Ranlot/single-parameter-fit
Real numbers, data science and chaos: How to fit any dataset with a single parameter
This project helps data scientists explore a novel way to approximate any dataset—whether it's time-series, images, or sound—using a single, continuous, and differentiable scalar function. You input your raw data, and the system produces a single real-valued parameter. This is for data scientists curious about alternative modeling approaches and the theoretical limits of data representation.
650 stars. No commits in the last 6 months.
Use this if you are a data scientist interested in demonstrating that any dataset can be precisely fitted by a function with just one parameter, offering a unique theoretical perspective on data encoding.
Not ideal if you need a model that generalizes to new, unseen data, as this approach is designed for precise fitting of existing samples rather than predictive power.
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650
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59
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Jupyter Notebook
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Last pushed
Nov 22, 2022
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