mikedewar/edhmm

An implementation of the Explicit Duration HMM using Python

edhmm's Issues

experiments all fail due to sample_mean_prec() mean calculation

Hi. I read the paper, I wanted to try the code!

I'm using python 2.7. If I run any of the experiments (i.e. cd experiments; python experiment_1.py) I get something like this:

../transition.py:25: RuntimeWarning: divide by zero encountered in log
  return np.log(self.A[i,j])
../log_space.py:7: RuntimeWarning: underflow encountered in exp
  return lny + log(1 + exp(lnx - lny))
Traceback (most recent call last):
  File "experiment_1.py", line 70, in 
    online=True, sample_U = True
  File "../edhmm.py", line 537, in beam
    self.O.update(Z_samples, Y)
  File "../emission.py", line 140, in update
    mu, tau = self.sample_mean_prec(Z, Y)
  File "../emission.py", line 59, in sample_mean_prec
    ybar = np.mean(n[i],1)
  File "/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py", line 2885, in mean
    out=out, keepdims=keepdims)
  File "/usr/local/lib/python2.7/dist-packages/numpy/core/_methods.py", line 56, in _mean
    rcount = _count_reduce_items(arr, axis)
  File "/usr/local/lib/python2.7/dist-packages/numpy/core/_methods.py", line 50, in _count_reduce_items
    items *= arr.shape[ax]
IndexError: tuple index out of range
zsh: exit 1     python experiment_1.py

I notice that the line that chokes is protected by an except ValueError. Maybe this should, or should also, guard against IndexError, but I don't know the code so I don't know the intention.

(In case it matters, on my system I also had to mildly rearrange the order of imports)