Speed up training

Four changes, ordered from largest effect to smallest. The first two cost nothing.

1. Use batch training

som.train(data, n_iteration=100, mode="batch")

It is the default, it is the mode the optimization work targets, and it needs far fewer iterations: 10 per sample against 1000 for the stepwise modes. If you are passing mode="random" or mode="sequential" for no particular reason, change it.

See Batch vs stepwise for what the modes do differently.

2. Keep the default distance function

The winner search has a fast path that applies only to the Euclidean distance. Supplying your own distance_function falls back to one evaluation per sample, several times slower. If you need a custom distance, see Use a custom strategy; if you do not, leave it alone.

3. Install numba

pip install numba

Nothing else to do. The compiled kernel is detected and used automatically, and results are identical to the NumPy path. Check it took effect:

import python_som

python_som.accelerated()  # True once numba is installed

Three things to expect:

• NumPy is downgraded in that environment. numba 0.66 requires numpy<2.5. Install it into a virtual environment you are willing to hold below that.
• The gain is uneven. Measured on batch training: 2.4x on a 60x60 map, about 1.0x on a 20x20 or a 100x100 one. Measure your own case before accepting the NumPy constraint.
• The first run is slower. About 80 ms to import numba and 500 ms to compile, once per process.

4. Size the map for the data

Cost grows with the node count, faster than linearly. Kohonen's rule of thumb is about 50 samples per node on average; well past that, you are paying for resolution the data does not support.

Let the constructor pick the ratio from the data rather than choosing both sides yourself:

som = python_som.SOM(x=20, y=None, input_len=4, data=data)

The measured timings show what map size costs across a range of shapes.

Why these and not others

How batch training is computed covers what the library does internally, including the approaches that were measured and rejected.