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Topographic error for hexa lattice and partitioned errors for when data is too big (memory not enough for errors calculations) #96

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ricardomourarpm
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Now the topographic error for hexa topology is available and

since the way the errors are calculate takes too much memory I've add two partitioned error calculating functions.

New_hexa example in root shows that works.

Please check!

JustGlowing and others added 27 commits October 2, 2020 08:12
added return_indices parameter to win_map()
This is so we can start bokeh plotting and separate out the matplotlib code from eventual bokeh code.
This is following the example laid out by matplotlib and provides an interactive version so you can hover over and see where data points are located on the SOM.
This is so it can be viewed in GitHub without having to generate in the notebook.

Also change language so it's more formal. This follows a code review.
This is so it aligns closer to matplotlib version.
…logy-bokeh

Example: Hexagonal Topology bokeh
Added requirement in introduction
added utf-8 encoding in "open" to make example running
deleted import of scipy.misc.imresize
corrected typo in introduction
corrected typos in introduction
included various visualizations: starburst map, property distribution, quality heatmap and property plots using plotly library
corrected typo
advanced visualizations in new file
@JustGlowing
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JustGlowing commented Feb 24, 2021

hi @ricardomourarpm, thanks for this PR.

Regarding the computation of the quantization error in chunks, I would love to have a notebook where the quantization error is computed in chunks in the examples folder. I could merge that pretty easily if the example is simple and properly commented.

Regarding the topographic error, I'm not sure I want to add this feature as it currently seems too much code to maintain and your PR doesn't include documentation or tests.

If you still want me to consider your submissions to add these features into minisom.py please create a PR for each new feature and follow the guidelines below:

  1. In the description of your Pull Request explain clearly what does it implements/fixes and your changes. Possibly give an example in the description of the PR. In cases that the PR is about a code speedup, report a reproducible example and quantify the speedup.
  2. Give your pull request a helpful title that summarises what your contribution does.
  3. Write unit tests for your code and make sure the existing tests are up to date. pytest can be used for this:
pytest minisom.py
  1. Make sure that there a no stylistic issues using pycodestyle:
pycodestyle minisom.py
  1. Make sure your code is properly commented and documented. Each public method needs to be documented as the existing ones.

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