Import files from Numpy

Original URL:
https://github.com/numpy/numpy/tree/61c8568e8ae2fe0afbb7a96b72902573ac4e22e7/numpy/random/mtrand
Wrap randomkit!
Make randomkit use Torch's RNG

By preventing randomkit from using its own Mersenne-twister, and calling
torch's Mersenne-twister instead, we ensure that we have a single
stream of random numbers.

The rk state is now only a receptacle of distributions-specific
information, nothing related to the actual mersenne twister. All seeding
and mersenne twister is done using torch's seed(), manual_seed(),
getRNGState(), setRNGState().

The functions are thus wrapped without any reference to the state,
making things simpler.
Automatically extract functions prototype

This will be used to vectorize the functions
Merge pull request #1 from jucor/extract

Automatically extract functions prototype
Initial pass converting scipy tests to lua

They parse but they don't pass!
Pass the tests ported from scipy!

Tests either pass or are disabled pending decisions / fixes.
Most simple distributions pass.

Added vectorization logic (from torch-cephes) to make the tests pass.
Enable and pass tests for 3-parameter distributions

Hypergeometric, non central F, and triangular distributions now work.
Enable and pass tests for 0-parameter distributions
Pass test_random_sample

Added random_sample as alias for double(), which uses *TWO* calls to the Mersenne twister - as opposed to torch.rand, which just uses the one.
Add and pass test for ensuring binomial() returns a number when only generating one sample
Add and pass test for randomkit.bytes()
Split init.lua into nonC.lua and wrapC.lua
Move test for multivariate distributions to a separate file
Enable and pass test for standard_normal

Add alias standard_normal -> gauss
Add randomkit.interval() and random kit.randint()
Add random kit.uniform()

Wrapper uses (low, high) instead of (loc, scale) parameterisation
Add missing error messages for tests
Add torch-ffi import
Merge pull request #3 from jucor/scipy_tests_rebased

This branch added the tests ported from scipy