Introduction
PySaRe is a Python package for neural network-based survival analysis and reliability engineering based on PyTorch. For an introduction to PySaRe, see examples/quickstart.ipynb.
Installation
PySaRe is available on pip:
pip install pysare
Publications
The package is based on the following publications
- Energy-Based Survival Models for Predictive Maintenance https://arxiv.org/abs/2302.00629
1 maintainer
Metadata
1 maintainer
Release files for pysare 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pysare-0.1.2.tar.gz | 51.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pysare-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 124.9 kB
Release files / pysare-0.1.2.tar.gz
| Download URL | pysare-0.1.2.tar.gz |
|---|---|
| Size | 51.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.14
|
Release files / pysare-0.1.2-py3-none-any.whl
| Download URL | pysare-0.1.2-py3-none-any.whl |
|---|---|
| Size | 73.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.1 CPython/3.10.14
|
