Metadata-Version: 2.1
Name: mlrose-hiive
Version: 2.1.7
Summary: MLROSe: Machine Learning, Randomized Optimization and Search (hiive extended remix)
Home-page: https://github.com/hiive/mlrose
Author: Genevieve Hayes (modified by Andrew Rollings)
License: BSD
Download-URL: https://github.com/hiive/mlrose/archive/2.1.7.tar.gz
Description: # mlrose: Machine Learning, Randomized Optimization and SEarch
        mlrose 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 was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.
        
        It includes implementations of all randomized optimization algorithms taught in this 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. 
        
        At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.
        
        ## 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.
        
        ## Installation
        mlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).
        
        The latest version can be installed using `pip`:
        ```
        pip install mlrose-hiive
        ```
        
        ## Documentation
        The official mlrose 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 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 in research publications and reports as follows:
        * 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*.
        
        Please also keep the original author's citation:
        * Hayes, G. (2019). ***mlrose: Machine Learning, Randomized Optimization and SEarch package for Python***. https://github.com/gkhayes/mlrose. Accessed: *day month year*.
        
        You can cite this fork in a similar way, but please be sure to reference the original work.
        Thanks to David S. Park for the MIMIC enhancements (from https://github.com/parkds/mlrose).
        
        
        BibTeX entry:
        ```
        @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}
        }
        
        @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}
        }
        ```
        
Platform: UNKNOWN
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3
Description-Content-Type: text/markdown
