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When cross-validation is used from GLAB, prototypes should optionally get re-learned for each sub-split. Currently, the prototypes are learned once, and then evaluation happens.

To support this, we'd need to refactor the ExperimentData object to combine the extractor and evaluation data. Alternatively, we could have cross-validation return multiple ExperimentData objects, where each corresponds to a different sub-split.

Hello Mick,
I'm trying to set up Glimpse and I'm running into some problems getting the dependencies to work together. Do you think you could show a conda list of a working environment?
Thanks,
Sean Southern

When the ML model uses an NDP activation function at S1, it's actually applying an absolute-value of NDP. This should be made more explicit by adding an ANDP (absolute-value NDP) operation to the backends, and removing the custom code at line 172 of glimpse/models/ml/model.py.

The ml.model.s1_kernels accessor always shifts orientations, ignoring the s1_shift_orientations parameter. Update line 86 of glimpse/models/ml/model.py.

Since this affects past experiments, we should also update the default value of params.s1_shift_orientations.

When processing a set of input files, the name of the current file should be available in codex "-c" commands.