Metadata-Version: 2.1
Name: dimensionality-reductions-jmsv
Version: 0.1.0
Summary: Package with the PCA, SVD and t-SNE methods for dimensionality reduction
Home-page: https://pypi.org/project/dimensionality_reductions_jmsv/#history
License: MIT
Keywords: SVD,PCA,t-SNE
Author: Mauricio Sierra
Maintainer: Send_Mail
Maintainer-email: mauricio@gmail.com
Requires-Python: >=3.10,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: numpy (>=1.24.2,<2.0.0)
Project-URL: Repository, https://github.com/mauriciosierrav/dimensionality-reduction-jmsv.git
Description-Content-Type: text/markdown

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### What is it?

**dimensionality_reductions_jmsv** is a Python package that provides three methods (PCA, SVD, t-SNE) to apply dimensionality reduction to any dataset.

### Installing the package

Requests is available on PyPI:

```bash
pip install dimensionality_reductions_jmsv
```

**_Try your first TensorFlow program_**

```python
from dimensionality_reductions_jmsv.decomposition import PCA
import numpy as np

X = (np.random.rand(10, 10) * 10).astype(int)
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
print("Original Matrix:", '\n', X, '\n')
print("Apply dimensionality reduction with PCA to Original Matrix:", '\n', X_pca)
```

### License
[MIT](https://mit-license.org/)

