[todo] Convert 2d/3d problems into 1d

[ryanhammonds]

The current constrained 2d/3d models can be simplified. Currently, it constrains weights to be equal that are equidistant to the center of the window. For 2d AR(1):

     x_1
x_2   y   x_3
     x_4

Where

$$ \hat{y} = \frac{1}{4} \sum_{i=1}^{4} w_1 x_i $$

This is implemented with convolutions by constraining a 2d kernel:

     w_1
w_1   0   w_1
     w_1

This constrained learning can be simplified. Pull the ar weight ($w_1$) out:

$$ \hat{y} =w_1 \frac{1}{4} \sum_{i=1}^{4} x_i $$

This allows us to take averages at each equidistant points, turning 2d/3d problems into 1d. This will simplify computation and allow standard 1d AR solvers.

The first thing to do is figuring out how to efficiently scale the averaging of x across all windows for AR(p) in n-d. The output should be a matrix X with shape (n_windows, p).