minaskar/sinflow
Sliced Iterative Normalizing Flow with Minimal Dependencies
This project helps data scientists, statisticians, and researchers understand the underlying probability distribution of complex datasets and generate new, realistic data points from it. You input your raw data, and it outputs a model that can estimate the likelihood of existing data or create synthetic data that mimics the original. It's for anyone working with data where understanding its shape and generating new samples are crucial.
No commits in the last 6 months. Available on PyPI.
Use this if you need to accurately model the probability density of your data or generate new data samples that closely resemble your original dataset, especially when dealing with complex, high-dimensional distributions.
Not ideal if you are looking for a simple regression model or classification algorithm, as its primary purpose is density estimation and sampling.
Stars
11
Forks
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Language
Python
License
GPL-3.0
Category
Last pushed
Nov 27, 2024
Commits (30d)
0
Dependencies
2
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