# mlrose-ky: Machine Learning, Randomized Optimization, and SEarch
[](https://pypi.org/project/mlrose-ky/)
[](https://htmlpreview.github.io/?https://github.com/nkapila6/mlrose-ky/blob/python-coverage-comment-action-data/htmlcov/index.html)
mlrose-ky is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.
## Project Background
mlrose-ky is a fork of the `mlrose-hiive` repository, which itself was a fork of the original `mlrose` repository. The original mlrose package was developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
This repository includes implementations of all randomized optimization algorithms taught in the course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.
## Main Features
#### *Randomized Optimization Algorithms*
- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm, and (discrete) MIMIC;
- Solve both maximization and minimization problems;
- Define the algorithm's initial state or start from a random state;
- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay, or exponential decay.
#### *Problem Types*
- Solve discrete-value (bit-string and integer-string), continuous-value, and tour optimization (travelling salesperson) problems;
- Define your own fitness function for optimization or use a pre-defined function.
- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens, and Max-K Color optimization problems.
#### *Machine Learning Weight Optimization*
- Optimize the weights of neural networks, linear regression models, and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm, or gradient descent;
- Supports classification and regression neural networks.
## Project Improvements and Updates
The `mlrose-ky` project is undergoing significant improvements to enhance code quality, documentation, and testing. Below is a list of tasks that have been completed or are in progress:
1. **Fix Python Warnings and Errors**: All Python warnings and errors have been addressed, except for a few unavoidable ones like "duplicate code." ✅
2. **Add Python 3.10 Type Hints**: Type hints are being added to all function and method definitions, as well as method properties (e.g., `self.foo: str = 'bar'`), to improve code clarity and maintainability.
3. **Enhance Documentation**: NumPy-style docstrings are being added to all functions and methods, with at least a one-line docstring at the top of every file summarizing its contents. This will make the codebase more understandable and easier to use for others.
4. **Increase Test Coverage**: Tests are being added using Pytest, with a goal of achieving 100% code coverage to ensure the robustness of the codebase.
5. **Resolve TODO/FIXME Comments**: A thorough search is being conducted for any TODO, FIXME, or similar comments, and their respective issues are being resolved.
6. **Optimize Code**: Vanilla Python loops are being optimized where possible by vectorizing them with NumPy to enhance performance.
7. **Improve Code Quality**: Any other sub-optimal code, bugs, or code quality issues are being addressed to ensure a high standard of coding practices.
8. **Clean Up Codebase**: All commented-out code is being removed to keep the codebase clean and maintainable.
## Installation
mlrose-ky was written in Python 3 and requires NumPy, SciPy, and Scikit-Learn (sklearn).
The latest version can be installed using `pip`:
```bash
pip install mlrose-ky
```
Once it is installed, simply import it like so:
```python
import mlrose_ky
```
## Documentation
The official mlrose-ky documentation can be found [here](https://mlrose.readthedocs.io/).
A Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).
## Licensing, Authors, Acknowledgements
mlrose-ky was forked from the `mlrose-hiive` repository, which was a fork of the original `mlrose` repository.
The original `mlrose` was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE).
You can cite mlrose-ky in research publications and reports as follows:
* Nakamura, K. (2024). ***mlrose-ky: Machine Learning, Randomized Optimization, and SEarch package for Python***. https://github.com/your-repo-url. Accessed: *day month year*.
Please also keep the original authors' citations:
* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.
* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
BibTeX entry:
```bibtex
@misc{Nakamura24,
author = {Nakamura, K.},
title = {{mlrose-ky: Machine Learning, Randomized Optimization and SEarch package for Python}},
year = 2024,
howpublished = {\url{https://github.com/your-repo-url}},
note = {Accessed: day month year}
}
@misc{Rollings20,
author = {Rollings, A.},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},
year = 2020,
howpublished = {\url{https://github.com/hiive/mlrose}},
note = {Accessed: day month year}
}
@misc{Hayes19,
author = {Hayes, G.},
title = {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
year = 2019,
howpublished = {\url{https://github.com/gkhayes/mlrose}},
note = {Accessed: day month year}
}
```
## Collaborators
<!-- readme: collaborators -start -->
<table>
<tbody>
<tr>
<td align="center">
<a href="https://github.com/nkapila6">
<img src="https://avatars.githubusercontent.com/u/12816113?v=4" width="100;" alt="nkapila6"/>
<br />
<sub><b>Nikhil Kapila</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/knakamura13">
<img src="https://avatars.githubusercontent.com/u/20162718?v=4" width="100;" alt="knakamura13"/>
<br />
<sub><b>Kyle Nakamura</b></sub>
</a>
</td>
</tr>
<tbody>
</table>
<!-- readme: collaborators -end -->
## Contributors
<!-- readme: contributors -start -->
<table>
<tbody>
<tr>
<td align="center">
<a href="https://github.com/hiive">
<img src="https://avatars.githubusercontent.com/u/24660532?v=4" width="100;" alt="hiive"/>
<br />
<sub><b>hiive</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/gkhayes">
<img src="https://avatars.githubusercontent.com/u/24857299?v=4" width="100;" alt="gkhayes"/>
<br />
<sub><b>Dr Genevieve Hayes</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/knakamura13">
<img src="https://avatars.githubusercontent.com/u/20162718?v=4" width="100;" alt="knakamura13"/>
<br />
<sub><b>Kyle Nakamura</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/ChristopherBilg">
<img src="https://avatars.githubusercontent.com/u/3654150?v=4" width="100;" alt="ChristopherBilg"/>
<br />
<sub><b>Chris Bilger</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/nkapila6">
<img src="https://avatars.githubusercontent.com/u/12816113?v=4" width="100;" alt="nkapila6"/>
<br />
<sub><b>Nikhil Kapila</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/Agrover112">
<img src="https://avatars.githubusercontent.com/u/42321810?v=4" width="100;" alt="Agrover112"/>
<br />
<sub><b>Agrover112</b></sub>
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/domfrecent">
<img src="https://avatars.githubusercontent.com/u/12631209?v=4" width="100;" alt="domfrecent"/>
<br />
<sub><b>Dominic Frecentese</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/harrisonfloam">
<img src="https://avatars.githubusercontent.com/u/130672912?v=4" width="100;" alt="harrisonfloam"/>
<br />
<sub><b>harrisonfloam</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/AlexWendland">
<img src="https://avatars.githubusercontent.com/u/3949212?v=4" width="100;" alt="AlexWendland"/>
<br />
<sub><b>Alex Wendland</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/cooknl">
<img src="https://avatars.githubusercontent.com/u/5116899?v=4" width="100;" alt="cooknl"/>
<br />
<sub><b>CAPN</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/KevinJBoyer">
<img src="https://avatars.githubusercontent.com/u/31424131?v=4" width="100;" alt="KevinJBoyer"/>
<br />
<sub><b>Kevin Boyer</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/jfs42">
<img src="https://avatars.githubusercontent.com/u/43157283?v=4" width="100;" alt="jfs42"/>
<br />
<sub><b>Jason Seeley</b></sub>
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/sareini">
<img src="https://avatars.githubusercontent.com/u/26151060?v=4" width="100;" alt="sareini"/>
<br />
<sub><b>Muhammad Sareini</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/nibelungvalesti">
<img src="https://avatars.githubusercontent.com/u/9278042?v=4" width="100;" alt="nibelungvalesti"/>
<br />
<sub><b>nibelungvalesti</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/tadmorgan">
<img src="https://avatars.githubusercontent.com/u/4197132?v=4" width="100;" alt="tadmorgan"/>
<br />
<sub><b>W. Tad Morgan</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/mjschock">
<img src="https://avatars.githubusercontent.com/u/1357197?v=4" width="100;" alt="mjschock"/>
<br />
<sub><b>Michael Schock</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/jlm429">
<img src="https://avatars.githubusercontent.com/u/10093986?v=4" width="100;" alt="jlm429"/>
<br />
<sub><b>John Mansfield</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/dstrube1">
<img src="https://avatars.githubusercontent.com/u/7396679?v=4" width="100;" alt="dstrube1"/>
<br />
<sub><b>David Strube</b></sub>
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/austin-bowen">
<img src="https://avatars.githubusercontent.com/u/4653828?v=4" width="100;" alt="austin-bowen"/>
<br />
<sub><b>Austin Bowen</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/bspivey">
<img src="https://avatars.githubusercontent.com/u/6569966?v=4" width="100;" alt="bspivey"/>
<br />
<sub><b>Ben Spivey</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/dreadn0ught">
<img src="https://avatars.githubusercontent.com/u/31293924?v=4" width="100;" alt="dreadn0ught"/>
<br />
<sub><b>David</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/brokensandals">
<img src="https://avatars.githubusercontent.com/u/328868?v=4" width="100;" alt="brokensandals"/>
<br />
<sub><b>Jacob Williams</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/ksbeattie">
<img src="https://avatars.githubusercontent.com/u/1534843?v=4" width="100;" alt="ksbeattie"/>
<br />
<sub><b>Keith Beattie</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/cbhyphen">
<img src="https://avatars.githubusercontent.com/u/12734117?v=4" width="100;" alt="cbhyphen"/>
<br />
<sub><b>cbhyphen</b></sub>
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/dsctt">
<img src="https://avatars.githubusercontent.com/u/45729071?v=4" width="100;" alt="dsctt"/>
<br />
<sub><b>Daniel Scott</b></sub>
</a>
</td>
<td align="center">
<a href="https://github.com/wyang36">
<img src="https://avatars.githubusercontent.com/u/5606561?v=4" width="100;" alt="wyang36"/>
<br />
<sub><b>Kira Yang</b></sub>
</a>
</td>
</tr>
<tbody>
</table>
<!-- readme: contributors -end -->
Raw data
{
"_id": null,
"home_page": null,
"name": "mlrose-ky",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "Kyle Nakamura <knakamura13dev@gmail.com>",
"keywords": "machine learning, randomized optimization, search algorithms, neural networks, genetic algorithm, simulated annealing, hill climbing, MIMIC, Python, OMSCS, CS, 7641, mlrose-hiive",
"author": null,
"author_email": "Kyle Nakamura <knakamura13dev@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/2c/91/ebc8b9119b4e9070e29eb15def343d862e81a5ae635cd1d9e4d1c4cc2735/mlrose_ky-1.0.6.tar.gz",
"platform": null,
"description": "# mlrose-ky: Machine Learning, Randomized Optimization, and SEarch\n\n[](https://pypi.org/project/mlrose-ky/)\n[](https://htmlpreview.github.io/?https://github.com/nkapila6/mlrose-ky/blob/python-coverage-comment-action-data/htmlcov/index.html)\n\nmlrose-ky is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.\n\n## Project Background\n\nmlrose-ky is a fork of the `mlrose-hiive` repository, which itself was a fork of the original `mlrose` repository. The original mlrose package was developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.\n\nThis repository includes implementations of all randomized optimization algorithms taught in the course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.\n\n## Main Features\n\n#### *Randomized Optimization Algorithms*\n- Implementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm, and (discrete) MIMIC;\n- Solve both maximization and minimization problems;\n- Define the algorithm's initial state or start from a random state;\n- Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay, or exponential decay.\n\n#### *Problem Types*\n- Solve discrete-value (bit-string and integer-string), continuous-value, and tour optimization (travelling salesperson) problems;\n- Define your own fitness function for optimization or use a pre-defined function.\n- Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens, and Max-K Color optimization problems.\n\n#### *Machine Learning Weight Optimization*\n- Optimize the weights of neural networks, linear regression models, and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm, or gradient descent;\n- Supports classification and regression neural networks.\n\n## Project Improvements and Updates\n\nThe `mlrose-ky` project is undergoing significant improvements to enhance code quality, documentation, and testing. Below is a list of tasks that have been completed or are in progress:\n\n1. **Fix Python Warnings and Errors**: All Python warnings and errors have been addressed, except for a few unavoidable ones like \"duplicate code.\" \u2705\n \n2. **Add Python 3.10 Type Hints**: Type hints are being added to all function and method definitions, as well as method properties (e.g., `self.foo: str = 'bar'`), to improve code clarity and maintainability.\n \n3. **Enhance Documentation**: NumPy-style docstrings are being added to all functions and methods, with at least a one-line docstring at the top of every file summarizing its contents. This will make the codebase more understandable and easier to use for others.\n \n4. **Increase Test Coverage**: Tests are being added using Pytest, with a goal of achieving 100% code coverage to ensure the robustness of the codebase.\n \n5. **Resolve TODO/FIXME Comments**: A thorough search is being conducted for any TODO, FIXME, or similar comments, and their respective issues are being resolved.\n\n6. **Optimize Code**: Vanilla Python loops are being optimized where possible by vectorizing them with NumPy to enhance performance.\n\n7. **Improve Code Quality**: Any other sub-optimal code, bugs, or code quality issues are being addressed to ensure a high standard of coding practices.\n\n8. **Clean Up Codebase**: All commented-out code is being removed to keep the codebase clean and maintainable.\n\n## Installation\n\nmlrose-ky was written in Python 3 and requires NumPy, SciPy, and Scikit-Learn (sklearn).\n\nThe latest version can be installed using `pip`:\n\n```bash\npip install mlrose-ky\n```\n\nOnce it is installed, simply import it like so:\n\n```python\nimport mlrose_ky\n```\n\n## Documentation\n\nThe official mlrose-ky documentation can be found [here](https://mlrose.readthedocs.io/).\n\nA Jupyter notebook containing the examples used in the documentation is also available [here](https://github.com/gkhayes/mlrose/blob/master/tutorial_examples.ipynb).\n\n## Licensing, Authors, Acknowledgements\n\nmlrose-ky was forked from the `mlrose-hiive` repository, which was a fork of the original `mlrose` repository.\n\nThe original `mlrose` was written by Genevieve Hayes and is distributed under the [3-Clause BSD license](https://github.com/gkhayes/mlrose/blob/master/LICENSE). \n\nYou can cite mlrose-ky in research publications and reports as follows:\n* Nakamura, K. (2024). ***mlrose-ky: Machine Learning, Randomized Optimization, and SEarch package for Python***. https://github.com/your-repo-url. Accessed: *day month year*.\n\nPlease also keep the original authors' citations:\n* Rollings, A. (2020). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix***. https://github.com/hiive/mlrose. Accessed: *day month year*.\n* Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.\n\nThanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).\n\nBibTeX entry:\n```bibtex\n@misc{Nakamura24,\n author = {Nakamura, K.},\n title = {{mlrose-ky: Machine Learning, Randomized Optimization and SEarch package for Python}},\n year = 2024,\n howpublished = {\\url{https://github.com/your-repo-url}},\n note = {Accessed: day month year}\n}\n\n@misc{Rollings20,\n author = {Rollings, A.},\n title \t= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python, hiive extended remix}},\n year \t= 2020,\n howpublished = {\\url{https://github.com/hiive/mlrose}},\n note \t= {Accessed: day month year}\n}\n\n@misc{Hayes19,\n author = {Hayes, G.},\n title \t= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},\n year \t= 2019,\n howpublished = {\\url{https://github.com/gkhayes/mlrose}},\n note \t= {Accessed: day month year}\n}\n```\n\n## Collaborators\n\n<!-- readme: collaborators -start -->\n<table>\n\t<tbody>\n\t\t<tr>\n <td align=\"center\">\n <a href=\"https://github.com/nkapila6\">\n <img src=\"https://avatars.githubusercontent.com/u/12816113?v=4\" width=\"100;\" alt=\"nkapila6\"/>\n <br />\n <sub><b>Nikhil Kapila</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/knakamura13\">\n <img src=\"https://avatars.githubusercontent.com/u/20162718?v=4\" width=\"100;\" alt=\"knakamura13\"/>\n <br />\n <sub><b>Kyle Nakamura</b></sub>\n </a>\n </td>\n\t\t</tr>\n\t<tbody>\n</table>\n<!-- readme: collaborators -end -->\n\n## Contributors\n\n<!-- readme: contributors -start -->\n<table>\n\t<tbody>\n\t\t<tr>\n <td align=\"center\">\n <a href=\"https://github.com/hiive\">\n <img src=\"https://avatars.githubusercontent.com/u/24660532?v=4\" width=\"100;\" alt=\"hiive\"/>\n <br />\n <sub><b>hiive</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/gkhayes\">\n <img src=\"https://avatars.githubusercontent.com/u/24857299?v=4\" width=\"100;\" alt=\"gkhayes\"/>\n <br />\n <sub><b>Dr Genevieve Hayes</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/knakamura13\">\n <img src=\"https://avatars.githubusercontent.com/u/20162718?v=4\" width=\"100;\" alt=\"knakamura13\"/>\n <br />\n <sub><b>Kyle Nakamura</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/ChristopherBilg\">\n <img src=\"https://avatars.githubusercontent.com/u/3654150?v=4\" width=\"100;\" alt=\"ChristopherBilg\"/>\n <br />\n <sub><b>Chris Bilger</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/nkapila6\">\n <img src=\"https://avatars.githubusercontent.com/u/12816113?v=4\" width=\"100;\" alt=\"nkapila6\"/>\n <br />\n <sub><b>Nikhil Kapila</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/Agrover112\">\n <img src=\"https://avatars.githubusercontent.com/u/42321810?v=4\" width=\"100;\" alt=\"Agrover112\"/>\n <br />\n <sub><b>Agrover112</b></sub>\n </a>\n </td>\n\t\t</tr>\n\t\t<tr>\n <td align=\"center\">\n <a href=\"https://github.com/domfrecent\">\n <img src=\"https://avatars.githubusercontent.com/u/12631209?v=4\" width=\"100;\" alt=\"domfrecent\"/>\n <br />\n <sub><b>Dominic Frecentese</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/harrisonfloam\">\n <img src=\"https://avatars.githubusercontent.com/u/130672912?v=4\" width=\"100;\" alt=\"harrisonfloam\"/>\n <br />\n <sub><b>harrisonfloam</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/AlexWendland\">\n <img src=\"https://avatars.githubusercontent.com/u/3949212?v=4\" width=\"100;\" alt=\"AlexWendland\"/>\n <br />\n <sub><b>Alex Wendland</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/cooknl\">\n <img src=\"https://avatars.githubusercontent.com/u/5116899?v=4\" width=\"100;\" alt=\"cooknl\"/>\n <br />\n <sub><b>CAPN</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/KevinJBoyer\">\n <img src=\"https://avatars.githubusercontent.com/u/31424131?v=4\" width=\"100;\" alt=\"KevinJBoyer\"/>\n <br />\n <sub><b>Kevin Boyer</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/jfs42\">\n <img src=\"https://avatars.githubusercontent.com/u/43157283?v=4\" width=\"100;\" alt=\"jfs42\"/>\n <br />\n <sub><b>Jason Seeley</b></sub>\n </a>\n </td>\n\t\t</tr>\n\t\t<tr>\n <td align=\"center\">\n <a href=\"https://github.com/sareini\">\n <img src=\"https://avatars.githubusercontent.com/u/26151060?v=4\" width=\"100;\" alt=\"sareini\"/>\n <br />\n <sub><b>Muhammad Sareini</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/nibelungvalesti\">\n <img src=\"https://avatars.githubusercontent.com/u/9278042?v=4\" width=\"100;\" alt=\"nibelungvalesti\"/>\n <br />\n <sub><b>nibelungvalesti</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/tadmorgan\">\n <img src=\"https://avatars.githubusercontent.com/u/4197132?v=4\" width=\"100;\" alt=\"tadmorgan\"/>\n <br />\n <sub><b>W. Tad Morgan</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/mjschock\">\n <img src=\"https://avatars.githubusercontent.com/u/1357197?v=4\" width=\"100;\" alt=\"mjschock\"/>\n <br />\n <sub><b>Michael Schock</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/jlm429\">\n <img src=\"https://avatars.githubusercontent.com/u/10093986?v=4\" width=\"100;\" alt=\"jlm429\"/>\n <br />\n <sub><b>John Mansfield</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/dstrube1\">\n <img src=\"https://avatars.githubusercontent.com/u/7396679?v=4\" width=\"100;\" alt=\"dstrube1\"/>\n <br />\n <sub><b>David Strube</b></sub>\n </a>\n </td>\n\t\t</tr>\n\t\t<tr>\n <td align=\"center\">\n <a href=\"https://github.com/austin-bowen\">\n <img src=\"https://avatars.githubusercontent.com/u/4653828?v=4\" width=\"100;\" alt=\"austin-bowen\"/>\n <br />\n <sub><b>Austin Bowen</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/bspivey\">\n <img src=\"https://avatars.githubusercontent.com/u/6569966?v=4\" width=\"100;\" alt=\"bspivey\"/>\n <br />\n <sub><b>Ben Spivey</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/dreadn0ught\">\n <img src=\"https://avatars.githubusercontent.com/u/31293924?v=4\" width=\"100;\" alt=\"dreadn0ught\"/>\n <br />\n <sub><b>David</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/brokensandals\">\n <img src=\"https://avatars.githubusercontent.com/u/328868?v=4\" width=\"100;\" alt=\"brokensandals\"/>\n <br />\n <sub><b>Jacob Williams</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/ksbeattie\">\n <img src=\"https://avatars.githubusercontent.com/u/1534843?v=4\" width=\"100;\" alt=\"ksbeattie\"/>\n <br />\n <sub><b>Keith Beattie</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/cbhyphen\">\n <img src=\"https://avatars.githubusercontent.com/u/12734117?v=4\" width=\"100;\" alt=\"cbhyphen\"/>\n <br />\n <sub><b>cbhyphen</b></sub>\n </a>\n </td>\n\t\t</tr>\n\t\t<tr>\n <td align=\"center\">\n <a href=\"https://github.com/dsctt\">\n <img src=\"https://avatars.githubusercontent.com/u/45729071?v=4\" width=\"100;\" alt=\"dsctt\"/>\n <br />\n <sub><b>Daniel Scott</b></sub>\n </a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/wyang36\">\n <img src=\"https://avatars.githubusercontent.com/u/5606561?v=4\" width=\"100;\" alt=\"wyang36\"/>\n <br />\n <sub><b>Kira Yang</b></sub>\n </a>\n </td>\n\t\t</tr>\n\t<tbody>\n</table>\n<!-- readme: contributors -end -->\n",
"bugtrack_url": null,
"license": "BSD-3-Clause",
"summary": "MLROSe-ky: Machine Learning, Randomized Optimization and Search",
"version": "1.0.6",
"project_urls": {
"Documentation": "https://nkapila6.github.io/mlrose-ky/",
"Homepage": "https://github.com/knakamura13/mlrose-ky",
"Issues": "https://github.com/knakamura13/mlrose-ky/issues"
},
"split_keywords": [
"machine learning",
" randomized optimization",
" search algorithms",
" neural networks",
" genetic algorithm",
" simulated annealing",
" hill climbing",
" mimic",
" python",
" omscs",
" cs",
" 7641",
" mlrose-hiive"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "7d277968cc4b3da7980a6a883a625526d1bbc9ff7ab3a0ca440894b6836e13f0",
"md5": "4231a9ec3b486311dd7679b23ad004c1",
"sha256": "410abf939ec1981c5fccf485a5235f6d127ca93668b8f5b5451b17ed9388ab03"
},
"downloads": -1,
"filename": "mlrose_ky-1.0.6-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4231a9ec3b486311dd7679b23ad004c1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 125272,
"upload_time": "2024-08-22T07:05:31",
"upload_time_iso_8601": "2024-08-22T07:05:31.892378Z",
"url": "https://files.pythonhosted.org/packages/7d/27/7968cc4b3da7980a6a883a625526d1bbc9ff7ab3a0ca440894b6836e13f0/mlrose_ky-1.0.6-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2c91ebc8b9119b4e9070e29eb15def343d862e81a5ae635cd1d9e4d1c4cc2735",
"md5": "aab7957bca040eb586801367f33f030c",
"sha256": "9014a2d265e9ee0dcad3eb97114442947c45cf63f7e94143db78d9bdff64971d"
},
"downloads": -1,
"filename": "mlrose_ky-1.0.6.tar.gz",
"has_sig": false,
"md5_digest": "aab7957bca040eb586801367f33f030c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 78600,
"upload_time": "2024-08-22T07:05:33",
"upload_time_iso_8601": "2024-08-22T07:05:33.855255Z",
"url": "https://files.pythonhosted.org/packages/2c/91/ebc8b9119b4e9070e29eb15def343d862e81a5ae635cd1d9e4d1c4cc2735/mlrose_ky-1.0.6.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-08-22 07:05:33",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "knakamura13",
"github_project": "mlrose-ky",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "matplotlib",
"specs": [
[
"~=",
"3.8.4"
]
]
},
{
"name": "networkx",
"specs": [
[
"~=",
"3.3"
]
]
},
{
"name": "numpy",
"specs": [
[
"~=",
"1.26.4"
]
]
},
{
"name": "pandas",
"specs": [
[
"~=",
"2.2.1"
]
]
},
{
"name": "scikit-learn",
"specs": [
[
"~=",
"1.5.0"
]
]
},
{
"name": "scipy",
"specs": [
[
"~=",
"1.13.1"
]
]
},
{
"name": "setuptools",
"specs": [
[
"~=",
"69.5.1"
]
]
},
{
"name": "pytest",
"specs": [
[
"~=",
"8.3.2"
]
]
}
],
"lcname": "mlrose-ky"
}
JSON
