Metadata-Version: 2.1
Name: eigpca
Version: 0.1.0
Summary: Principal Component Analysis via eigen-decomposition of the covariance/correlation matrix
Home-page: https://github.com/sercandogan/eigpca/
License: MIT
Keywords: PCA,eigen-decomposition
Author: Sercan Dogan
Author-email: sercandogan@yandex.com
Maintainer: Sercan Dogan
Maintainer-email: sercandogan@yandex.com
Requires-Python: >=3.7,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Dist: matplotlib (>=3.2.1,<4.0.0)
Requires-Dist: numpy (>=1.18.2,<2.0.0)
Project-URL: Repository, https://github.com/sercandogan/eigpca/
Project-URL: issues, https://github.com/sercandogan/eigpca/issues
Description-Content-Type: text/markdown

# eigpca
PCA via eigen-decomposition of the covariance/correlation matrix.

# Install
```bash
pip install eigpca
```

# Example
```python
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
```python
pca.plot(y="pov")
```
![Scree plot](examples/scree_plot_iris.png)


