ekhiru Goto Github PK
Type: User
Company: Telecom Paris
Location: Paris
Type: User
Company: Telecom Paris
Location: Paris
Includes functions to work with the Mallows and Generalized Mallows Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence).
PyRoad partitions the roads of a road network into homogeneous segments. It generates a data base of the segments and an interactive map. The road data can be read from OpenStreetMap.
Generate a random permutation with a given number of inversions
This repository contains the dataset STUDENTS and applications.
Mallows Kendall. A python package to for Mallows Model with top-k and complete rankings using Kendall's-tau distance. By Ahmed Boujaada, Fabien Collas and Ekhine Irurozki. We present methods for inference in the Mallows Model (MM), the best-known distribution for permutations. This is short tutorial for top-k rankings and complete rankings under the Kendall's-tau distance. Theoretical details are given in Fabien Collas and Ekhine Irurozki (2020). Concentric mixtures of Mallows Models for top-k rankings: sampling and identifiability. In: International Conference on Machine Learning (ICML 21). 2021.
code for universal rank aggregation, i.e., for different distances. See the full paper in https://da2pl.pre.utc.fr/wp-content/uploads/sites/70/2022/11/DA2PL_paper_8600.pdf and https://hal.science/hal-03972357
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