Release v0.2.8

• methods create_instances_from_lists and create_instances_from_matrices (module weka.core.dataset) now allow the specification of column names, for input and output variables.

Release v0.2.7

• Added property for attribute indices to DistanceFunction class (module weka.core.distances) (thanks to Martin Trat, #39)
• improved instantiation of classes, avoiding misleading output of exceptions
• JavaArray class (module: weka.core.classes) now has __str__ and __repr__ methods that output classname and size

Release v0.2.6

• upgraded bundled Weka to 3.9.6

Release v0.2.5

• switched to python-javabridge, the new name (fork?) of the javabridge library
• Package.__str__ (weka.core.packages module) method now returns a string rather than printing the name/version
• added to_numpy(...) methods to Instance and Instances classes (module weka.core.dataset) to make it easy to obtain a numpy array from the Weka dataset

Release v0.2.4

• added method help_for to weka.core.classes module to generate a help screen for an weka.core.OptionHandler class using just the classname.
• the to_help method of the weka.core.classes.OptionHandler class now allows to tweak the generated output a bit better (e.g., what sections to output).
• setting window title of Matplotlib is now dependent on version (to avoid deprecation notice being output)
• plot_classifier_errors (module weka.plot.classifiers) now plots the diagonal after adding all the plot data to get the right limits

Release v0.2.3

• added weka.core.distances module for distance functions, with DistanceFunction base class
• added avg_silhouette_coefficient method to weka.clusterers to calculate the average silhouette coefficient

Release v0.2.2

• the Package class of the weka.core.packages module now has a version property to quickly access the version which is stored in the meta-data; the metadata property now returns a proper Python dictionary
• added convenience methods to the weka.core.packages module: install_packages to install more than one package, install_missing_package and install_missing_packages to install one or more packages if missing (can automatically stop the JVM and exit the process), uninstall_packages to remove more than one package in one operation

Release v0.2.1

• the ASEvaluation class in the weka.attribute_selection module now offers the following methods for attribute transformers like PCA: transformed_header, transformed_data, convert_instance
• classes derived from weka.core.classes.JavaObject are now serializable via pickle
• added the method copy_structure to the weka.core.dataset.Instances class to quickly get the header of a dataset
• added the property header to the following classes that returns the training data structure: ASEvaluation, ASSearch, Associator, Classifier, Clusterer, TSForecaster
• methods from weka.core.serialization have been moved into weka.core.classes, with the following methods getting the serialization_ prefix: write, write_all, read, read_all

Release v0.2.0

• classes.new_instance method can take an options list now as well
• added classes.get_enum method to return the instance of a Java enum item
• added classes.new_instance method to create new instance of Java class
• added typeconv.jstring_list_to_string_list method to convert a java.util.List containing strings into a Python list
• added typeconv.jdouble_to_float method to convert a java.lang.Double to a Python float
• in module typeconv renamed methods: string_array_to_list to jstring_array_to_list, string_list_to_array to string_list_to_jarray, double_matrix_to_ndarray to jdouble_matrix_to_ndarray, enumeration_to_list to jenumeration_to_list, double_to_float to float_to_jfloat
• added weka.timeseries module that wraps the timeseriesForecasting Weka package

Release v0.1.16

• upgraded Weka to 3.9.5