Metadata-Version: 2.1
Name: pyords
Version: 0.0.6
Summary: A python package for operations research and data science problems.
Home-page: https://github.com/christopherpryer/pyords
Author: Chris Pryer
Author-email: christophpryer@gmail.com
License: PUBLIC
Description: (In Development)
        
        ![python package](https://github.com/christopherpryer/pyords/workflows/Python%20package/badge.svg)
        
        # pyords
        A library for operations research, data science, and financial engineering.
        
        ## features
        
        - pyords.transopt - transportation optimization
        - pyords.netopt - network optimization
        - pyords.schedopt - schedule optimization
        
        ## implementations
        
        - graph theory
        - genetic algorithm
        - simulation
        - machine learning
        
        ## motivation behind the project
        Working solo in an engineering team, I want to dedicate a fair amount of time to productionalizing the different skills I've been working on. This library will help me expose myself more to the following:
        
        1. [Open-source software development](https://en.wikipedia.org/wiki/Open-source_software_development).
        2. [Data Science](https://en.wikipedia.org/wiki/Data_science).
        3. [Operations Research](https://en.wikipedia.org/wiki/Operations_research).
        4. [Financial Engineering](https://en.wikipedia.org/wiki/Financial_engineering).
        5. [Visualizations](https://en.wikipedia.org/wiki/Data_visualization) in Python
        or JavaScript.
        6. Comprehensive self-education of tools such as [NumPy](https://en.wikipedia.org/wiki/NumPy),
        [Pandas](https://en.wikipedia.org/wiki/Pandas_(software)),
        [D3.js](https://en.wikipedia.org/wiki/D3.js),
        [Matplotlib](https://en.wikipedia.org/wiki/Matplotlib),
        [IPython](https://en.wikipedia.org/wiki/IPython) and [jupyter](https://en.wikipedia.org/wiki/Project_Jupyter),
        [scikit-learn](https://en.wikipedia.org/wiki/Scikit-learn) and [SciPy](https://en.wikipedia.org/wiki/SciPy),
        [git](https://en.wikipedia.org/wiki/Git),
        [Google OR Tools (ortools)](https://developers.google.com/optimization/),
        [Pyomo](https://en.wikipedia.org/wiki/Pyomo),
        [Supply Chain Guru](https://www.llamasoft.com/products/design/supply-chain-guru/),
        [Keras](https://en.wikipedia.org/wiki/Keras), [Hadoop](https://en.wikipedia.org/wiki/Apache_Hadoop),
        [AWS](https://en.wikipedia.org/wiki/Amazon_Web_Services),
        [GCP](https://en.wikipedia.org/wiki/Google_Cloud_Platform), [Vagrant](https://www.vagrantup.com/).
        
        # Development & Documentation
        Design is up for discussion. I'm going to start by just *namespacing* most of the unique features implemented. Some currently developed features include the following:
        
        ### cluster
        *cluster* is aimed at identifying groups in data. See
        [clustering](https://en.wikipedia.org/wiki/Cluster_analysis).
        
        #### current scope
        
        1. [Greenfield Analysis](http://supplychaindetective.com/2017/08/12/network-strategy-part-1-greenfield-analysis/) -
        a facility location and operation problem. *cluster* will provide a clustering
        algorithm for heuristic solutions.
        
        2. Route heuristic for clustering final-destination demand nodes by proximity.
        
        ### genetic_algorithm
        [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm).
        
        ### solver
        1. [Route optimization](https://en.wikipedia.org/wiki/Vehicle_routing_problem).
        
        ### distance
        1. [Haversine distance](https://en.wikipedia.org/wiki/Haversine_formula).
        2. Distance matrix preprocessing.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
