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# Analysis
## Files
- analysis.R: Do some analysis and generate Table IV by comparing coverage and energy from simulation results to random targets
- kmeans.R: Just here for doing some tests
- 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 Table II and III
- generate_inputs: generate the inputs for the simulations experiments + the decision tree plots
- Generate Fig 5 (decision tree nodes)
- 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)
## Folders
- inputs/: Random targets and predicted policy to use for offline experiments
- figures/: All analysis output
## Notes
Todo: remove minbucket=1 (does not impact the results)
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