and meaning of State -> Potential Pool -> Permanence -> Activation -> State'
As you may have noted, the description
of this Repository is State -> Potential Pool -> Permanence -> Activation -> State',
but what does this mean?
If your are more into SP Theory, you may noted that this is
a picture of the SP Algorithm.
First there is a State, a SDR that is
also the input to the SP.
And then for each column in the SP,
the Algorithm first ignores those
parts of the state that are not in
the potential pool, then those
for which the permanence is less
than a threshhold.
After these two extractions,
the algorithm counts the active
parts of the state left and
tests again whether that count
is greater than a threshhold.
If so, the collumn upon which these
steps where executed is activated
and the output is activated.
About
State -> Potential Pool -> Permanence -> Activation -> State'
Topics
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