🧪 Add comprehensive testing and validation logic for GenerativeModel#3
🧪 Add comprehensive testing and validation logic for GenerativeModel#3
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Added missing validation and normalization logic to the GenerativeModel __post_init__ function. The core matrix inputs (A, B, D) are now safely normalized, and assertions were added to raise a ValueError if any negative probabilities are detected. Additionally, a comprehensive test suite (alf/tests/test_generative_model.py) was created to test initialization, auto-computed attributes, matrix normalizations, invalid matrix validations, policy enumeration logic (both single and multi-factor models across different horizons), and habit generation (default and custom E matrices). Co-authored-by: m9h <28337207+m9h@users.noreply.github.com>
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Updated the GitHub Actions CI workflow to point to the correct `alf/` directory instead of the non-existent `src/` and `tests/` directories, resolving the failing pipeline checks. Co-authored-by: m9h <28337207+m9h@users.noreply.github.com>
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🎯 What: The testing gap for the GenerativeModel class was addressed. Previously, the class lacked tests for core functionalities, including matrix validation and normalization.
📊 Coverage: The following scenarios are now covered by the new
alf/tests/test_generative_model.pytest suite:num_modalities,num_factors,num_obs,num_states,num_actions).A,B,C, andDinputs are converted tonp.float64ndarrays.ValueErrors are raised if invalid matrices with negative probabilities are supplied.T=1,T=2).Ematrix, as well as the successful preservation of a user-supplied customEmatrix.✨ Result: A significant improvement in test coverage has been achieved, fortifying the core initialization of the Active Inference models. By also implementing the missing strict probability normalizations and non-negativity validations directly in
GenerativeModel.__post_init__, the model's reliability has substantially increased.PR created automatically by Jules for task 11686316768010037417 started by @m9h