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README.md

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+This repository contains the work of Paul Martin and Samuel Diai for the Project 6.8 of the Course "Time Series Learning" of Laurent Oudre.
+On this project, we studied a novel approach to learn a structured Graph with Laplacian constraints.
+The work is adapted from the article "Structured Graph Learning via Laplacian Spectral
+Constraints" of Sandeep Kumar, Jiaxi Ying, Jose Vinicius de M. Cardoso and Daniel P. Palomar.
+
+The repository contains : 
+* The code of the Structured Graph Learning algorithm (SGL)
+* Some tests made on very simple datasets (two moons, circles and blops) - basic_experiments.py
+* An experiment on an animal dataset - animals..py
+* An experiment on a cancer genome dataset - cancer.py
+* A notebook where we do the above experiments and plot the results.
+* A notebook where we apply the SGL algorithm to timeseries data. It is directly adapted from the assignment n°6 of the course "Time Series Learning" by Charles Truong.
+* The final report of our project.