1) Factorizing into a CalibratedClassifier that calibrates a fitted predictor
   on the single set provided in fit(), and CalibratedClassifierCV that does
   the calibration on cv, calling CalibratedClassifier on each fold
2) Changing averaging log probabilities to averaging the raw probabilities
3) A simple test to test CalibratedClassifier.

TODO:
1) More tests, espeically multi-class
2) I don't like the implicit way to handle the binary/multiclass cases
3) The multi-class case could be handled by a single calibrator that shares the
   calibration parameters across the k one-agains-all problems. Works well for
   balanced problems.