[WIP] Sparse Target Dummy Classifier

- [x] Test sparse with all four strategies 
- [x] Fit with sparse target
- [x] Predict sparse target
- [ ] Simplify (skip steps) when constant strategy is used
- [x] Clean up redundancies of indicies, data, indptr in predict
- [x] Clean up conditional redundancies in fit 
- [ ] Random sampling function outputs sparse column
- [ ] Test random sampling function with simulation
Maybe instead of classes, you could but n_classes : a numpy array of size (n_ouputs). The number of classes of each output.