pypelearn
A pipeline for testing and validation of synthetic machine learning data
pypelearn is a Python framework for handling and processing high-dimensional time series data.
For full documentation, please visit https://harston.io/pypelearn
pypelearn utilises numpy, pandas, matplotlib, and statsmodels to allow linear and nonlinear modelling of high-dimensional time series tensors.
1 maintainer
Release files for pypelearn 0.0.20
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pypelearn-0.0.20.tar.gz | 9.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pypelearn-0.0.20-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.2 kB
Release files / pypelearn-0.0.20.tar.gz
| Download URL | pypelearn-0.0.20.tar.gz |
|---|---|
| Size | 9.8 kB |
| Tags | Source |
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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 |
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twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.6.5
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Release files / pypelearn-0.0.20-py3-none-any.whl
| Download URL | pypelearn-0.0.20-py3-none-any.whl |
|---|---|
| Size | 11.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/39.2.0 requests-toolbelt/0.8.0 tqdm/4.26.0 CPython/3.6.5
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