Release v0.3.16

• added console scripts: pww-associator, pww-attsel, pww-classifier, pww-clusterer, pww-datagenerator, pww-filter
• added serialize, deserialize methods to weka.classifiers.Classifier to simplify loading/saving model
• added serialize, deserialize methods to weka.clusterers.Clusterer to simplify loading/saving model
• added serialize, deserialize methods to weka.filters.Filter to simplify loading/saving filter
• added methods plot_rocs and plot_prcs to weka.plot.classifiers module to plot ROC/PRC curve on same dataset
  for multiple classifiers
• method plot_classifier_errors of weka.plot.classifiers module now allows plotting predictions of multiple
  classifiers by providing a dictionary
• method create_instances_from_matrices from module weka.core.dataset now allows string and bytes as well
• method create_instances_from_lists from module weka.core.dataset now allows string and bytes as well

Release v0.3.15

• added wrapper classes for association classes that implement AssociationRuleProducer
  (package weka.associations): AssociationRules, AssociationRule, item
• added to_source method to weka.classifiers.Classifier and weka.filters.Filter
  (underlying Java classes must implement the respective Sourcable interface)

Release v0.3.14

• fixed logging setup in weka.core.jvm to avoid global setting global logging
  setup to DEBUG (thanks to https://github.com/Arnie97)

Release v0.3.13

• upgraded to Weka 3.9.3
• weka.jar now included in PyPi package
• exposed the following methods in weka.classifiers.Evaluation:
  cumulative_margin_distribution, sf_prior_entropy, sf_scheme_entropy

Release v0.3.12

• upgraded to Weka 3.9.2
• properly initializing package support now, rather than adding package jars to classpath
• added weka.core.ClassHelper Java class for obtaining classes and static fields, as
  javabridge only uses the system class loader

Release v0.3.11

• added check_for_modified_class_attribute method to FilterClassifier class
• added complete_classname method to weka.core.classes module, which allows completion of partial classnames like .J48 to weka.classifiers.trees.J48
  if there is a unique match; JavaObject.new_instance and JavaObject.check_type now make use of this functionality, allowing for instantiations like Classifier(cls=".J48")
• jvm.start(system_cp=True) no longer fails with a KeyError: 'CLASSPATH' if there is no CLASSPATH environment variable defined
• Libraries mtl.jar, core.jar and arpack_combined_all.jar were added as is to the weka.jar in the 3.9.1 release instead of adding their content to it. Repackaged weka.jar to fix this issue (#52)

Release v0.3.10

• types.double_matrix_to_ndarray no longer assumes a square matrix
  (#48)
• len(Instances) now returns the number of rows in the dataset (module weka.core.dataset)
• added method insert_attribute to the Instances class
• added class method create_relational to the Attribute class
• upgraded Weka to 3.9.1

Release v0.3.9

• plot_learning_curve method of module weka.plot.classifiers now accepts a list of test sets;
  * is index of test set in label template string
• added missing_value() methods to weka.core.dataset module and Instance class
• output variable y for convenience method create_instances_from_lists in module
  weka.core.dataset is now optional
• added convenience method create_instances_from_matrices to weka.core.dataset module to easily create
  an Instances object from numpy matrices (x and y)

Release v0.3.8

• works with javabridge 1.0.14 now

Release v0.3.7

• upgraded Weka to 3.9.0