{"repo":"dragonfly-ai/slash","free":true,"listed":false,"github":"https://github.com/dragonfly-ai/slash","clone":"git clone https://github.com/dragonfly-ai/slash.git","description":"Linear Algebra and Statistics library for Scala.js, JVM, and Native.","language":"Scala","stars":41,"topics":["scalajs","scala3","linear-algebra","machine-learning","matrix","matrix-decomposition","principal-component-analysis","scala-native","statistics","vector"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"S.L.A.S.H Scala Linear Algebra & Statistics Hacks Design goals: Cross compile to JVM, Native, and JavaScript platforms Maximize performance Minimize memory footprint Provide convenient syntax Seamlessly interoperate with other math libraries Seamlessly interoperate with native languages like JavaScript, C/C++, and Java Serialize efficiently and compactly by default sbt Features: - High performance Vector data types with convenient vector math syntax. - Probability Distributions, Parametric and Estimated (Online/Streaming): Gaussian/Normal, Poisson, LogNormal, Binomial (parametric only), Beta, and PERT; each with support for sampling and probability density functions, PDFs. - Sampleable trait for making types into generative models. - Math functions: Beta, Factorial, and Gamma functions: B(α, β), x! and Γ(x). Convenience macros, methods, and case classes for computing logarithms of arbitrary base. - Geometry: Sample points uniformly from volumes defined by 3D tetrahedrons. - Bresenham Line Drawing Algorithm that invokes a lambda for each discrete point on a line. - Kernels: Gaussian, Epanechnikov, Uniform, and Discrete. - Flexible Histogram data structures with Console friendly Text Based Visualizations inspired by Julia Plots . - Bijection[A, B]: an abstraction for bijective implicit conversions. - BigRandom: scala.util.Random extension methods to generate random BigInt and BigDecimal values. - Interval and Domain types and objects with support for random sampling. - Unicode ","default_branch":null,"files":null,"tree":[],"storefront":"/r/dragonfly-ai","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/dragonfly-ai/slash/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}