معرفی شرکت ها
cocopp-2.6
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Benchmarking framework for all types of black-box optimization algorithms, postprocessing.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | cocopp-2.6 |
| نام | cocopp |
| نسخه کتابخانه | 2.6 |
| نگهدارنده | ['Dimo Brockhoff and Nikolaus Hansen'] |
| ایمیل نگهدارنده | ['dimo.brockhoff@inria.fr'] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/numbbo/coco |
| آدرس اینترنتی | https://pypi.org/project/cocopp/ |
| مجوز | BSD |
# COmparing Continuous Optimisers (COCO) post-processing
The ([`cocopp`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.html)) package takes data generated with the [COCO framework](https://github.com/numbbo/coco) to compare continuous opitmizers and produces output figures and tables in <tt class="rst-docutils literal">html</tt> format and for including into LaTeX-documents.
## Installation
pip install cocopp
## Usage
The main method of the [`cocopp`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.html) package is [`main`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.rungeneric.html#main) (currently aliased to [`cocopp.rungeneric.main`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.rungeneric.html#main)). The [`main`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.rungeneric.html#main) method also allows basic use of the post-processing through a shell command-line interface. The recommended use is however from an IPython/Jupyter shell or notebook:
<pre class="py-doctest"><span class="py-prompt">>>></span> <span class="py-keyword">import</span> cocopp
<span class="py-prompt">>>></span> cocopp.main(<span class="py-string">'exdata/my_output another_folder yet_another_or_not'</span>) <span class="py-comment"></span></pre>
postprocesses data from one or several folders, for example data generated with the help from the [`cocoex`](https://numbbo.github.io/gforge/apidocs-cocoex) module. Each folder should contain data of a full experiment with a single algorithm. (Within the folder the data can be distributed over subfolders). Results can be explored from the <tt class="rst-docutils literal">ppdata/index.html</tt> file, unless a a different output folder is specified with the <tt class="rst-docutils literal"><span class="pre">-o</span></tt> option. **Comparative data** from over 200 full experiments are archived online and can be listed, filtered, and retrieved from [`cocopp.archives`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.archives.html) (of type [`OfficialArchives`](https://numbbo.github.io/gforge/apidocs-cocopp/cocopp.archiving.OfficialArchives.html)) and processed alone or together with local data. For example
<pre class="py-doctest"><span class="py-prompt">>>></span> cocopp.archives.bbob(<span class="py-string">'bfgs'</span>) <span class="py-comment"></span>
<span class="py-output">['2009/BFGS_...</span></pre>
lists all data sets run on the `bbob` testbed containing <tt class="rst-docutils literal">'bfgs'</tt> in their name. The first in the list can be postprocessed by
<pre class="py-doctest"><span class="py-prompt">>>></span> cocopp.main(<span class="py-string">'bfgs!'</span>) <span class="py-comment"></span></pre>
All of them can be processed like
<pre class="py-doctest"><span class="py-prompt">>>></span> cocopp.main(<span class="py-string">'bfgs*'</span>) <span class="py-comment"></span></pre>
Only a trailing `*` is accepted and any string containing the substring is matched. The postprocessing result of
<pre class="py-doctest"><span class="py-prompt">>>></span> cocopp.main(<span class="py-string">'bbob/2009/*'</span>) <span class="py-comment"></span></pre>
can be browsed at [https://numbbo.github.io/ppdata-archive/bbob/2009-all](https://numbbo.github.io/ppdata-archive/bbob/2009-all). To display algorithms in the background, the <tt class="rst-docutils literal">genericsettings.background</tt> variable needs to be set:
<pre class="py-doctest"><span class="py-prompt">>>></span> cocopp.genericsettings.background = {<span class="py-builtin">None</span>: cocopp.archives.bbob.get_all(<span class="py-string">'bfgs'</span>)} <span class="py-comment"></span></pre>
where [`None`](http://docs.python.org/library/constants.html#None) invokes the default color (grey) and line style (solid) <tt class="rst-docutils literal">genericsettings.background_default_style</tt>. Now we could compare our own data with the first <tt class="rst-docutils literal">'bfgs'</tt>-matching archived algorithm where all other archived BFGS data are shown in the background with the command
<pre class="py-doctest"><span class="py-prompt">>>></span> cocopp.main(<span class="py-string">'exdata/my_output bfgs!'</span>) <span class="py-comment"></span></pre>
نیازمندی
| مقدار | نام |
|---|---|
| >=1.7 | numpy |
| >=3.1 | matplotlib |
نحوه نصب
نصب پکیج whl cocopp-2.6:
pip install cocopp-2.6.whl
نصب پکیج tar.gz cocopp-2.6:
pip install cocopp-2.6.tar.gz