Fitting fails with AR emissions

[bantin]

import ssm
true_lds = ssm.LDS(15, 10, dynamics="none", emissions="ar")
x, y = true_lds.sample(1000)

lds = ssm.LDS(15, 10, dynamics="none", emissions="ar")
lds.fit(y, method="svi", variational_posterior="mf")

fails because of a dimension mismatch when trying to initialize the ARHMM using KMeans. In the case of an LDS (as opposed toS LDS) the number of clusters passed to Kmeans is just 1, so it's pretty much a no-op anyway.

It's line 111 (observations.py) that fails, because the dimension of data is wrong -- it's T x T x D instead of T x D.

Seems like the cause might be here:

 xs = [self.emissions.invert(data, input, mask, tag)
              for data, input, mask, tag in zip(datas, inputs, masks, tags)]

This outputs a list where each entry is T x T x D.

[bantin]

Addressed in 0e397ac

[bantin]

• Generally, there is an issue here trying to call _invert in cases where it doesn't make sense. We should only try to call _invert if the emissions model has a single subspace and is not the NeuralNetwork emissions type.