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Resource Allocation via Clustering

Project description

HOTS

Hybrid Optimization for Time Series
HOTS solves problems presented as time series thanks to machine learning and optimization methods.
The library supports multiple resource related problems (placement, allocation), presented as one or more metrics.

Requirements for running HOTS

HOTS works on any platform with Python 3.8 and up.

The dev Python version must be used to install the package (for example install the package python3.10-dev in order to use Python 3.10).

A solver needs to be installed for using HOTS. By default, GLPK is installed with HOTS, but the user needs to install the following packages before using HOTS :

  • libglpk-dev
  • glpk-utils

Installing HOTS

A Makefile is provided, which creates a virtual environment and install HOTS. You can do :

make

Running HOTS

The application can be used simply by running :

hots /path/to/data/folder

Make sure to activate the virtual environment before running HOTS with :

source venv/bin/activate

Some parameters can be defined with the hots command, such as :

  • k : the number of clusters used in clustering ;
  • tau : the window size during the loop process ;
  • param : a specific parameter file to use.

All the CLI options are found running the help option :

hots --help

More parameters can be defined through a .JSON file, for which an example is provided in the tests folder. See the documentation, section User manual, for more details about all the parameters.

Note that a test data is provided within the package, so you can easily test the installation with :

hots /tests/data/generated_7

Credits

Authors:

  • Etienne Leclercq - Software design, lead developer
  • Jonathan Rivalan - Product owner, Lead designer
  • Marco Mariani
  • Gilles Lenfant
  • Soobash Daiboo
  • Kang Du
  • Amaury Sauret
  • SMILE R&D

Links

License

This software is provided under the terms of the MIT license you can read in the LICENSE.txt file of the repository or the package.

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