| Name | probnum JSON |
| Version |
0.1.25
JSON |
| download |
| home_page | |
| Summary | Probabilistic Numerics in Python. |
| upload_time | 2023-04-03 08:53:26 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | <3.12,>=3.8 |
| license | The MIT License (MIT) Copyright (c) 2020 ProbNum Development Team Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
probabilistic-numerics
machine-learning
numerical-methods
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<a href="https://probnum.readthedocs.io"><img align="center" src="https://raw.githubusercontent.com/probabilistic-numerics/probnum/main/docs/source/assets/img/logo/probnum_logo_dark_txtbelow.svg" alt="probabilistic numerics" width="400" style="padding-right: 10px; padding left: 10px;" title="Probabilistic Numerics in Python"/>
</a>
<h3>Learn to Approximate. Approximate to Learn.</h3>
<p>Probabilistic Numerics in Python.</p>
</div>
---
<div align="center">
<h4 align="center">
<a href="https://probnum.readthedocs.io">Home</a> |
<a href="https://probnum.readthedocs.io/en/latest/tutorials.html">Tutorials</a> |
<a href="https://probnum.readthedocs.io/en/latest/api.html">API Reference</a> |
<a href="https://probnum.readthedocs.io/en/latest/development.html">Contributing</a>
</h4>
[](https://github.com/probabilistic-numerics/probnum/actions?query=workflow%3ACI-build)
[](https://codecov.io/gh/probabilistic-numerics/probnum/branch/main)
[](https://probabilistic-numerics.github.io/probnum-benchmarks/benchmarks/)
[](https://pypi.org/project/probnum/)
</div>
---
**ProbNum** is a Python toolkit for solving numerical problems in linear algebra, optimization, quadrature and
differential equations. ProbNum solvers not only estimate the solution of the numerical problem, but also its uncertainty (numerical error) which arises from finite computational resources, discretization and stochastic input. This numerical uncertainty can be used in downstream decisions.
Currently, available solvers are:
- **Linear solvers:** Solve $A x = b$ for $x$.
- **ODE solvers:** Solve $\dot{y}(t) = f(y(t), t)$ for $y$.
- **Integral solvers (quadrature):** Solve $F = \int_D f(x) \mathrm{d}p(x)$ for $F$.
Lower level structure includes:
- **Random variables and random processes**, as well as arithmetic operations thereof.
- Memory-efficient and lazy implementation of **linear operators**.
- **Filtering and smoothing** for (probabilistic) state-space models, mostly variants of Kalman filters.
ProbNum is underpinned by the research field probabilistic numerics (PN), which lies at the intersection of machine learning and numerics.
PN aims to quantify uncertainty arising from intractable or incomplete numerical computation and from stochastic input
using the tools of probability theory. The general vision of probabilistic numerics is to provide well-calibrated
probability measures over the output of a numerical routine, which then can be propagated along the chain of
computation.
## Installation
To get started install ProbNum using `pip`.
```bash
pip install probnum
```
Alternatively, you can install the latest version from source.
```bash
pip install git+https://github.com/probabilistic-numerics/probnum.git
```
> Note: This package is currently work in progress, therefore interfaces are subject to change.
## Documentation and Examples
For tips on getting started and how to use this package please refer to the
[**documentation**](https://probnum.readthedocs.io). It contains a
[quickstart guide](https://probnum.readthedocs.io/en/latest/tutorials/quickstart.html)
and Jupyter notebooks illustrating the basic usage of the ProbNum solvers.
## Package Development
This repository is currently under development and benefits from contribution to the code, examples or documentation.
Please refer to the [contribution guidelines](https://probnum.readthedocs.io/en/latest/development.html) before
making a pull request.
A list of core contributors to ProbNum can be found
[here](https://probnum.readthedocs.io/en/latest/development.html#probnum-team).
## Citing ProbNum
If you are using ProbNum in your research, please cite as provided.
The "Cite this repository" button on the sidebar generates a BibTeX entry or an APA entry.
## License and Contact
This work is released under the [MIT License](https://github.com/probabilistic-numerics/probnum/blob/main/LICENSE.txt).
Please submit an [issue on GitHub](https://github.com/probabilistic-numerics/probnum/issues/new) to report bugs or
request changes.
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"description": "<div align=\"center\">\n <a href=\"https://probnum.readthedocs.io\"><img align=\"center\" src=\"https://raw.githubusercontent.com/probabilistic-numerics/probnum/main/docs/source/assets/img/logo/probnum_logo_dark_txtbelow.svg\" alt=\"probabilistic numerics\" width=\"400\" style=\"padding-right: 10px; padding left: 10px;\" title=\"Probabilistic Numerics in Python\"/>\n </a>\n <h3>Learn to Approximate. Approximate to Learn.</h3>\n <p>Probabilistic Numerics in Python.</p>\n</div>\n\n---\n\n<div align=\"center\">\n\n<h4 align=\"center\">\n <a href=\"https://probnum.readthedocs.io\">Home</a> |\n <a href=\"https://probnum.readthedocs.io/en/latest/tutorials.html\">Tutorials</a> | \n <a href=\"https://probnum.readthedocs.io/en/latest/api.html\">API Reference</a> |\n <a href=\"https://probnum.readthedocs.io/en/latest/development.html\">Contributing</a>\n</h4>\n\n[](https://github.com/probabilistic-numerics/probnum/actions?query=workflow%3ACI-build)\n[](https://codecov.io/gh/probabilistic-numerics/probnum/branch/main)\n[](https://probabilistic-numerics.github.io/probnum-benchmarks/benchmarks/)\n[](https://pypi.org/project/probnum/)\n\n</div>\n\n---\n\n**ProbNum** is a Python toolkit for solving numerical problems in linear algebra, optimization, quadrature and\ndifferential equations. ProbNum solvers not only estimate the solution of the numerical problem, but also its uncertainty (numerical error) which arises from finite computational resources, discretization and stochastic input. This numerical uncertainty can be used in downstream decisions.\n\nCurrently, available solvers are:\n\n- **Linear solvers:** Solve $A x = b$ for $x$.\n- **ODE solvers:** Solve $\\dot{y}(t) = f(y(t), t)$ for $y$.\n- **Integral solvers (quadrature):** Solve $F = \\int_D f(x) \\mathrm{d}p(x)$ for $F$.\n\nLower level structure includes:\n\n- **Random variables and random processes**, as well as arithmetic operations thereof.\n- Memory-efficient and lazy implementation of **linear operators**.\n- **Filtering and smoothing** for (probabilistic) state-space models, mostly variants of Kalman filters.\n\nProbNum is underpinned by the research field probabilistic numerics (PN), which lies at the intersection of machine learning and numerics.\nPN aims to quantify uncertainty arising from intractable or incomplete numerical computation and from stochastic input \nusing the tools of probability theory. The general vision of probabilistic numerics is to provide well-calibrated \nprobability measures over the output of a numerical routine, which then can be propagated along the chain of \ncomputation.\n\n\n## Installation\nTo get started install ProbNum using `pip`.\n```bash\npip install probnum\n```\nAlternatively, you can install the latest version from source.\n```bash\npip install git+https://github.com/probabilistic-numerics/probnum.git\n```\n\n> Note: This package is currently work in progress, therefore interfaces are subject to change.\n\n## Documentation and Examples\nFor tips on getting started and how to use this package please refer to the\n[**documentation**](https://probnum.readthedocs.io). It contains a \n[quickstart guide](https://probnum.readthedocs.io/en/latest/tutorials/quickstart.html) \nand Jupyter notebooks illustrating the basic usage of the ProbNum solvers.\n\n## Package Development\nThis repository is currently under development and benefits from contribution to the code, examples or documentation.\nPlease refer to the [contribution guidelines](https://probnum.readthedocs.io/en/latest/development.html) before\nmaking a pull request.\n\nA list of core contributors to ProbNum can be found\n[here](https://probnum.readthedocs.io/en/latest/development.html#probnum-team).\n\n## Citing ProbNum\nIf you are using ProbNum in your research, please cite as provided. \nThe \"Cite this repository\" button on the sidebar generates a BibTeX entry or an APA entry. \n\n## License and Contact\nThis work is released under the [MIT License](https://github.com/probabilistic-numerics/probnum/blob/main/LICENSE.txt).\n\nPlease submit an [issue on GitHub](https://github.com/probabilistic-numerics/probnum/issues/new) to report bugs or\nrequest changes.\n",
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