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
Also I have some doubts about the name of the function. If I understand correctly, this just builds class-uniform sample weights that correspond to the given class_weight, right?

Will ELM support user-specified sample weights in the future? If so, this class should also get a `sample_weight` parameter that it will reweight by the class weights, right? Is that left for a different PR?