repsim (Python)
Representational Similarity Metrics for Machine Learning and Neuroscience
repsim provides efficient implementations of representational similarity measures using a C++17 backend via pybind11 and Eigen, whose vendored version is included under third_party/eigen for reproducible builds. It parallels the R package repsim, ensuring consistency across environments.
1 maintainer
Metadata
1 maintainer
Release files for repsim 0.1.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| repsim-0.1.5.tar.gz | 1.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| repsim-0.1.5-cp312-cp312-macosx_15_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 15.0+ ARM64 | Details |
Total release size: 1.5 MB
Release files / repsim-0.1.5.tar.gz
| Download URL | repsim-0.1.5.tar.gz |
|---|---|
| Size | 1.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
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Release files / repsim-0.1.5-cp312-cp312-macosx_15_0_arm64.whl
| Download URL | repsim-0.1.5-cp312-cp312-macosx_15_0_arm64.whl |
|---|---|
| Size | 146.8 kB |
| Tags | CPython 3.12 macOS 15.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.12
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