GSoC 2014: Standard Extreme Learning Machines

Finished implementing the standard extreme learning machines (ELMs). I am getting the following results with 550 hidden neurons against the digits datasets,

Training accuracy using the logistic activation function: 0.999444
Training accuracy using the tanh activation function: 1.000000

Fortunately, this algorithm is much easier to implement and debug than multi-layer perceptron :).
I will push a test file soon.

@ogrisel , @larsmans
If I make `_recursive_lsqr` as a **function** wouldn't I need to pass many parameters?
Do you suggest I compute the hidden activations first then pass it to `_recursive_lsqr` along with `n_hidden` and `n_output_` or is passing `self` possible? 

Also, I am thinking to re-use, as you said, `.fit` for `.partial_fit` but that would require `_warm_start` which prevents `elm` from  re-initializing the two variables, `_recursive_var` and `coef_output_`. 
I also realized that this is necessary, because `partial_fit` must take advantage of using batches which is currently only available for `.fit`. With this I could also **inline** `_recursive_lsqr` :).

What do you think?
Thanks.