Download nz.ac.waikato.cms.weka.thirdparty JAR files with all dependencies
default-value-plugin from group org.andromda.thirdparty.jaxb2_commons (version 1.1)
This is an XJC plugin to set default values in the XJC generated classes based on the default attribute to
<xs:element>. Note that JAXB handles defaults for <xs:attribute> natively, so this plugin is not
necessary for an attribute default.
The plugin is particularly useful while generating Value Objects for a user interface from an XML schema. User
interface tags such as the Struts HTML tags use reflection on bean properties to render themselves, so it is
often useful to have a set of sensible defaults set in the Java Beans mapping to the user interface themselves.
Group: org.andromda.thirdparty.jaxb2_commons Artifact: default-value-plugin
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adamsflow2docker from group nz.ac.waikato.cms.adams (version 0.0.2)
Library for generating Docker images with an ADAMS workflow running inside.
booleangetter from group org.andromda.thirdparty.jaxb2_commons (version 1.0)
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instant-adams from group nz.ac.waikato.cms.adams (version 0.1.5)
Tool for bootstrapping ADAMS applications just by supplying a list of modules.
jaxb2_commons-ant from group org.andromda.thirdparty.jaxb2_commons (version 0.4.1.5)
Group: org.andromda.thirdparty.jaxb2_commons Artifact: jaxb2_commons-ant
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android-matrix-algorithms from group nz.ac.waikato.cms.adams (version 0.0.3)
Java library of precomputed matrix algorithms aimed to be used on Android.
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annotate from group org.andromda.thirdparty.jaxb2_commons (version 0.4.1.5)
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moa-kafka from group nz.ac.waikato.cms.moa (version 2024.07.0)
Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
This artifact enables you to stream data to MOA from Kafka.
jaxb from group org.andromda.thirdparty.jaxb2_commons (version 2.1.10.1)
Group: org.andromda.thirdparty.jaxb2_commons Artifact: jaxb
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weka-package from group nz.ac.waikato.cms.moa (version 2024.07.0)
Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
This artifact enables you to use MOA from within WEKA.
moa from group nz.ac.waikato.cms.moa (version 2024.07.0)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
localOutlierFactor from group nz.ac.waikato.cms.weka (version 1.0.4)
A filter that applies the LOF (Local Outlier Factor) algorithm to compute an outlier score for each instance in the data. Can use multiple cores/cpus to speed up the LOF computation for large datasets. Nearest neighbor search methods and distance functions are pluggable.
For more information, see:
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.
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moa-pom from group nz.ac.waikato.cms.moa (version 2024.07.0)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
Group: nz.ac.waikato.cms.moa Artifact: moa-pom
There is no JAR file uploaded. A download is not possible! Please choose another version.
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wekaPython from group nz.ac.waikato.cms.weka (version 1.0.18)
Integration with CPython for Weka. Python version 2.7.x or higher is required. Also requires the following packages to be installed in python: numpy, pandas, matplotlib and scikit-learn. This package provides a wrapper classifier and clusterer that, between them, cover 60+ scikit-learn algorithms. It also provides a general scripting step for the Knowlege Flow along with scripting plugin environments for the Explorer and Knowledge Flow.
tiny-weka from group nz.ac.waikato.cms.weka (version 3.9.15955)
The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This artifact represents the bare API of the developer version,
with no package manager, PMML, XML or user interface. It is aimed at commercial
applications that license some of WEKA's algorithms.
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