{"library":"opentsne","title":"openTSNE","description":"Extensible, parallel implementations of t-SNE for visualizing high-dimensional data. Supports multiple affinity models (e.g., multiscale mixture), optimization via Barnes-Hut or FIt-SNE, and out-of-sample embedding. Current version 1.0.4, requires Python >=3.9, with low release cadence (major/minor every few years).","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install opentsne","pip install opentsne[gpu]"],"cli":{"name":"opentsne","version":"sh: 1: opentsne: not found"}},"imports":["from openTSNE import TSNE","from openTSNE import TSNEEmbedding","from openTSNE.affinity import Affinity","from openTSNE.affinity import MultiscaleMixture"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from openTSNE import TSNE\nfrom sklearn.datasets import load_iris\nimport numpy as np\n\niris = load_iris()\nX = iris.data\ny = iris.target\n\n# Initialize with default parameters (multiscale perplexity = [50, 500])\ntsne = TSNE(random_state=42, verbose=False)\nembedding = tsne.fit(X)\n\nprint(embedding.shape)  # (150, 2)","lang":"python","description":"Basic t-SNE embedding using default multiscale mixture affinity.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}