Add unsupervised methods

[perellonieto]

Some easy to add tools could be density estimation models that learn the distribution of the data without any labels.

Also, one class classification models, novelty or anomaly detection algorithms.

[tdiethe]

As a general note, possibly you could support the base classes in sklearn (ClassifierMixin, RegressorMixin) rather than individual methods, and then the actual methods could be passed as a parameter (possibly a string, although that's ugly) which would mean less work on your end.

[perellonieto]

Commit 3260fe6 adds a tool for anomaly detection. That is, it uses a model that can be trained in an unsupervised way and generates scores at every stream. Because the model is passed as a parameter, it is not only anomaly detection but could be density estimation, outlier detection, clustering, or any other unsupervised method. The tool should be refactored in order to indicate that.

[perellonieto]

Open #3 to refactor the anomaly detection tool into unsupervised learning.