mdespriee opened a new pull request #12489: [WIP] Symbol Random api URL: https://github.com/apache/incubator-mxnet/pull/12489 ## Description ## Introduce Symbol.random module in scala API ## Checklist ## ### Essentials ### Please feel free to remove inapplicable items for your PR. - [ ] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes) - [ ] Changes are complete (i.e. I finished coding on this PR) - [ ] All changes have test coverage: - Unit tests are added for small changes to verify correctness (e.g. adding a new operator) - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore) - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL) - [ ] Code is well-documented: - For user-facing API changes, API doc string has been updated. - For new C++ functions in header files, their functionalities and arguments are documented. - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable - Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change ### Changes ### - Symbol.random api generated by macros ## Comments ## - Is there any JIRA mentioning this feature ? - Only implemented for Symbol. Adding the same for NDArray seems quite straightforward. Tell me if I should to it in the same PR or in a separate - The produced API is not strictly similar to the one in python: * in python, each function, eg random.normal, has 2 overrides, one scalar, one symbolic. * the generated scala api has Symbol.random.sample_normal with symbol inputs, and Symbol.random.random_normal for scalars. The arguments naming is also not fully consistent (mu & sigma, vs loc & scale). => What's your recommendations on this ? - The unit-test is poor. What should be tested for generated code like this ?
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