v0.0.3

Added Features

Added n_estimators to LifelongClassificationForests add_{task, transformer} functions. This allows each task to have a different number of trees.
Previously, the classes in forest.py used finite_sample_correction with the only possible value of kappa being 1. We added the ability to set any kappa.

Previously, the default values of all model parameters to LifelongClassification{Forest, Network} add_{task, transformer} functions were None. The value of None indicated to use the default model parameter specified in the instantiation of the LifelongClassification{Forest, Network}. But, this meant that a user would be unable to train a new tree transformer to purity (by setting max_depth = None) unless default_max_depth was None. This is an undesirable restriction. So, we changed the default value of all model parameters to LifelongClassification{Forest, Network} add_{task, transformer} functions to the string "default" - this indicates to use the default model parameter specified in the instantiation of the LifelongClassification{Forest, Network}.
Updated selection of {transformer, voter, decider} data to be without replacement.
Previously, we were encountering issues when the voters across bags would output posteriors of different lengths (caused by only training on a subset of the classes). We manually fixed these to ensure that all voters across bags output the same number of classes, by appending 0's to missing classes in the voter posterior estimates.
Type checked all inputs X, y to {network, forest}.py classes.

Previously, many of the docs did not render correctly in Sphinx because they were formatted incorrectly. We fixed this formatting error and now all documentation renders correctly in Sphinx (and thus the web docs).

transformer_voter_decider_split was changed to network_construction_proportion in network.py
Minimized documentation and Python Package to only expose well-documented, tested functions that are described in the paper