A new method for scalable model-free online change-point detection.
This repository contains the code for NEWMA: a new method for scalable model-free online change-point detection, Nicolas Keriven, Damien Garreau, Iacopo Poli.
The code is written for Python 3.
You can install the Python modules required by running pip install -r requirements.txt
inside the folder.
You can install the onlinecp
Python package by running
pip install ./
from the root folder of this repository.
You can generate data for the figures in the paper as follows:
- run
test_dim.py
andtest_B_runningtime.py
for Figure 4a - run
test_adaptive_vs_fixed.py
for Figure 4b - run
test_algos_synthetic_data.sh
for Figure 4c - run
test_algos_vad.sh
for Figure 4d
The scripts to generate the plots from data are in plots
and they have the same name prepended by plot_
.
Look at plots/README.md
for info on how to run them.
The code for the older version of our paper is in code_v1
.
The subdirectory contains its README.md
.