PrincetonUniversity / PsyNeuLink
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LAST BUILD ON BRANCH feat/pec_hist
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davidt0x 
perf: add vectorized PEC histogram fill mode

Add an opt-in vectorized histogram likelihood path for PEC data fitting. The new path keeps the existing local per-trial/per-category histogram estimator as the default, but allows histogram users to pass vectorized=True so one histogram fill includes explicit trial and category axes plus the continuous outcome axes.

Use shared global continuous bin edges in the vectorized path, encode categorical outcome tuples into a category axis, preserve per-trial likelihood lookup, and retain NumPy fallback behavior when boost_histogram is unavailable. This gives higher-trial workloads a way to avoid many small histogram fills without changing default numerical behavior.

Expose --histogram-vectorized on the DDM and stability-flexibility benchmark scripts, include the vectorized setting in benchmark output, and fix the stability-flexibility benchmark trial sequence generation so --num-trials values above 240 generate a valid counterbalanced sequence before slicing.

Add unit coverage for vectorized histogram likelihoods against a hand-computed categorical case and for vectorized boost fallback to NumPy. Update PEC likelihood_estimator_kwargs documentation to include vectorized.

Validation: python -m py_compile for touched Python files; pytest tests/composition/pec/test_parameterestimationcomposition.py -q -n 0 -k 'simulation_likelihood or pec_log_likelihood'. Benchmarked local vs vectorized histogram fills at 50 and 300 trials; vectorized mode is workload-dependent and remains opt-in.

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27069361069 feat/pec_hist perf: add vectorized PEC histogram fill mode Add an opt-in vectorized histogram likelihood path for PEC data fitting. The new path keeps the existing local per-trial/per-category histogram estimator as the default, but allows histogram users to p... push davidt0x github
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