{"library":"scikit-dimension","title":"scikit-dimension","description":"scikit-dimension is a Python module for intrinsic dimension estimation built according to the scikit-learn API. It provides estimators for global and local intrinsic dimension, supporting methods like MLE, DANCo, kNN, and others. Current version 0.3.4 is released under the 3-Clause BSD license. Release cadence is irregular.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install scikit-dimension"],"cli":null},"imports":["from skdim.id import MLE","from skdim.id import DANCo","from skdim.id import kNN","from skdim.id import TLE","from skdim.id import EigValue","from skdim.id import FisherS","from skdim.id import CorrInt","from skdim.id import LPCA","from skdim.id import MADA","from skdim.id import MOM"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from skdim.id import MLE\nimport numpy as np\n\n# Generate synthetic data\nX = np.random.rand(100, 10)\n\n# Estimate intrinsic dimension\nmle = MLE()\ndim = mle.fit_transform(X)\nprint('Estimated dimension:', dim)","lang":"python","description":"Basic usage of MLE estimator on random data.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}