fixed: can't compute Gaussian density on a masked observation array #53
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
I can confirm that the problem this MR addresses exists and that this MR solves it. |
|
Is there a reason why this two-liner is not merged? If there is, maybe we can help? This fixes another open issue #50 and it would be nice to have tests pass, so that other PRs (or forks) would not have to deal with this. |
|
Any news on this? |
ath0m
added a commit
to ath0m/pykalman
that referenced
this pull request
Sep 30, 2019
fixed: can't compute Gaussian density on a masked observation array pykalman#53
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This fixes a small issue: the KalmanFilter.loglikelihood() method automatically parses its input into a MaskedArray, but then tries to directly pass elements of this array to log_multivariate_normal_density(), which raises an error because solve_triangular() does not support masked arrays.
The solution is to pass just the data from the masked array. This is valid because the likelihood is already only computed at timesteps where the observation is not masked.