Allow GLM style specification of Bernoulli outcomes for logistic regression

I often like to use `glm(X, y)` where `y` is a vector of 0's and 1's and thought it would be nice to offer the same interface for working with `glmnet`. This patch makes some quick changes to allow that to happen. Let me know if you'd like me to handle the changes in another way since I didn't spend a lot of time thinking about the cleanest way to add this functionality.

The script below gives a basic demo of the extended functionality. I can make it into a test:

	using GLMNet
	using Distributions
	using Base.Test

	srand(1)

	invlogit(z::Real) = 1 / (1 + exp(-z))

	n, p = 250_000, 2

	intercept = randn()
	beta = randn(p)
	X = randn(n, p)
	y = X * beta
	for i in 1:n
		y[i] = rand(Bernoulli(invlogit(intercept + y[i])))
	end

	path = glmnet(X, y, Binomial())
	@test abs(intercept - path.a0[end]) < 0.1
	@test norm(beta - convert(Matrix{Float64}, path.betas)[:, end]) < 0.1