Found 43 packages
Provides a .NET implementation to the diff algorithm (shortest edit script) described by Eugene Myers in "An O(ND) Difference Algorithm and Its Variations". Unlike some other implementations, this one can compare sequences of any object type, using the standard Equals method or a custom IEqualityComparer. This project uses Semantic Versioning (https://semver.org/).
JSON object diffs and reversible patching
DiffPlex is a diffing library that allows you to programmatically create text diffs. DiffPlex is a fast and tested library.
Differencing algorithms for text files, binary files, and directories
Windows Forms controls for showing diffs of text, files, and directories
DiffSharp is a tensor library with support for differentiable programming. It is designed for use in machine learning, probabilistic programming, optimization and other domains. For documentation and installation instructions visit: https://diffsharp.github.io/
A library for producing diffs of images with highlighting.
Create snapshots of any .Net object, compare them and create a structured diff; C#; .Net2.0; .Net4.0
DiffPlex.Wpf is a WPF control library that allows you to programatically render visual text diffs in your application. It also provide a diff viewer control used in Windows Forms application.
Robust, easy-to-use diff and patch library for comparing and synchronising text with zero dependencies. Provides useful extension methods to generate diff and patch operations for strings, generating HTML for visualising diffs, and applying patch operations to text. Based on Google's diff-match-patch library. Copyright (c) Cloudey IT Ltd For diff-match-patch library: Copyright (c) 2018 The diff-match-patch Authors (https://github.com/google/diff-match-patch) Licensed under Apache 2.0, with modifications from package authors
DiffSharp is an automatic differentiation (AD) library. AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow. Using the DiffSharp library, derivative calculations (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) can be incorporated with minimal change into existing algorithms. Diffsharp supports nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations. The library is under active development by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth. DiffSharp is implemented in the F# language and can be used from C# and the other languages running on Mono or the .Net Framework, targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.
Compares members of two objects and evaluates if they are equal or none equal based on conventions (public properties with the same names and same/derived/convertible types). Provides also few tweak options