Physics-based dynamic PCA for modeling stochastic reaction networks with TensorFlow
This is the source repo. for the physDBD Python package. It allows the creation of physics-based machine learning models in TensorFlow for modeling stochastic reaction networks.
Quickstart
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Install:
pip install physDBD -
See the example notebook in the GitHub repo..
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Read the documentation.
Further information
See the project's GitHub page.
1 maintainer
Metadata
1 maintainer
Release files for physDBD 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| physDBD-0.1.1.tar.gz | 23.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| physDBD-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 66.0 kB
Release files / physDBD-0.1.1.tar.gz
| Download URL | physDBD-0.1.1.tar.gz |
|---|---|
| Size | 23.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / physDBD-0.1.1-py3-none-any.whl
| Download URL | physDBD-0.1.1-py3-none-any.whl |
|---|---|
| Size | 42.3 kB |
| Tags | Python 3 |
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twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.5.0.1 requests/2.24.0 requests-toolbelt/0.9.1 tqdm/4.50.0 CPython/3.7.4
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