Parameter fitting of compositions using MLE via probability density approximation #1920
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…Link into pred_prey_opt
…Link into pred_prey_opt
…Link into pred_prey_opt
…Link into model-fit # Conflicts: # psyneulink/components/functions/function.py # psyneulink/components/mechanisms/mechanism.py # psyneulink/components/system.py # psyneulink/globals/environment.py # psyneulink/library/subsystems/evc/evcauxiliary.py
The standard deviation of the Gaussian noise for the DDM is conventionally defined as noise * sqrt(dt). See Bogacz et al., Eq 6.
Default value of 1000 is arbitrary, should be unlimited in my opinion. When time_step_size gets below 0.001 then 1000 steps are often needed. Setting the sys.maxsize make its practically unlimited for now. This is a hack though, maybe there is a way to disable the limit but I can't find it in the docs.
- _sequential_evaluate: implement same_randomization_for_all_allocations
_function: re-removed num_estiamtes loop, and use np.apply to apply aggregation_function
- _function: refactored to put use aggregation_function at end - _grid_evaluate: still needs to return all_samples
- _function: refactored to put use aggregation_function at end - _grid_evaluate: still needs to return all_samples
- _gen_llvm_evaluate_function: num_estimates -> num_trials_per_estimate
…teger Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
get_param_struct_type(): restored test on num_estimates
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This PR causes the following changes to the html docs (ubuntu-latest-3.7-x64): See CI logs for the full diff. |
GradientOptimization was invoking the base class _function implementation which is an abstract method now. It now invokes _evaluate instead to get the old behaviour.
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This PR causes the following changes to the html docs (ubuntu-latest-3.7-x64): See CI logs for the full diff. |
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This pull request introduces 32 alerts and fixes 1 when merging 657cb78 into c7c172b - view on LGTM.com new alerts:
fixed alerts:
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Initial implementation of parameter estimation with compositions using maximum likelihood estimation (MLE) via probability density approximation (PDA).