Open Source Algorithms - Page 22
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This chess program changes its strength to give the best match against you. Eventually it learns to beat you specifically through learning alogirthms. Features included transposition tables and a elementary 3-piece endgame tablebase.
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DecisionTree.jl
Julia implementation of Decision Tree (CART) Random Forest algorithm
Julia implementation of Decision Tree (CART) and Random Forest algorithms. -
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DeepSpec
A full-stack codebase for training and evaluating speculative decoding
DeepSpec is a full-stack codebase for training and evaluating draft models used in speculative decoding. It provides the components needed to prepare data, train draft models, and measure acceptance behavior against target models. The workflow starts with data preparation, including prompt download, target answer regeneration, and target cache construction. It then trains a draft model using configuration files for different algorithms and target model setups. The evaluation pipeline measures speculative decoding performance across benchmark tasks such as math, coding, instruction-following, and chat-style datasets. Overall, it is useful for researchers and engineers studying faster language model inference through speculative decoding methods. -
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Delaunator
Fast JavaScript library for Delaunay triangulation of 2D points
Delaunator is a fast library for Delaunay triangulation. It takes as input a set of points. The triangulation is represented as compact arrays of integers. It’s less convenient than other representations but is the reason the library is fast. After constructing a delaunay = Delaunator.from(points) object, it will have a triangles array and a halfedges array, both indexed by half-edge id. What’s a half-edge? A triangle edge may be shared with another triangle. Instead of thinking about each edge A↔︎B, we will use two half-edges A→B and B→A. Having two half-edges is the key to everything this library provides. It will also be useful to have some helper functions to go from one half-edge to the next and previous half-edges in the same triangle. We can draw all the triangle edges without constructing the triangles themselves. -
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This component is an easing animation value calculator. Easing types: BackEaseIn, backEaseOut, BackEaseInOut, BounceEaseIn, BounceEaseOut, CircEaseIn, CircEaseOut, CircEaseInOut, CubicEaseIn, CubicEaseOut, CubicEaseInOut, ElasticEaseIn, ElasticEaseOu
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Depends implements a generic dependency tracker written in C++ and comes with extensive documentation on how to implement a dependency tracker (and, thus, how this one is implemented). It is very simple to use and provides the tools to "roll your own"
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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This project is an extended implementation of Knuth's "Dancing Links" algorithm and some use cases (e.g. Sudoku).
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Diagonal
A command line toolki to solve a problem your favorite program defines
Diagonal can be used for: - getting descriptive statistics such as mean/median/mode with your program producing a sample - finding a root of an equation your program defines - calculating a fixed point of a function your program defines - detecting a cycle of a fuction your program defines as well as - decoding a VCDIFF file -
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Is a software for design algorithms by diagrams. Es un software para diseñar algoritmos de diagramas de flujo.
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Dictmatch is a high performance multi-mode string match lib which supports full matching and max forward matching. It is used to match substrings which contented in dictionary from string user provides. Its throughout is more than 20w line per sec.
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DifferenceKit
A fast and flexible O(n) difference algorithm framework
A fast and flexible O(n) difference algorithm framework for Swift collection. The algorithm is optimized based on the Paul Heckel’s algorithm. This is a diffing algorithm developed for Carbon, works stand alone. The algorithm optimized based on the Paul Heckel’s algorithm. See also his paper A technique for isolating differences between files released in 1978. It allows all kind of diffs to be calculated in linear time O(n). RxDataSources and IGListKit are also implemented based on his algorithm. The type of the element that to take diffs must be conform to the Differentiable protocol. -
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Robust Perl OO Module for Digest based Authentication leveraging DBI as a backend. This Performance oriented Hash Authentication Library is scalable from single pc to multi-database single signon www applications. Optionally uses Javascript and Cookies.
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DisChoco
DisChoco: Distributed Constraint Reasoning Solver
DisChoco is a Distributed Constraint Reasoning Solver. Several Distributed Constraint Reasoning algorithms are implemented (like ABT, AFC, Adopt, ...). Users may easily implement and test their algorithms with DisChoco. contact: wahbi[at]users[dot]sourceforge[dot]net -
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We introduce our implementation that accelerates the distance matrix computation in the x86 and Cell Broadband Engine (BE) architecture, a homogeneous and heterogeneous multi-core system, respectively.
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Distant Speech Recognition
Beamforming and Speech Recognition Toolkit
BTK contains C++ and Python libraries that implement speech processing and microphone array techniques such as speech feature extraction, speech enhancement, speaker tracking, beamforming, dereverberation and echo cancellation algorithms. The Millennium ASR provides C++ and python libraries for automatic speech recognition. The Millennium ASR implements a weighted finite state transducer (WFST) decoder, training and adaptation methods. These toolkits are meant for facilitating research and development of automatic distant speech recognition. -
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Distributed Multithread Apriori (DMTA)
A parallel implementation using MPI and OpenMP to Apriori algorithm
DMTA (Distributed Multithreaded Apriori) is a parallel implementation of Apriori algorithm, which exploits the parallelism at the level of threads and processes, seeking to perform load balancing among the cores. Was implemented in C++ language, using the parallelization libraries OpenMP and MPI. The algorithm was generated as a result of a project developed by André Camilo Bolina, under the guidance of teachers Marluce Rodrigues Pereira, Ahmed Ali Abdalla Esmin and Denilson Alves Pereira, in Department of Computer Science at Federal University of Lavras. The results of this project were published in the Revista de Sistemas de Informação da FSMA and is available in http://www.fsma.edu.br/si/edicao11/FSMA_SI_2013_1_Principal_1.html -
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Free scripts & examples- JSP, Eclipse, SQL, PHP, ASP, Win32 Batch, Javascript, Flash/Actionscript. -=-=-=-=-=-=- NOTE: This project is closed. For new development related to integrating Beyond Compare with Eclipse, see http://beyondcvs.sourceforge.net/
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DomainBed
DomainBed is a suite to test domain generalization algorithms
DomainBed is a PyTorch-based research suite created by Facebook Research for benchmarking and evaluating domain generalization algorithms. It provides a unified framework for comparing methods that aim to train models capable of performing well across unseen domains, as introduced in the paper In Search of Lost Domain Generalization. The library includes a wide range of well-known domain generalization algorithms, from classical baselines such as Empirical Risk Minimization (ERM) and Invariant Risk Minimization (IRM) to more advanced techniques like Domain Adversarial Neural Networks (DANN), Adaptive Risk Minimization (ARM), and Invariance Principle Meets Information Bottleneck (IB-ERM/IB-IRM). DomainBed also integrates multiple standard datasets—including RotatedMNIST, PACS, VLCS, Office-Home, DomainNet, and subsets from WILDS—allowing consistent experimentation across image classification tasks. -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important by Hessel et al., n-step Bellman updates, prioritized experience replay, and distributional reinforcement learning. For completeness, we also provide an implementation of DQN (Mnih et al., 2015). For additional details, please see our documentation. We provide a set of Colaboratory notebooks which demonstrate how to use Dopamine. We provide a website which displays the learning curves for all the provided agents, on all the games. -
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Sample implementation of multi-shift BiCGstab(ell) inverters, single-pass, double-pass and pseudo-double-pass as published in PoS of Lattice 2010.
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A simple rendering, image processing engine. Includes a framework width (multiplatform) event handling, plugin management, a templated based math library and some exotic features like general linear camera, gpu based (OpenGL) image process...
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We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. License: The DSS R-package is made available under the terms of the GNU General Public License, which means that the source code is freely available for use within other software, but if you alter the code and distribute it, you must make the new source code freely available as well. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY. In case you use the package in your work, we do appreciate a citation to a DSS publication. Citation: Yadav et al. (2014), Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Scientific Reports 4, 5193; DOI:10.1038/srep05193 Please visit the link below for details
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DualPipe
A bidirectional pipeline parallelism algorithm
DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap with communication. DualPipe addresses that by scheduling micro-batches from both ends of the pipeline in a bidirectional fashion—i.e. some micro-batches flow forward while others flow backward—so that computation on one partition can coincide with communication for another.
