[MRG] Add decision_function for OneVsOneClassifier.

Adding `decision_function` support for the `OneVsOneClassifier`.

This is based off of #1548 and addresses #1523.

- [x] Implement the `decision_function` for `OneVsOneClassifier` 

decision_function returns `votes + scores * (0.4 / max(|scores.max(axis=None)|, |scores.min(axis=None)|))`

As a corner case, when both max and min are same, it simply returns `votes`.

- [x] Add tests for ovo decision function
* Tests `decision_function(X).argmax(axis=1) == predict(X)`
* Tests `decision_function(X).shape == (n_samples, n_classes)`
* Tests `np.round(decision_function(X)) == separately_computed_votes`
* Tests `computed_scores = (decision_function(X) - np.round(decision_function(X)) ) * upscale_factor_derived_from_computed_scores `

- [x] Incorporate the `decision_function` output in `test_ovo_ties`
`ovo_ties` test requires `votes` to assert tie and `scores` to assert if the prediction breaks ties using aggregated decision scores. Both `votes` and `scores` ( individual ) were separately computed till now. These are modified to use `decision_function`
* `votes = round(decision_values)`
* `scaled_aggregated_scores = decision_values - votes`

@arjoly, @amueller, @mblondel  Pl. review when you find time!
Umm. I meant below this, https://github.com/scikit-learn/scikit-learn/pull/3891/files#diff-488a2ffa6f9aae5929da81cb89a384a3R526

Something like, "The aggregated scores are scaled and added to the votes so as to break the ties in case of equal number of votes."

However I'm still not sure the `decision_function` should be modified to return to this. I defer to others to vote on this.