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The Computational Geometry Algorithms Library (CGAL) is an open source software library of computational geometry algorithms. While primarily written in C++, Scilab bindings and bindings generated with SWIG (supporting Python and Java for now) are also available. [2] [3] The software is available under dual licensing scheme.
Geometry of a hand and some examples of measurements that can be taken by hand geometry reading devices. [1] A hand geometry reading device with pegs to control the placement of the hand. Angled mirror on the left reflects the side view image of the hand to the camera. A CCD camera is beneath the keypad to take the top view image of the hand ...
Boost.Geometry support Douglas–Peucker simplification algorithm; Implementation of Ramer–Douglas–Peucker and many other simplification algorithms with open source licence in C++; XSLT implementation of the algorithm for use with KML data. You can see the algorithm applied to a GPS log from a bike ride at the bottom of this page
CGAL is an opensource Computational Geometry Algorithms Library which has support for boolean operations on Polyhedra; but no sweep, revolve or NURBS. Open CASCADE is an opensource modeling kernel. sgCore is a freeware proprietary modeling kernel distributed as an SDK. [9] K3 kernel [10] is developed by Center GeoS.
Since 7 October 2024, Python 3.13 is the latest stable release, and it and, for few more months, 3.12 are the only releases with active support including for bug fixes (as opposed to just for security) and Python 3.9, [55] is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.8 reaching end-of-life.
Provides classification and regression datasets in a standardized format that are accessible through a Python API. Metatext NLP: https://metatext.io/datasets web repository maintained by community, containing nearly 1000 benchmark datasets, and counting. Provides many tasks from classification to QA, and various languages from English ...
Real high-dimensional data is typically sparse, and tends to have relevant low dimensional features. One task of TDA is to provide a precise characterization of this fact. For example, the trajectory of a simple predator-prey system governed by the Lotka–Volterra equations [1] forms a closed circle in state space. TDA provides tools to detect ...
Wu's method is powerful for mechanical theorem proving in elementary geometry, and provides a complete decision process for certain classes of problem. It has been used in research in his laboratory (KLMM, Key Laboratory of Mathematics Mechanization in Chinese Academy of Science) and around the world.