Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Alter Fit Convention #7

Open
vc1492a opened this issue Nov 14, 2017 · 2 comments
Open

Alter Fit Convention #7

vc1492a opened this issue Nov 14, 2017 · 2 comments
Labels
enhancement New feature of request packaging This issue relates in some way to packaging and testing

Comments

@vc1492a
Copy link
Owner

vc1492a commented Nov 14, 2017

Provide parameters in LocalOutlierProbability() and provide data in fit() as opposed to providing data in LocalOutlierProbability() along with params. This is s.t. PyNomaly is more in line with scikit-learn and other popular libraries.

@vc1492a vc1492a added the enhancement New feature of request label Nov 14, 2017
@vc1492a vc1492a added this to the PyNomaly-1.0.0 milestone Oct 30, 2018
@vc1492a
Copy link
Owner Author

vc1492a commented Sep 30, 2019

More specifically and as an update, the following parameters could stay in the LocalOutlierProbability() method:

  • extent
  • n_neighbors

And any data-related parameters that may change from execution to execution (such in the case of inference) could instead be passed to the fit() method:

  • data
  • distance_matrix
  • neighbor_matrix
  • use_numba
  • progress_bar

@vc1492a vc1492a added the packaging This issue relates in some way to packaging and testing label May 29, 2020
@vc1492a
Copy link
Owner Author

vc1492a commented Jul 9, 2020

See NetworkX implementation.

@vc1492a vc1492a modified the milestones: 1.0.0, Lightning Speed Aug 19, 2024
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement New feature of request packaging This issue relates in some way to packaging and testing
Projects
None yet
Development

No branches or pull requests

1 participant