v0.2.0

[0.2.0] - 2026-07-28

Added

• Mexican hat neighborhood function, selectable with neighborhood_function='mexicanhat'
  (contributed by @Arne49 in
  #2).

  Implemented as the isotropic 2-D form (1 - u) * exp(-u) over u = sqdist(c, i) / (2 * sigma^2),
  normalized so that h(c, c) = 1. It crosses zero at a radius of sqrt(2) * sigma and reaches its
  minimum of -exp(-2) at a radius of 2 * sigma.

  Note that a neighborhood function must depend on the grid distance alone, not on the two axis
  offsets separately, per sqdist(c, i) in Kohonen (2013) Eq. (5) and the "lateral distance"
  abscissa of Vrieze (1995) Fig. 3. The gaussian happens to be separable into a product of per-axis
  terms, but that is a property of the exponential rather than of neighborhood functions in general.

• ## Neighborhood functions section in the README, comparing all three and documenting the
  batch-mode restriction below.

• Test suite for the neighborhood functions, asserting closed-form analytic properties rather than
  golden values.

Fixed

• Cyclic (toroidal) neighborhoods were only correct in one direction. The fold-back handled
  offsets above +length/2 but left those below -length/2 alone, so a winner in the upper half of
  an axis did not wrap. On a 10x10 toroidal map with sigma=1, a winner at row 0 gave h[9] = 0.6065
  while a winner at row 9 gave h[0] = 0.0 instead of the same 0.6065. Now uses the minimum-image
  convention, which folds both tails.

  This changes results for any map built with cyclic_x=True or cyclic_y=True.

• A neighborhood radius of zero divided by zero and produced inf/nan weights instead of raising.
  _gaussian and _mexicanhat now reject a non-finite or non-positive radius; _bubble still
  accepts a radius of zero, which selects the winner alone.

• An unknown neighborhood_function raised a bare KeyError. It now raises ValueError listing the
  valid names, matching the behavior of weight_initialization.

Changed

• requires-python is now >=3.10. The declared floor of >=3.9 was never accurate: the module
  uses PEP 604 unions (int | None) in runtime-evaluated annotations without
  from __future__ import annotations, so importing it on Python 3.9 raises TypeError. Python 3.9
  reached end of life in October 2025. Installers will now decline the package on 3.9 rather than
  install something that cannot be imported.

• train(mode='batch') combined with neighborhood_function='mexicanhat' now raises ValueError.
  The batch update of Kohonen (2013) Eq. (8) is a weighted mean whose denominator sum_j n_j * h_ji
  is only well defined for a non-negative neighborhood function. The mexican hat is signed by
  construction, so the denominator can vanish or turn negative: on a 12x12 grid, 49 of 144
  denominators come out negative, which inverts or explodes the update. This is a property of the
  function itself rather than of this implementation. Use mode='random' or mode='sequential'.

Packaging

• Restored the cli extra (pip install python-som[cli], which pulls in tqdm for training
  progress bars). This extra shipped in 0.1.3 on PyPI but was never committed to the repository, so
  releasing from the repository as-is would have silently removed it.

Note on 0.1.3

There is no changelog entry for 0.1.3 because it was published to PyPI from an uncommitted working
tree and has no corresponding commit. Its python_som/__init__.py is byte-identical to the 0.1.2
tag; the only differences are the version string and the cli extra restored above.