Metadata-Version: 2.1
Name: tsne-torch
Version: 1.0.1
Summary: t-SNE accelerated with PyTorch
Home-page: UNKNOWN
Author: Xiao Li, Palle Klewitz
License: MIT
Project-URL: Source Code, https://github.com/palle-k/tsne-pytorch
Description: # t-SNE pytorch Implementation with CUDA
        CUDA-accelerated PyTorch implementation of the t-stochastic neighbor embedding algorithm described in [Visualizing Data using t-SNE](https://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf). 
        
        ## Installation
        
        Requires Python 3.7
        
        ### Install via Pip
        
        ```bash
        pip3 install tsne-torch
        ```
        
        ### Install from Source
        
        ```bash
        git clone https://github.com/palle-k/tsne-pytorch.git
        cd tsne-pytorch
        python3 setup.py install
        ```
        
        ## Usage
        
        ```python
        from tsne_torch import TorchTSNE as TSNE
        
        X = ...  # shape (n_samples, d)
        X_emb = TSNE(n_components=2, perplexity=30, n_iter=1000, verbose=True).fit_transform(X)  # returns shape (n_samples, 2)
        ```
        
        ## Command-Line Usage
        
        ```bash
        python3 -m tsne_torch --xfile <path> --yfile <path>
        ```
        
        ## Example
        
        This is our result compare to result of python implementation.
        * PyTorch result
        
        ![pytorch result](https://github.com/palle-k/tsne-pytorch/raw/master/images/pytorch.png)
        * python result
        
        ![python result](https://github.com/palle-k/tsne-pytorch/raw/master/images/python.png)
        
        ## Credit
        This code highly inspired by 
        * author's python implementation code [here](https://lvdmaaten.github.io/tsne/).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
