Devel #2578
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Devel #2578
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Merge pull request from PrincetonUniversity/master
Add missing pytest.mark.composition to tests that construct and run Composition. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
Pulls in pytorch[0] so it needs the same constraints. [0] https://github.com/ModECI/MDF/blob/main/setup.cfg Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
… bumped package before installing PNL (#2481) Some packages may pull in updated dependencies that cause issues when rolled back. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…p packages (#2482) The packages should be rebuilt if some of the shared dependencies change Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…for not installed packages (#2483) This happens if the package is an 'extras' dependency. e.g. a 'doc' or 'cuda' dependency not installed in 'dev' CI jobs. Fixes package bumps of docs dependencies. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
Many psyneulink Functions must be split into several MDF functions/expressions to reproduce their behavior. Instead of adding these supplemental functions on the owners of Functions, add Function._assign_to_mdf_model that adds anything necessary to the owner
MDF: support DriftDiffusionIntegrator
Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…on test Pass the modulator value directly from input. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…llvm_is_finished_cond' Passing base parameters matches invocation by the scheduler. Any modulation needs to be applied explicitly. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…threshold. Matches Python semantics. Add threshold modulation test for integrator based DDM. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…ant (#2490) Consistently use modulated mechanism parameters (e.g. threshold) of TransferMechanism in is_finished function whether it's called from inside the mechanism loop, or by the scheduler. Use modulated function parameters in DDM's 'is_finished' function in both internal and scheduler invocation.
scipy doesn't provide 32-bit wheels for >=1.9.2 Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…0,<1.13.0 (#2504) Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…2505) The only supported evaluation type atm is "evaluate_type_objective", which uses the result of ocm.objective_mechanism as output (combined with costs). Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
jupyter pulls in jupyter-server-terminals which requires pywinpty>=2.0.3 on windows [0] [0] https://github.com/jupyter-server/jupyter_server_terminals/blob/main/pyproject.toml Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…tation (#2560) Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…anch name (#2566) Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…d in MechanismError (#2569) string values of errors are not stable and can change between versions. Fixes 'input_list_of_strings' tests when using numpy>=1.22.x. Refactor handling of input port execution exceptions to only check calls to 'execute', parameter set/get do not throw exceptions. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…tions Individual terms of the sum are close in magnitude. Instead of accmulating all the postive terms, alternate between positive and negative terms. This keeps the intermadiate/partial results small in magnitude improving accuracy of the floating point expression. This is enough to make the "test_drift_difussion_analytical_shenhav_compat_mode" when using numpy-1.22 with its new AVX512 based floating point operations [0] [0] numpy/numpy@1eff1c5 Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
… operations Individual terms of the sum are close in magnitude. Instead of accmulating all the postive terms, alternate between positive and negative terms. This keeps the intermadiate/partial results small in magnitude improving accuracy of the floating point expression. This matches the updated Python version. Fix use of incorrect terms. Skew calculation needs cubic terms. Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…22 and AVX512 CPUs Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
Updates the requirements on [numpy](https://github.com/numpy/numpy) to permit the latest 1.22.x release. - [Release notes](https://github.com/numpy/numpy/releases) - [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst) - [Commits](numpy/numpy@v1.17.0...v1.22.4) Signed-off-by: Jan Vesely <jan.vesely@rutgers.edu>
…function and update numpy to <1.22.5 (#2476) numpy-1.22 introduced a new set of optimized floating point routines on CPUs that support AVX512 instructions[0]. Reassociate operations in the DriftDiffusionAnalytical function to improve accuracy, this is enough to pass the "test_drift_difussion_analytical_shenhav_compat_mode" test that otherwise fails when using numpy 1.22.x on CPUs that support AVX512 instructions. Update results in tests/functions/test_distribution.py::test_execute[DriftDiffusionAnalytical..] to match the new reassocaited routines for small drift rate and fp32 precision. Use windows/mac expected results on CPUs that support AVX512 when numpy>=1.22 is used. Allow numpy < 1.22.5 Closes: #2572 [0] numpy/numpy@1eff1c5
* • function.py
- Function_Base:
move arguments referring to parameters in call to function to params arg
(to be treated as runtime_params)
• memoryfunctions.py
- ContentAddressableMemory: add store and retrieve convenience methods
* -
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CodeQL found more than 10 potential problems in the proposed changes. Check the Files changed tab for more details.
* master: Add CodeQL workflow for GitHub code scanning (#2533)
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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. |
jdcpni
approved these changes
Jan 14, 2023
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