{"id":1391,"url":"https://github.com/rugheid/Swift-MathEagle","last_synced_at":"2025-08-02T04:30:57.646Z","repository":{"id":26584942,"uuid":"30039483","full_name":"rugheid/Swift-MathEagle","owner":"rugheid","description":"A general math framework to make using math easy. 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The syntax is designed to be as simple as\npossible. Contributors are always welcome, the more the merrier!\n\n## Warning!\nMathEagle is still in its initial development phase so the syntax still changes frequently.\n\n# Installation\nThe fastest way to add MathEagle to your project is to download it as a zip file and drag all files in the MathEagle/Source\nfolder to your project. Make sure to add them to your target. For Mac applications you can use the generated framework,\ncheck the wiki for more information. I'm currently working on an iOS framework.\n\nMathEagle requires gmp (GNU multiple precision arithmetic library) be installed.  Install gmp via\n``brew install gmp`` assuming you have Homebrew installed.\n\n# Supported Xcode Versions\nMathEagle is fully compatible with:\n- Xcode 9 and Swift 4\n\n# Currently Supported\n* Basic extensions, operators like power (** operator) and functions like sign and factorial\n* Prime functions\n* Complex numbers\n* Single variable function root solving\n* Single variable function optimization\n* Matrix and Vector math\n* Basic graph algorithms\n\nMathEagle is fully generic, so almost all classes and functions work with any type (even types you define). MathEagle\nvectors for example can also be of type Int, while the Float and Double type vectors use Accelerate in the background\nfor speed.\n\nTake a look at the Wiki for documentation.\n\n# Advantages\nThe biggest advantage of MathEagle is that it's totally generic. You can create matrices and vectors and use functions\nlike isPrime with all numeric types. You can even create your own type (let's say Quaternion), implement the protocols\nand everything just works. Of course specific implementations are overloaded to maintain performance. Vector addition\nfor Float vectors for example is overloaded to use the Accelerate framework.\n\n# Benchmarking\nFirst benchmarking tests show that MathEagle is as fast as Python's famous numpy. You can check the \"Benchmarking Files\"\nfolder in MathEagle's tests. (These tests were conducted on an early 2013 15-inch Retina Macbook Pro with a\n2.7 GHz Intel Core i7 and 16 GB of RAM.\n\nHere are some plots for Vector addition:\n![Vector Addition Float Benchmarking](/MathEagleTests/Benchmarking Files/Plot Images/VectorAdditionFloat.png)\n![Vector Addition Double Benchmarking](/MathEagleTests/Benchmarking Files/Plot Images/VectorAdditionDouble.png)\n\n# To Do\n- [ ] Documentation\n- [ ] Further General Development\n- [ ] Incorporate Accelerate for all applications\n","funding_links":[],"categories":["Math"],"sub_categories":["Other Hardware","Other free courses"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frugheid%2FSwift-MathEagle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frugheid%2FSwift-MathEagle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frugheid%2FSwift-MathEagle/lists"}