ftrlprox.default: FTRL Proximal for matrix class
In FTRLProximal: FTRL Proximal Implementation for Elastic Net Regression

Online elastic net regression using the FTRL Proximal algorithm for training.

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## Default S3 method:
ftrlprox(x, y, lambda, alpha, a, b = 1, num_epochs = 1,
  save_loss = F, ...)

`x`	the model matrix containing features
`y`	the response variable
`lambda`	regularization term
`alpha`	mixing parameter, alpha=0 corresponds to L2 regularization and alpha=1 to L1.
`a`	learning rate parameter.
`b`	learning rate parameter controlling decay, defaults to 1.
`num_epochs`	number of times we should traverse over the traiing set, defaults to 1.
`save_loss`	is to save the loss function during training.
`...`	additional args