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Collection of algorithms to compute diversity between elements and identify diverse element collections within a dataset. Technologies for Information Systems course project (A.Y. 2018/2019) at Politecnico di Milano.

Python 100.00%
diversity diversity-measurement greedy-algorithms randomized-algorithm interchange-algorithms neighborhood-based python python-3 polimi

tis-diversity-algorithms's Introduction

Diversity algorithms

Collection of algorithms to compute diverisity between elements and idenfiy diverse element collections whitin a dataset

Requirements

Python 3.

Usage

import diversity as d

elements = range(42)

# greedy algorithm
greedy_most_diverse = d.greedy(elements, 10)

# interchange algorithm
interchange_most_diverse = d.interchange(elements, 10)

# neighborhood algorithm
neighborhood_most_diverse = d.neighborhood(elements, 20)

Checkout docs/usage.md for further details.

Documentation

Checkout the documentation to initialize the environment, use the module and run the tests.

Project context

This project has been developed for the Technologies for Information Systems course (A.Y. 2018/2019) at Politecnico di Milano.

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