realML primitives
- realML.matrix: fast least absolute deviations regression, fast robust (entrywise-l1 norm) low-rank matrix decomposition, sufficient dimensionality reduction (non-linear low-rank factorization)
- realML.kernel: preconditioned Gaussian and Polynomial regression, tensor machines for adaptive polynomial features
Release files for realML 3.0.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| realML-3.0.3.tar.gz | 46.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| realML-3.0.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 146.3 kB
Release files / realML-3.0.3.tar.gz
| Download URL | realML-3.0.3.tar.gz |
|---|---|
| Size | 46.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.2.0 requests-toolbelt/0.8.0 tqdm/4.30.0 CPython/3.8.10
|
Release files / realML-3.0.3-py3-none-any.whl
| Download URL | realML-3.0.3-py3-none-any.whl |
|---|---|
| Size | 100.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
5452a9c3da3748503b57208babc01547de3f16b3c54758088794b224875dce95
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BLAKE2b-256 checksum How to use checksums |
5321882641aec5d217d422843607d81e8bfd6ab7d1b5b0e2a0b06c94a79813f7
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.2.0 requests-toolbelt/0.8.0 tqdm/4.30.0 CPython/3.8.10
|
