https://github.com/j2kun/svd

Python code implementing the power method for Singular Value Decomposition
https://github.com/j2kun/svd

algorithm linear-algebra optimization programming python svd

Last synced: about 2 months ago
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Python code implementing the power method for Singular Value Decomposition

• Host: GitHub
• URL: https://github.com/j2kun/svd
• Owner: j2kun
• Created: 2015-08-29T22:23:13.000Z (over 9 years ago)
• Default Branch: main
• Last Pushed: 2023-07-04T02:04:01.000Z (over 1 year ago)
• Last Synced: 2024-04-14T06:08:02.568Z (9 months ago)
• Topics: algorithm, linear-algebra, optimization, programming, python, svd
• Language: Python
• Homepage: https://jeremykun.com/2016/05/16/singular-value-decomposition-part-2-theorem-proof-algorithm/
• Size: 5.66 MB
• Stars: 99
• Watchers: 11
• Forks: 41
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
# SVD

An implementation of the greedy algorithm for SVD, using the power method for the 1-dimensional case.

For the post [Singular Value Decomposition Part 2: Theorem, Proof, Algorithm](http://jeremykun.com/2016/05/16/singular-value-decomposition-part-2-theorem-proof-algorithm/)

And the first (motivational) post in the series: [Singular Value Decomposition Part 1: Perspectives on Linear Algebra](https://jeremykun.com/2016/04/18/singular-value-decomposition-part-1-perspectives-on-linear-algebra/)

# Setup

Run the following to set up all the requirements needed to run the code in this repository.

```

$ virtualenv venv

$ source venv/bin/activate

$ pip install -r requirements.txt

$ bash setup.sh   # downloads relevant NLP corpora from nltk

```

Then run `python3 topicmodel.py` for the main topic-model routine, `svd.py` for the core svd algorithm, and `demo.py` for the numpy examples from the post.

When finished, run `$ deactivate` to exit the virtual environment.