[MRG] Add quantile strategy to DummyRegressor. Fixes #3421.

Hi, it looks like DummyRegressor does not handle sparse y in the current implementation (in master), for example:

```
>>> from sklearn.dummy import DummyRegressor
>>> r = DummyRegressor('median')
>>> import numpy as np
>>> from scipy.sparse import csc_matrix
>>> X = None  # Ignored
>>> y = csc_matrix(np.array([1]))
>>> r.fit(X, y)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "sklearn/dummy.py", line 391, in fit
    self.constant_ = np.reshape(np.median(y, axis=0), (1, -1))
  File "/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py", line 2878, in median
    overwrite_input=overwrite_input)
  File "/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py", line 2769, in _ureduce
    raise IndexError("axis %d out of bounds (%d)" % (axis, a.ndim))
IndexError: axis 0 out of bounds (0)
```

It looks like DummyClassifier supports sparse y (there are some tests for this), but not DummyRegressor.

Would you like me to add support for sparse y to DummyRegressor? Even if not I could change the check you described to use _num_samples so it will work in future when sparse y is supported.