Metadata-Version: 2.1
Name: scase
Version: 0.1.1
Summary: Spectral Embedding Using Deep Neural Networks
Home-page: https://github.com/shaham-lab/ScaSE.git
Author: Nir Ben-Ari
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/shaham-lab/ScaSE/issues
Description: # ScaSE
        
        <p align="center">
        
        [//]: # (    <img src="https://github.com/shaham-lab/SpectralNet/blob/main/figures/twomoons.png">)
        
        ScaSE is a Python package that performs Spectral Embedding with deep neural networks.<br><br>
        This package is based on the following paper - [ScaSE]()
        
        [//]: # (## Installation)
        
        [//]: # (You can install the latest package version via)
        
        [//]: # (```bash)
        [//]: # (pip install spectralnet)
        [//]: # (```)
        
        ## Usage
        
        The basic functionality is quite intuitive and easy to use, e.g.,
        
        ```python
        from scase import ScaSE
        
        scase = ScaSE(n_components=10) # n_components is the number of dimensions in the low-dimensional representation
        scase.fit(X) # X is the dataset and it should be a torch.Tensor
        X_reduced = scase.transfrom(X) # Get the low-dimensional representation of the dataset
        Y_reduced = scase.transform(Y) # Get the low-dimensional representation of a test dataset
        
        ```
        
        You can read the code docs for more information and functionalities.<br>
        
        #### Running examples
        
        In order to run the model on the moon dataset, you can either run the file, or using the command-line command:<br>
        `python -m examples.reduce_moon`<br>
        This will run the model on the moon dataset and plot the results.
        
        The same can be done for the circles dataset:<br>
        `python -m examples.reduce_circles`<br>
        
        
        [//]: # (## Citation)
        
        [//]: # ()
        [//]: # (```)
        
        [//]: # ()
        [//]: # (@inproceedings{shaham2018,)
        
        [//]: # (author = {Uri Shaham and Kelly Stanton and Henri Li and Boaz Nadler and Ronen Basri and Yuval Kluger},)
        
        [//]: # (title = {SpectralNet: Spectral Clustering Using Deep Neural Networks},)
        
        [//]: # (booktitle = {Proc. ICLR 2018},)
        
        [//]: # (year = {2018})
        
        [//]: # (})
        
        [//]: # ()
        [//]: # (```)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
