soft-brownian-offset
Soft Brownian Offset (SBO) defines an iterative approach to translate points by a most likely distance from a given dataset. It can be used for generating out-of-distribution samples.
Installation
This project is hosted on PyPI and can therefore be installed easily through pip:
pip install sbo
Dependending on your setup you may need to add --user after the install.
Usage
For brevity's sake here's a short introduction to the library's usage:
from sklearn.datasets import make_moons
from sbo import soft_brownian_offset
X, _ = make_moons(n_samples=60, noise=.08)
X_ood = soft_brownian_offset(X, d_min=.35, d_off=.24, n_samples=120, softness=0)
For more details please see the documentation.
Background
The technique is described in detail within the paper TBA. For citations please see cite.
Demonstration
See the following plot to gain intuition on the approach's results:
Please see the documentation for the source code to recreate the plot.
Cite
Please cite SBO in your paper if it helps your research TBA:
@article{name2020sbo,
Author = {TBA},
Journal = {arXiv preprint arXiv:TBA},
Title = {TBA},
Year = {2020}
}
1 maintainer
Metadata
1 maintainer
Release files for sbo 1.0.1
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Source distribution (sdist)
| File | Size | Uploaded | |
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| sbo-1.0.1.tar.gz | 53.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sbo-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 61.7 kB
Release files / sbo-1.0.1.tar.gz
| Download URL | sbo-1.0.1.tar.gz |
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| Size | 53.6 kB |
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Release files / sbo-1.0.1-py3-none-any.whl
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| Size | 8.2 kB |
| Tags | Python 3 |
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