<?xml version="1.0" encoding="utf-8"?>
<?xml-stylesheet type="text/xsl" href="assets/xml/rss.xsl" media="all"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>WEKA Blog</title><link>https://waikato.github.io/weka-blog/</link><description>Stuff around and about WEKA</description><atom:link href="https://waikato.github.io/weka-blog/rss.xml" rel="self" type="application/rss+xml"></atom:link><language>en</language><lastBuildDate>Thu, 19 Dec 2024 19:53:54 GMT</lastBuildDate><generator>Nikola (getnikola.com)</generator><docs>http://blogs.law.harvard.edu/tech/rss</docs><item><title>Wekalist has moved</title><link>https://waikato.github.io/weka-blog/posts/2024-12-20-wekalist-has-moved/</link><dc:creator>FracPete</dc:creator><description>&lt;p&gt;Unfortunately, the university mailman server was being decommissioned and the Wekalist had
to find a new home. Its home is now on Sourceforge.net, where all the Weka releases and
packages are hosted as well:&lt;/p&gt;
&lt;p&gt;&lt;a class="reference external" href="https://sourceforge.net/projects/weka/lists/weka-users"&gt;https://sourceforge.net/projects/weka/lists/weka-users&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;You can find the mailing list archives here from now on:&lt;/p&gt;
&lt;p&gt;&lt;a class="reference external" href="https://sourceforge.net/p/weka/mailman/weka-users/"&gt;https://sourceforge.net/p/weka/mailman/weka-users/&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;Due to the move, you will have to subscribe again before you can post to the list
using the following email address:&lt;/p&gt;
&lt;p&gt;&lt;a class="reference external" href="mailto:weka-users@lists.sourceforge.net"&gt;weka-users@lists.sourceforge.net&lt;/a&gt;&lt;/p&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2024-12-20-wekalist-has-moved/</guid><pubDate>Thu, 19 Dec 2024 19:40:00 GMT</pubDate></item><item><title>python-weka-wrapper3 release using JPype</title><link>https://waikato.github.io/weka-blog/posts/2024-07-16-pww3-release-using-jpype/</link><dc:creator>FracPete</dc:creator><description>&lt;p&gt;Some of you might have come across the python-weka-wrapper3 library, which makes Weka available from Python:&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;&lt;a class="reference external" href="https://github.com/fracpete/python-weka-wrapper3"&gt;https://github.com/fracpete/python-weka-wrapper3&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;
&lt;p&gt;When the project started out, it relied on the javabridge library in order to communicate with Java Virtual Machine
running the Weka process. Unfortunately, that project hasn't been maintained properly for a while now and it made
installation difficult or even impossible for M1/M2 Mac (ARM) users.&lt;/p&gt;
&lt;p&gt;Over the last few weeks, I migrated the code base to using the &lt;a class="reference external" href="https://github.com/jpype-project/jpype"&gt;JPype library&lt;/a&gt;
and updated the documentation accordingly.&lt;/p&gt;
&lt;p&gt;Since the code-based appeared stable (tests and examples worked), I made a new release on Friday last week: &lt;strong&gt;0.3.0&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;There are several good news: &lt;strong&gt;first&lt;/strong&gt;, the installation is much easier; &lt;strong&gt;second&lt;/strong&gt;, ARM Mac users can use it
now as well; &lt;strong&gt;third&lt;/strong&gt;, there were pretty much no interface changes necessary, as all changes happened behind
the scenes (only low-level access to Java objects changed).&lt;/p&gt;
&lt;p&gt;So, if you had problems in the past, you might want to give it a go again.&lt;/p&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2024-07-16-pww3-release-using-jpype/</guid><pubDate>Mon, 15 Jul 2024 23:40:00 GMT</pubDate></item><item><title>Weka moved away from Github</title><link>https://waikato.github.io/weka-blog/posts/2022-08-10-weka-moved-away-from-github/</link><dc:creator>FracPete</dc:creator><description>&lt;p&gt;Due to the size limitations for repositories on Github, Weka has been moved in-house again:&lt;/p&gt;
&lt;p&gt;&lt;a class="reference external" href="https://git.cms.waikato.ac.nz/weka/weka"&gt;https://git.cms.waikato.ac.nz/weka/weka&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;This repository contains all the branches and all commits, not just a subset like the Github ones did in the past.&lt;/p&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2022-08-10-weka-moved-away-from-github/</guid><pubDate>Wed, 10 Aug 2022 01:00:00 GMT</pubDate></item><item><title>New WekaDeeplearning4j Release - CNN explorer, saliency maps, progress manager, and model summaries </title><link>https://waikato.github.io/weka-blog/posts/2020-11-26-wekaDeeplearning4j-1.7.0/</link><dc:creator>Rhys Compton</dc:creator><description>&lt;div&gt;&lt;p&gt;A new version of WekaDeeplearning4j, version 1.7.0, is available!&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2020-11-26-wekaDeeplearning4j-1.7.0/"&gt;Read more…&lt;/a&gt; (2 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2020-11-26-wekaDeeplearning4j-1.7.0/</guid><pubDate>Thu, 26 Nov 2020 06:06:00 GMT</pubDate></item><item><title>New WekaDeeplearning4j Release - Pretrained Models, Feature Extraction Update, and more </title><link>https://waikato.github.io/weka-blog/posts/2020-07-04-wekaDeeplearning4j-1.6.0/</link><dc:creator>Eibe Frank</dc:creator><description>&lt;div&gt;&lt;p&gt;A new version of WekaDeeplearning4j, version 1.6.0, has just been released and brings with it a bunch of exciting new features.&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2020-07-04-wekaDeeplearning4j-1.6.0/"&gt;Read more…&lt;/a&gt; (1 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2020-07-04-wekaDeeplearning4j-1.6.0/</guid><pubDate>Sat, 04 Jul 2020 06:06:00 GMT</pubDate></item><item><title>Weka Debian packages</title><link>https://waikato.github.io/weka-blog/posts/2019-09-23-weka-debian-packages/</link><dc:creator>FracPete</dc:creator><description>&lt;div&gt;&lt;p&gt;Users installing Weka on Linux must have always felt left out
a bit, with no installer available, instead having to deal with
just a ZIP file. For power users, that would not have mattered,
but users new to Linux may have found that a bit more challenging.&lt;/p&gt;
&lt;p&gt;Well, things have changed - at least for users of Debian or
one of its many derivatives like Ubuntu - with the advent
of &lt;a class="reference external" href="https://www.cs.waikato.ac.nz/~ml/weka/snapshots/weka_snapshots.html"&gt;snapshots&lt;/a&gt;
being available for download as &lt;a class="reference external" href="https://en.wikipedia.org/wiki/Deb_%28file_format%29"&gt;Debian packages&lt;/a&gt; (stable 3.8 and developer version).&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2019-09-23-weka-debian-packages/"&gt;Read more…&lt;/a&gt; (2 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2019-09-23-weka-debian-packages/</guid><pubDate>Mon, 23 Sep 2019 01:21:00 GMT</pubDate></item><item><title>Jupyter notebooks (on Windows)</title><link>https://waikato.github.io/weka-blog/posts/2019-04-02-jupyter-windows/</link><dc:creator>Peter Reutemann</dc:creator><description>&lt;div&gt;&lt;p&gt;In the previous &lt;a class="reference external" href="https://waikato.github.io/weka-blog/posts/2019-03-29-jupyter/"&gt;post&lt;/a&gt;, I quickly
described how to set up Jupyter Notebooks to run Java code. Now I want
to explain how to set things up on Windows using
&lt;a class="reference external" href="https://www.anaconda.com/"&gt;Anaconda&lt;/a&gt;. Of course, you have to have
a Java 9 JDK or later installed on your system, as the JDK's &lt;em&gt;JShell&lt;/em&gt;
feature is required to execute the Java code on the fly.&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2019-04-02-jupyter-windows/"&gt;Read more…&lt;/a&gt; (1 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>scripting</category><guid>https://waikato.github.io/weka-blog/posts/2019-04-02-jupyter-windows/</guid><pubDate>Tue, 02 Apr 2019 00:44:00 GMT</pubDate></item><item><title>Jupyter notebooks</title><link>https://waikato.github.io/weka-blog/posts/2019-03-29-jupyter/</link><dc:creator>Peter Reutemann</dc:creator><description>&lt;div&gt;&lt;p&gt;&lt;a class="reference external" href="https://jupyter.org/"&gt;Jupyter notebooks&lt;/a&gt; are extremely popular in the
Python world, simply because it is great to combine documentation and
code in a visually appealing way. Makes it an ideal tool for teaching!&lt;/p&gt;
&lt;p&gt;A recent post on the &lt;a class="reference external" href="https://moa.cms.waikato.ac.nz/how-to-use-jupyter-notebooks-with-moa/"&gt;MOA blog&lt;/a&gt;, demonstrated the &lt;a class="reference external" href="https://github.com/SpencerPark/IJava"&gt;IJava kernel&lt;/a&gt; for executing Java scripts. The kernel relies on the Java JDK 9+ feature
called &lt;em&gt;JShell&lt;/em&gt;, which allows executing Java code without compiling it
first.&lt;/p&gt;
&lt;p&gt;Rather than using &lt;em&gt;mybinder.org&lt;/em&gt;, I was intrigued how easy it would be
to run it on ones local machine. As it turns out, extremely easy!&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2019-03-29-jupyter/"&gt;Read more…&lt;/a&gt; (1 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>scripting</category><guid>https://waikato.github.io/weka-blog/posts/2019-03-29-jupyter/</guid><pubDate>Fri, 29 Mar 2019 01:04:00 GMT</pubDate></item><item><title>Bookmarks support</title><link>https://waikato.github.io/weka-blog/posts/2019-03-01-bookmarks/</link><dc:creator>Peter Reutemann</dc:creator><description>&lt;div&gt;&lt;p&gt;One long-standing annoyance in Weka's user interface was the
opening and saving of files: you needed to click many times
before you reached your destination. The only thing you were
able to modify, for example for the Explorer, was the &lt;em&gt;initial
directory&lt;/em&gt; a file chooser was using (&lt;a class="reference external" href="https://github.com/Waikato/weka-trunk/blob/master/weka/src/main/java/weka/gui/explorer/Explorer.props"&gt;Explorer.props&lt;/a&gt;).&lt;/p&gt;
&lt;p&gt;However, your datasets are usually distributed, especially
when you are working on many projects, which renders a
single starting directory ineffective, unfortunately.&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2019-03-01-bookmarks/"&gt;Read more…&lt;/a&gt; (1 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>gui</category><guid>https://waikato.github.io/weka-blog/posts/2019-03-01-bookmarks/</guid><pubDate>Thu, 28 Feb 2019 23:10:00 GMT</pubDate></item><item><title>Micro averages in multi-class classification</title><link>https://waikato.github.io/weka-blog/posts/2019-02-16-micro_average/</link><dc:creator>Eibe Frank</dc:creator><description>&lt;div&gt;&lt;p&gt;When evaluating multi-class classification models, Weka outputs a weighted average of the per-class precision, recall, and F-measure: it computes these statistics for each class individually, treating the corresponding class as the "positive" class and the union of the other classes as the negative class, and computes a weighted average of these per-class statistics, with a per-class weight that is equal to the proportion of data in that class. A recent question on the Weka mailing list was whether the software also outputs micro-averaged precision, recall, and F-measure for multi-class problems. It turns out that it does! To find out where these can be found, read on.&lt;/p&gt;
&lt;p&gt;&lt;a href="https://waikato.github.io/weka-blog/posts/2019-02-16-micro_average/"&gt;Read more…&lt;/a&gt; (3 min remaining to read)&lt;/p&gt;&lt;/div&gt;</description><category>github</category><guid>https://waikato.github.io/weka-blog/posts/2019-02-16-micro_average/</guid><pubDate>Sat, 16 Feb 2019 01:48:00 GMT</pubDate></item></channel></rss>