ybdesire - 9 months ago 56

Python Question

There are three fundamental problems for HMMs:

- Given the model parameters and observed data, estimate the optimal sequence of hidden states.
- Given the model parameters and observed data, calculate the likelihood of the data.
- Given just the observed data, estimate the model parameters.

The problem 1 and problem 3 could be resolved by the sklearn HMM tutorial. But how can we use sklearn to resolve problem 2?

Answer Source

Use the score() function. From the code:

```
def score(self, X, lengths=None):
"""Compute the log probability under the model.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Feature matrix of individual samples.
lengths : array-like of integers, shape (n_sequences, ), optional
Lengths of the individual sequences in ``X``. The sum of
these should be ``n_samples``.
Returns
-------
logprob : float
Log likelihood of ``X``.
```