pyIPCA a python library for Incremental PCA

I extracted some of the useful code and nifty examples from the background of my Thesis as a python library for your enjoyment. PCA or Principal Component Analysis is a pretty common data analysis technique, incremental PCA lets you perform the same type of analysis but uses the input data one sample at a time rather than all at once.

The code fully conforms to the scikit-learn api and you should be able to easily use it anywhere you are currently using one of the sklearn.decomposition classes. In fact this library is sort of on the waiting list for sklearn: https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets

IPCA on 2D point cloud shaped like an ellipse

IPCA on 2D point cloud shaped like an ellipse

Check it out if you’re interested and holla at sklearn if you want this feature!
https://github.com/kevinhughes27/pyIPCA

4 comments

    1. The references to the papers are in the source code. Some of them are in the doc string for the partial_fit method and not the main doc string

  1. hi,
    when I install the setup.py. I just run the iris.py in examples file. When it error with the information cannot import name array2d.
    Neal

    1. I haven’t ran this code for a couple of years – its possible that array2d has been deprecated and replaced with something else. That’s where I would start looking at least. If you find a fix please let me know or send a PR my way.

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