Python-weka-wrapper3

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- improved logging output in command-line scripts (eg pww-classifier)
- added sub-command `freeze` to the `pww-packages` command-line tool to output the
currently installed packages in pip-like format (unofficial packages get URL appended)
either to stdout or to text file (eg `requirements.txt`)
- the sub-command `install` of the `pww-packages` command-line tool can process
a requirements text file now as well
- added `pww_version()` function to `weka.core.version` to obtain the installed
version of python-weka-wrapper3
- added `with_graph_support()` function to `weka.core.version` to see whether
python-weka-wrapper3 has support for graphs using pygraphviz
- added `with_plot_support()` function to `weka.core.version` to see whether
python-weka-wrapper3 has plotting support via matplotlib
- added sub-command `bootstrap` to the `pww-packages` command-line tool to output a
Python script that can reinstall python-weka-wrapper3 and all the packages in
another virtual environment or even another machine
- added `is_official_package` function to `weka.core.packages` module to check
whether a package is an official one from the Weka package repository or not

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- the `max_heap_size` parameter of the `start` method from the `weka.core.jvm` module now has an effect

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- added `is_additional_measure_producer` variable, `additional_measures` property and
`additional_measure(str)` method to the `weka.classifiers.Classifier` class,
giving convenient access to classes implementing `weka.core.AdditionalMeasureProducer`
- replaced javabridge with jpype behind the scenes, simplifying native API calls
- removed property `JavaObject.jclasswrapper`
- property `JavaObject.jwrapper` is now deprecated and `JavaObject.jobject` should be used instead
- renamed `JavaArray.new_instance` to `JavaArray.new_array`
- now works on M1/M2 Macs
- updated doc strings
- updated documentation, especially around installation

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- `setup.py` no longer lists python-javabridge with github URL, as it fails to
install via pip otherwise

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- the methods `create_instances_from_lists` and `create_instances_from_matrices` of the
`weka.core.dataset` module can handle missing values now (`None` in case of lists,
`nan` in case of matrices), as well as being able to force columns to be nominal
- added the method `load_csv_file` to the module `weka.core.converters` to provide a more
reliable way of loading CSV files compared to Weka's native `CSVLoader` converter
(uses Python's csv module and then calls `create_instances_from_lists`).
- added `simple_range` library as dependency to make it easier to generate integer list from range strings
- added `weka.core.utils` module with following methods: correlation, variance, normalize
- updated requirements (and install instructions) to make pww3 work with Python 3.11+

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- extracted arpack_combined.jar, core.jar and mtj.jar from weka.jar to make them available under Java 17