Implementation of the tSNE embedding enabling streaming visualization.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
app
.gitignore
LICENSE
README.md
buffers.py
data.py
embedding.py
illustration.jpg
server.py

README.md

Computing tSNE embeddings in streaming

For more details read the blog post. As explained, at the moment we only have "fake" streaming. Stay tuned.

demo-app

TODO

Server

Install

For less trouble, setup python3 with anaconda. Missing packages can be installed with 'pip install X Y Z'

Configuration

To use with your own data you'll need to define in data.py:

  • how to load your data samples. We offer digits by default.
  • which pipeline you use for feature engineering.

Then you may want to change learners used in embedding.py. We use SVR at the moment. To do a seach for decent calibration run:

python embedding.py

Note that this tool can we used for real-time update of regression results, whatever that means.

Run

python server.py

Client

Have nodejs ready, and it should be as simple as:

cd app
npm install
npm start

If you want to change ports, etc, modify the .webpack.config files.