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authorLoïc Guégan <loic.guegan@mailbox.org>2025-09-19 13:36:32 +0200
committerLoïc Guégan <loic.guegan@mailbox.org>2025-09-19 13:36:32 +0200
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tree9fcddfbb728e347905180e1745a2e7a329ea8965 /README.md
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# loosely-policies-analytics
-## Analysis folder
-- offline.R: contains two major functions:
- - build_models: To generate K-fold cross-validation results (note that hyper-parameters for decisions tree is fixed (no validation set))
- - generate_inputs: generate the inputs for the simulations experiments + the decision tree plots
-- in-situ.R: Implement the in-situ learning approach (Figure 4a 4b and 4c)
- - For figure 4a and 4b we train the model with increasing amount of data from previous results as if we were using one policy per day (see section IV.A)
- - For figure 4c, delta is generated by comparing using each policies in round-robin (one per days to perform the training)
- to each previous paper results with single policy only (see paper section IV.A)
-
-Todo: remove minbucket=1 (does not impact the results)
-
-## Simulation folder
-
-- src/: contains the simulator code (based on SimGrid)
-- libs/: contains a setup script that will fetch and configure the correct SimGrid version
- - see simulation/README.md for more info
-- results/: Contains all needed script to run the experiments
- - In particular paper.sh generates the results present in the paper
+## Folders
+
+- `analysis/`
+ - Contains data analysis + offline and in-situ training code.
+ - More info in `analysis/README.md`.
+- `simulation/`
+ - Contains the simulator source code and script to run the experiments from the paper.
+- `slides_recap/`
+ - Just backup slides. Nothing interresting.