[MRG+1] fix RBFSampler's incorrect bandwidth

`RBFSampler` is missing a factor of 2 in variance of the Gaussian to sample from. The original paper talks about the kernel `exp(1/(2 σ^2) ||x-y||^2)` Fourier-transforming into a Gaussian with variance `1/σ^2`; the current code has `exp(ɣ ||x-y||^2)` (incorrectly) Fourier-transforming to a Gaussian with variance `ɣ`.

Demonstration: here's the value of the RBF kernel between 0 and 1 for varying values of `gamma`, via `rbf_kernel`, `RBFSampler(gamma)`, and `RBFSampler(2 * gamma)` (using lots of components):
![](https://i.imgur.com/1z4Fk4u.png).

The reason `test_rbf_sampler` works is because the test is too loose, and it happens to be somewhat close at the values tested:
![](https://i.imgur.com/97fsDz5.png)
(x is true value, y is the approximated value). I just upped the number of Monte Carlo samples and increased it to 2 decimals, giving this new scatterplot:
![](http://i.imgur.com/4KnURsY.png)
This demonstrates that `assert_array_almost_equal` isn't really the right tool for testing Monte Carlo approximators like this (we can still only use 2 decimals in this test, with 10k samples). Instead, there should probably be a helper function to test that the mean absolute difference is fairly small, and also a looser bound on the max difference...or something.

Here's [a notebook](http://nbviewer.ipython.org/gist/dougalsutherland/11fd9895924c3c1aed19) that makes these pictures.

(This probably won't dramatically change anyone's practical results with `RBFSampler`, since effectively you're just cross-validating over a set of `gamma` values that are double the ones you thought you were cross-validating over. But if people were picking `gamma` based on an exact evaluation on a subset or from some other external source, their they'd be using a substantially different kernel than they thought they were.)
Yeah, it only affects how `random_weights_` are picked; a pickled `RBFSampler` that isn't re-fit will be the same.