Principal Component Analysis via eigen-decomposition of the covariance/correlation matrix
Project description
eigpca
PCA via eigen-decomposition of the covariance/correlation matrix.
Install
pip install eigpca
Example
from eigpca import PCA
from sklearn.datasets import load_iris
X = load_iris().data
pca = PCA()
pca.fit(X)
pca.transform(X, n_components=2)
Scree Plot
pca.plot(y="pov")
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
eigpca-0.1.0.tar.gz
(3.8 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file eigpca-0.1.0.tar.gz.
File metadata
- Download URL: eigpca-0.1.0.tar.gz
- Upload date:
- Size: 3.8 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.0.5 CPython/2.7.10 Darwin/18.0.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8e821b44fbe8a897cadd4ac64a1bd01b43121e13a26f0b5cfac503572cfa90e4
|
|
| MD5 |
e55e5f470574a20324e02ec0d8082fe2
|
|
| BLAKE2b-256 |
655df5963d6a9f9dfc637eb69c7430e6005c1883014b61e7eae219466b256c01
|
File details
Details for the file eigpca-0.1.0-py3-none-any.whl.
File metadata
- Download URL: eigpca-0.1.0-py3-none-any.whl
- Upload date:
- Size: 3.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.0.5 CPython/2.7.10 Darwin/18.0.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
84c365da6d2b58425ee5276e3dd52a4f83d97c0b2706484beb7644c3dabcb804
|
|
| MD5 |
bd66c9cf233b22b59e195352e7d49e67
|
|
| BLAKE2b-256 |
818eeb862c0c1d47ff15eb0130bdef771f15abe44c9da29212cd9c5566a5010d
|
