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- Description:
A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls. Offers all major APIs, like data generators, loaders, savers, filters, classifiers, clusterers, attribute selection, associations and experiments. Weka packages can be listed/installed/uninstalled as well. It does not provide any graphical frontend, but some basic plotting and graph visualizations are available through matplotlib and pygraphviz.
- Changes to previous version:
- added convenience methods "no_class" (to unset class) and "has_class" (class set?) to "Instances" class
- switched to using faster method objects for methods "classify_instance"/"distribution_for_instance" in "Classifier" class
- switched to using faster method objects for methods "cluster_instance"/"distribution_for_instance" in "Clusterer" class
- switched to using faster method objects for methods "class_index", "is_missing", "get/set_value", "get/set_string_value", "weight" in "Instance" class
- switched to using faster method objects for methods "input", "output", "outputformat" in "Filter" class
- switched to using faster method objects for methods "attribute", "attribute_by_name", "num_attributes", "num_instances", "class_index", "class_attribute", "set_instance", "get_instance", "add_instance" in "Instances" class
- BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Arff, Csv, Libsvm, Xrff
- Tags: Machine Learning, Weka
- Archive: download here
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