{"action":"published","release":{"url":"https://api.github.com/repos/impressivemachines/Metaphor/releases/1096790","assets_url":"https://api.github.com/repos/impressivemachines/Metaphor/releases/1096790/assets","upload_url":"https://uploads.github.com/repos/impressivemachines/Metaphor/releases/1096790/assets{?name}","html_url":"https://github.com/impressivemachines/Metaphor/releases/tag/1.0.1","id":1096790,"tag_name":"1.0.1","target_commitish":"master","name":"Metaphor C++ library for linear algebra and numerical computation","draft":false,"author":{"login":"impressivemachines","id":4986842,"avatar_url":"https://avatars.githubusercontent.com/u/4986842?v=3","gravatar_id":"","url":"https://api.github.com/users/impressivemachines","html_url":"https://github.com/impressivemachines","followers_url":"https://api.github.com/users/impressivemachines/followers","following_url":"https://api.github.com/users/impressivemachines/following{/other_user}","gists_url":"https://api.github.com/users/impressivemachines/gists{/gist_id}","starred_url":"https://api.github.com/users/impressivemachines/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/impressivemachines/subscriptions","organizations_url":"https://api.github.com/users/impressivemachines/orgs","repos_url":"https://api.github.com/users/impressivemachines/repos","events_url":"https://api.github.com/users/impressivemachines/events{/privacy}","received_events_url":"https://api.github.com/users/impressivemachines/received_events","type":"User","site_admin":false},"prerelease":false,"created_at":"2015-03-27T03:53:57Z","published_at":"2015-03-27T04:00:53Z","assets":[],"tarball_url":"https://api.github.com/repos/impressivemachines/Metaphor/tarball/1.0.1","zipball_url":"https://api.github.com/repos/impressivemachines/Metaphor/zipball/1.0.1","body":"Version 1.0.1 removes some test code that was a work in progress.\r\n\r\nMetaphor/Core provides many common tools for linear algebra and statistics and is built on very flexible matrix and vector objects. The features include:\r\n\r\n* General purpose light-weight matrix and vector view classes:\r\n    * View objects can reference existing data with arbitrary signed row and column stride.\r\n    * Any data type supported.\r\n    * Many common data operations, such as sub-views copying, re-ordering, and decimation.\r\n    * Matrices and vectors can be saved and printed in a variety of formats, including Matlab text format.\r\n* Linear algebra operations for float, double, and complex types:\r\n    * A full suite of optimized standard BLAS operations supporting BLAS 1, 2 and 3.\r\n    * Optimized low-level matrix block math functions which work on vector and matrix view types.\r\n    * Math operations include data sorting, conversion, statistics, and reduce operations.\r\n* Matrix and vector memory objects\r\n    * Tightly integrated with view objects\r\n    * Offer a multitude of member functions for common linear algebra operations.\r\n    * Use shared pointers to memory to avoid data copying.\r\n* Typical matrix decompositions:\r\n    * LU decomposition.\r\n    * Cholesky LDLT and LLT decomposition.\r\n    * QR decomposition with and without pivoting.\r\n    * Tridiagonal decomposition.\r\n    * Eigenvalue decomposition for symmetric matrices.\r\n    * Jacobi SVD with QR preconditioning.\r\n    * Special case 2x2 Eigen and SVD decompositions.\r\n* Signal processing:\r\n    * Fast block-based convolution for 1D and 2D signals.\r\n    * Arbitrary radix FFT in 1D and 2D.\r\n* Statistics:\r\n    * Convenient interfaces for random number generation.\r\n    * Principal components analysis.\r\n    * Estimation of covariances.\r\n    * Generation of multidimensional Gaussian noise with specified covariance.\r\n    * Computation of multivariate Gaussians and Mahalanobis distance.\r\n* Solution to quadratic and cubic polynomials.\r\n* Quadratic fitting and interpolation in 1D and 2D.\r\n* Least squares and robust 2D line fitting.\r\n* Multi-dimensional regression.\r\n* Geometric 2D and 3D rotations and transforms.\r\n* Quaternions with SLERP and rotation matrix / euler / axis-angle integration.\r\n* Numerical root finding for scalar functions.\r\n* Optimization of functions:\r\n    * Bracketing, line minimization, and line minimization with derivatives.\r\n    * Levenberg Marquardt minimization.\r\n    * Stochastic gradients with optional momentum or acceleration.\r\n    * Powell minimization.\r\n    * Conjugate gradients.\r\n    * BFGS.\r\n    * Limited memory BFGS."}}