GSOC 2019: Adding multiclassification to Machine Learning backends

gsoc19_multiclassification.gist

This project involves adding multi-classification capabilities to the Moodle Machine Learning backends. 
The current prediction processor API is limited to supervised learning binary classifiers. 
Some models will require multiple classes to be able to predict if a student will receive a grade which will be 'very low',
'low', 'normal', 'high' or 'very high'.

This involved two parts:
[ ] Adjusting the PHP Moodle core code to work with multi-class cases. Also, adding unit tests for all the changes. 
	Squashed commit with all the changes introduced can be found at: 
	https://github.com/valadhi/moodle/commit/093b99efb7e689691ebd1c1d85ee9d3d1552d822
[ ] Setting up the python Tensorflow backend to accept training, prediction and evaluation of multi-class datasets. 
	Squashed commit with all changes can be found at: 
	https://github.com/valadhi/moodle-mlbackend-python/commit/fd46117537bb4507081008f1bdf0a009dcb89e06
	
The project is almost ready for integration. The PHP part has passed the automated checks on the tracker and is 
waiting for peer review: https://tracker.moodle.org/browse/MDL-58992. The python backend requires regression testing 
to make sure nothing changed as a result of the added code.