[mlpack-git] [mlpack] [Proposal]Enhance the class SparseAutoencoder and SoftmaxRegression (#454)

[stereomatchingkiss]

I forgot to said, rather than add a default constructor, I would suggest adding one more constructors , this way the users can reuse the trained parameters to train the model if they want to.

For SparseAutoencder

when user do not want to retrain the model but reuse the trained parameters to get the features

SparseAutoencoder<> sae(""smoke_params_of_sae");

Same as SoftmaxRegression

SoftmaxRegression<> sm("smoke_params_of_sm"); 

This way the users can reuse the trained more easier, I intent to use boost::serialization to store the data of the class, what do you think about it?

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https://github.com/mlpack/mlpack/issues/454#issuecomment-143409277
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