loss functions added for Gradient Boosting

- loss functions relevant to gradient boosting added
I see two approaches:
- Either you hard code numerical values everywhere and write a short comment so it would be possible to make sense out of them in the future.
- You compute the test values in another way. For instance, you can write in this file a small function or macro that computes the square (for the squared_loss) of the difference between the predicted and the actual value. It should be possible to take a similar approach of the other losses. It is sort of funny since that is what the loss function class is for, but there are other reasons why they exist.