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scikit-learn 1.3.2
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# `sklearn.utils`.gen_even_slices

sklearn.utils.gen_even_slices(n, n_packs, *, n_samples=None)

  Generator to create `n_packs` evenly spaced slices going up to `n`.

  If `n_packs` does not divide `n`, except for the first `n % n_packs` slices, remaining slices may contain fewer elements.

  Parameters:
    n int

      Size of the sequence.

    n_packs int

      Number of slices to generate.

    n_samples int, default=None

      Number of samples. Pass `n_samples` when the slices are to be used for sparse matrix indexing; slicing off-the-end raises an exception, while it works for NumPy arrays.

  Yields:
    `slice` representing a set of indices from 0 to n.

  See also

  `gen_batches`

    Generator to create slices containing batch_size elements from 0 to n.

  Examples

      >>> from sklearn.utils import gen_even_slices
      >>> list(gen_even_slices(10, 1))
      [slice(0, 10, None)]
      >>> list(gen_even_slices(10, 10))
      [slice(0, 1, None), slice(1, 2, None), ..., slice(9, 10, None)]
      >>> list(gen_even_slices(10, 5))
      [slice(0, 2, None), slice(2, 4, None), ..., slice(8, 10, None)]
      >>> list(gen_even_slices(10, 3))
      [slice(0, 4, None), slice(4, 7, None), slice(7, 10, None)]

## Examples using `sklearn.utils.gen_even_slices`

Poisson regression and non-normal loss

Poisson regression and non-normal loss