{"library":"pyriemann","title":"pyRiemann","description":"A Python library for classification and clustering of multivariate data using Riemannian geometry. Provides tools for covariance matrix estimation, geodesic filtering, tangent space mapping, and various classifiers on the manifold of symmetric positive definite matrices. Currently at version 0.11, with semi-annual releases.","language":"python","status":"active","last_verified":"Sat May 09","install":{"commands":["pip install pyriemann"],"cli":null},"imports":["from pyriemann.estimation import Covariances","from pyriemann.classification import MDM","from pyriemann.tangentspace import TangentSpace"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from pyriemann.estimation import Covariances\nfrom pyriemann.classification import MDM\nimport numpy as np\n\n# Generate toy data: 10 trials, 3 channels, 100 time points\nX = np.random.randn(10, 3, 100)\ny = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])\n\n# Estimate covariance matrices\ncov = Covariances().fit_transform(X)\n\n# Classify using Minimum Distance to Mean\nclf = MDM().fit(cov, y)\npred = clf.predict(cov)\nprint(pred)","lang":"python","description":"Estimate covariance matrices from multivariate time series and classify with Riemannian distance.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}