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OccamBayesian · Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge

Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.

Dependencies

Install the BayesianOptimization package and the file_read_backwards package by

> pip3 install bayesian-optimization
> pip3 install file_read_backwards

Simple usage

Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run

> python3 occam_bayesian.py

2D optimization example

By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as

"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)" franke_function

Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.

> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |

Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).

> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |

The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.

> python3 plot_logs.py path/to/logs

2d_example

The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.

1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.

About

Bayesian optimization applied to OCCAM hPF simulations.

Topics

bayesian-optimizationhybrid-particle-fieldoccampython3

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1 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} 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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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
About
Bayesian optimization applied to OCCAM hPF simulations.
Topics
bayesian-optimizationhybrid-particle-fieldoccampython3
Resources
Readme
Activity
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Watchers
1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
About
Bayesian optimization applied to OCCAM hPF simulations.
Topics
bayesian-optimizationhybrid-particle-fieldoccampython3
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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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('^' + ".*" + '

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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
About
Bayesian optimization applied to OCCAM hPF simulations.
Topics
bayesian-optimizationhybrid-particle-fieldoccampython3
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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" + '

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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
About
Bayesian optimization applied to OCCAM hPF simulations.
Topics
bayesian-optimizationhybrid-particle-fieldoccampython3
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
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, 'i');
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
About
Bayesian optimization applied to OCCAM hPF simulations.
Topics
bayesian-optimizationhybrid-particle-fieldoccampython3
Resources
Readme
Activity
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1 watching
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0 forks
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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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OccamBayesian
·
Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.Codacy Badge
Bayesian optimization applied to OCCAM hybrid particle-field simulations, built on top of fmfn/BayesianOptimization.
Dependencies
Install the BayesianOptimization package and the file_read_backwards package by
> pip3 install bayesian-optimization
> pip3 install file_read_backwards
Simple usage
Set the OCCAM_PATH in occam_bayesian.py to wherever the OCCAM executable is located, and run
> python3 occam_bayesian.py
2D optimization example
By default, the occam_bayesian_2d.py script uses Franke's bivariate test function1, described as
"This surface consists of two Gaussian peaks and a sharper Gaussian dip superimposed on a surface sloping toward the first quadrant. The latter was included mainly to enhance the visual aspects of the surface (...)"
franke_function
Running occam_bayesian_2d.py runs a series of optimization steps, with a default of 10 initial (random) ones before the Gaussian process is used for subsequent point suggestions.
> python3 occam_bayesian_2d.py Logging to logfile: /current/working/directory/logs/log_0.json
| iter | target | x | y |
-------------------------------------------------
| 1 | -1.84 | 0.9081 | -0.1615 || 2 | -0.5958 | 0.1638 | -0.1437 || 3 | -1.052 | 0.8944 | 0.3331 || 4 | -0.3604 | 0.1154 | 0.03668 || 5 | -1.367 | 0.09501 | 0.7301 || 6 | -1.403 | 0.1466 | 0.739 || 7 | -1.722 | 0.8219 | -0.1401 || 8 | -1.733 | 0.8275 | -0.1042 || 9 | -1.011 | 0.4294 | 0.4017 || 10 | -1.677 | 0.9369 | 0.1053 |
=================================================
| iter | target | x | y |
-------------------------------------------------
| 11 | -2.144 | 1.0 | 1.0 || 12 | -0.5517 | 0.0 | -0.0719 |
Subsequent runs will continue where this left off, by loading any files found in the current working directory, or the logs/ directory (relative to the current working directory).
> python3 occam_bayesian_2d.py
Logging to logfile: /current/working/directory/logs/log_1.json
Loading previous runs from logfile(s):
/current/working/directory/logs/log_0.json
...
| iter | target | x | y |
-------------------------------------------------
| 13 | -0.5076 | 0.0 | 0.2673 || 14 | -0.1863 | 0.257 | 0.1143 |
The optimization process can be visualized by running the plot_logs.py script. By default, this attempts to find logs in the current working directory {or if that fails, the relative logs/ directory}). You can also specify a directory to look for log files manually on the command line, i.e.
> python3 plot_logs.py path/to/logs
2d_example
The plot shows the mean of the Gaussian process (top) and the standard deviation (bottom), with the actual measurements indicated as black dots. The last performed measurement is given by the cross hairs. It is possible to inspect the optimization during any iteration, using the slider at the bottom.
1: Franke, Richard. A critical comparison of some methods for interpolation of scattered data. Monterey, California: Naval Postgraduate School, 1979.
About
Bayesian optimization applied to OCCAM hPF simulations.
Topics
bayesian-optimizationhybrid-particle-fieldoccampython3
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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