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cgpm

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The aim of this project is to provide a unified probabilistic programming framework to express different models and techniques from statistics, machine learning and non-parametric Bayes. It serves as the primary modeling and inference runtime system for bayeslite, an open-source implementation of BayesDB.

Composable generative population models (CGPM) are a computational abstraction for probabilistic objects. They provide an interface that explicitly differentiates between the sampler of a random variable from its conditional distribution and the assessor of its conditional density. By encapsulating models as probabilistic programs that implement CGPMs, complex models can be built as compositions of sub-CGPMs, and queried in a model-independent way using the Bayesian Query Language.

Installing

Conda

The easiest way to install cgpm is to use the package on Anaconda Cloud. Please follow these instructions.

Manual Build

cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning this repository and following these instructions. It is highly recommended to install cgpm inside of a virtualenv which was created using the --system-site-packages flag.

  1. Install dependencies from apt, listed here.

  2. Retrieve and build the source.

    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.

    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications

CGPMs, and their integration as a runtime system for BayesDB, are described in the following technical report:

Applications of using cgpm and bayeslite for data analysis tasks can be further found in:

Tests

Running ./check.sh will run a subset of the tests that are considered complete and stable. To launch the full test suite, including continuous integration tests, run py.test in the root directory. There are more tests in the tests/ directory, but those that do not start with test_ or do start with disabled_ are not considered ready. The tip of every branch merged into master must pass ./check.sh, and be consistent with the code conventions outlined in HACKING.

To run the full test suite, use ./check.sh --integration tests/. Note that the full integration test suite requires installing the C++ crosscat backend.

License

Copyright (c) 2015-2016 MIT Probabilistic Computing Project

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

About

Library of composable generative population models which serve as the modeling and inference backend of BayesDB.

Topics

bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

25 stars

Watchers

22 watching

Forks

11 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
Repository files navigation
cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 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
Repository files navigation
cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 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
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cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 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
Repository files navigation
cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 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
Repository files navigation
cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 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
Repository files navigation
cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 forks
Report repository
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Used by
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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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cgpm
Build Status
The aim of this project is to provide a unified probabilistic programming
framework to express different models and techniques from statistics, machine
learning and non-parametric Bayes. It serves as the primary modeling and
inference runtime system for bayeslite,
an open-source implementation of BayesDB.
Composable generative population models (CGPM) are a computational abstraction
for probabilistic objects. They provide an interface that explicitly
differentiates between the sampler of a random variable from its conditional
distribution and the assessor of its conditional density. By encapsulating
models as probabilistic programs that implement CGPMs, complex models can be
built as compositions of sub-CGPMs, and queried in a model-independent way
using the Bayesian Query Language.
Installing
Conda
The easiest way to install cgpm is to use the
package on Anaconda Cloud.
Please follow these instructions.
Manual Build
cgpm targets Ubuntu 14.04 and 16.04. The package can be installed by cloning
this repository and following these instructions. It is highly recommended to
install cgpm inside of a virtualenv which was created using the
--system-site-packages flag.
  1. Install dependencies from apt, listed here.
  2. Retrieve and build the source.
    % git clone git@github.com:probcomp/cgpm
% cd cgpm
% pip install --no-deps .

  3. Verify the installation.
    % python -c 'import cgpm'
% cd cgpm && ./check.sh

Publications
CGPMs, and their integration as a runtime system for
BayesDB, are described in the following
technical report:
Applications of using cgpm and bayeslite for data analysis tasks can be further
found in:
Tests
Running ./check.sh will run a subset of the tests that are considered complete
and stable. To launch the full test suite, including continuous integration
tests, run py.test in the root directory. There are more tests in the tests/
directory, but those that do not start with test_ or do start with disabled_
are not considered ready. The tip of every branch merged into master must
pass ./check.sh, and be consistent with the code conventions outlined in
HACKING.
To run the full test suite, use ./check.sh --integration tests/. Note that the
full integration test suite requires installing the C++
crosscat backend.
License
Copyright (c) 2015-2016 MIT Probabilistic Computing Project
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
About
Library of composable generative population models which serve as the modeling and inference backend of BayesDB.
Topics
bayesian-inferencedata-analysismachine-learningprobabilistic-programmingtabular-data
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
25 stars
Watchers
22 watching
Forks
11 forks
Report repository
Releases
Packages
Used by
Contributors
Languages