To do list

[achiefa]

List of things to do and check:

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CLI:

• [x] train should require only the folder in which data are stored.
• [x] Hence datagen should copy meta.yaml.

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  Consistency check

• [x] #3
• [x] #2
• [x] #8
• [x] #9

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  Random generator:

• [ ] Check how the seed in the runcard enters the data generation and the initialisation of replicas
• [ ] Check consistency for random numbers in datagen and train

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  NTK scaling:

• [ ] Get rid of the NTK_scaling flag. Adopt diagonal learning tensor, take the squared root, and multiply it by the derivative of the network
• [x] Implementing numerical differentiation using functors.

  ---

  Analysis

• [ ] Store outputs at each time step to how the network adapts during training
• [ ] Modify jupyter notebook for the analysis

  ---

  Code development

• [ ] #5
• [ ] Implement a configuration manager either from scratch or using [config-cpp](https://github.com/CJLove/config-cpp/tree/master
• [x] Implement testing using Google Test
• [ ] Make CMakeList.txt look more professional...
• [ ] Introduce the possibility to choose between the LeCun initialisation and the one followed by Jacot et Al..

[achiefa]

RE testing, it has been implemented in #13 using Catch2. The reason being that Catch2 is much simpler than Google Test and hence preferred for this project. Still, it can be improved and for that reason I'll leave the issue open for time being.