Skip to content

Built-in utilities for parameter fitting #86

Open
Open
Built-in utilities for parameter fitting#86

Description

@kach
kach
opened on Jan 31, 2025

We already have examples/recipes, but it could be even easier for people if there were built-in utilities:

@memo
def f(alpha, beta): ...

f.grid_search(
  loss=lambda pred: …,
  grid=(np.linspace(…), np.linspace(…), …)
)

f.gradient_fit(
  loss=lambda pred: …,
  init=(0, 0.5),
  step_size=…,
  num_iterations=…
)

Activity

  1. kach commented on Feb 13, 2025

    @kach
    kach
    on Feb 13, 2025
    OwnerAuthor

    More thoughts: could use type hints on params to specify ranges. Could also automatically generate Jupyter widgets for interaction.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Metadata

Metadata

Assignees

No one assigned

    Labels

    No labels
    No labels

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions