ZCA whitening in Python with a Scikit-Learn like interface.
Usage
from zca import ZCA
import numpy as np
X = np.random.random((10000, 15)) # data array
trf = ZCA().fit(X)
X_whitened = trf.transform(X)
X_reconstructed = trf.inverse_transform(X_whitened)
assert(np.allclose(X, X_reconstructed)) # TrueInstallation
pip install -U zcaLicence
GPLv3
1 maintainer
Metadata
1 maintainer
Release files for zca 0.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| zca-0.1.1.tar.gz | 15.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| zca-0.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 30.1 kB
Release files / zca-0.1.1.tar.gz
| Download URL | zca-0.1.1.tar.gz |
|---|---|
| Size | 15.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/4.0.2 CPython/3.10.10
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Release files / zca-0.1.1-py3-none-any.whl
| Download URL | zca-0.1.1-py3-none-any.whl |
|---|---|
| Size | 15.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.2 CPython/3.10.10
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