Tste.py -

(Lambda) : Regularization parameter to prevent the points from flying too far apart.

(Alpha) : Degrees of freedom for the Student-t distribution (usually set to is dimensions). tste.py

: If the embedding looks like a random "ball," try lowering the learning rate. 📊 When to use t-STE vs. t-SNE Learning to Taste A Multimodal Wine Dataset (Lambda) : Regularization parameter to prevent the points

You can typically execute it via terminal. Parameters often include the number of dimensions (usually 2 or 3) and the number of objects: 📊 When to use t-STE vs

python tste.py --triplets triplets.txt --n_objects 100 --n_dims 2 Use code with caution. Copied to clipboard 3. Key Parameters to Tune

Your input file (e.g., triplets.txt ) should contain zero-indexed integer IDs: 0 1 2 5 3 8 2 0 4 Use code with caution. Copied to clipboard (Meaning: Object 0 is more like Object 1 than Object 2) 2. Run the Embedding