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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Fast NNLS (FNNLS) is an optimized version of the Lawson–Hanson algorithm. [2] Other algorithms include variants of Landweber 's gradient descent method [ 10 ] and coordinate-wise optimization based on the quadratic programming problem above.
scikit-learn, a popular machine learning library in Python implements t-SNE with both exact solutions and the Barnes-Hut approximation. Tensorboard, the visualization kit associated with TensorFlow, also implements t-SNE (online version) The Julia package TSne implements t-SNE
Open-source, cross-platform C library to generate PDF files. OpenPDF: GNU LGPLv3 / MPLv2.0: Open source library to create and manipulate PDF files in Java. Fork of an older version of iText, but with the original LGPL / MPL license. PDFsharp: MIT C# developer library to create, extract, edit PDF files. Poppler: GNU GPL
[8] [12] In February 2011, some of the authors of the original L-BFGS-B code posted a major update (version 3.0). A reference implementation in Fortran 77 (and with a Fortran 90 interface). [13] [14] This version, as well as older versions, has been converted to many other languages. An OWL-QN C++ implementation by its designers. [3] [15]
Python has the statsmodelsS package which includes many models and functions for time series analysis, including ARMA. Formerly part of the scikit-learn library, it is now stand-alone and integrates well with Pandas. PyFlux has a Python-based implementation of ARIMAX models, including Bayesian ARIMAX models.
Standardized coefficients shown as a function of proportion of shrinkage. In statistics, least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani.