Search Results for "state machine editor"
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Showing 16 open source projects for "state machine editor"
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CasADi
CasADi is a symbolic framework for numeric optimization
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT, etc. It can be used in C++, Python, or Matlab/Octave. CasADi's backbone is a symbolic framework implementing forward and reverse modes of AD on expression graphs to construct gradients, large-and-sparse Jacobians, and Hessians. These expression graphs, encapsulated in Function objects, can be evaluated in a virtual machine or exported to stand-alone C code. ... -
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Tree
tree is a library for working with nested data structures
...It generalizes Python’s built-in map function to operate over arbitrarily nested collections — including lists, tuples, dicts, and custom container types — while preserving their structure. This makes it particularly useful in machine learning pipelines and JAX-based workflows, where complex parameter trees or hierarchical state representations are common. The library provides efficient operations such as flatten, unflatten, and map_structure, enabling users to apply functions to all leaves of a nested structure seamlessly. Backed by a high-performance C++ core, tree is optimized for large-scale, performance-critical applications. -
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Dlib is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
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soulng
Lexer and parser generator tool for C++
...The slg tool takes a .lexer file that contains the description of a lexical analyzer as input and produces C++ source code for a lexical analyzer as output. The produced lexical analyzer is a finite state machine that recognizes patterns described as regular expressions and return corresponding token identifiers to the parser. The spg tool takes .parser files that contain descriptions of parsers as input and produces C++ source code for parser classes as output. The parser generator produces a recursive descent top-down backtracking parser that use the lexical analyzer generated by slg to tokenize the input. -
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RE/flex lexical analyzer generator
The regex-centric, fast lexical analyzer generator for C++
...Supports fast scanning of UTF-8/16/32 files, strings, and streams. The reflex scanner generator generates clean C++ lexer class code that is thread-safe. Generates Graphviz files to visualize state machine DFAs. RE/flex works seamlessly with Bison. Language: C++ License: BSD-3 Documentation: https://www.genivia.com/doc/reflex/html/index.html Repository: https://github.com/Genivia/RE-flex Changelog: see SF-README.md -
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YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and... -
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Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and... -
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State Controller Library
A C++ framework that implements generalised finite state machines.
The State Controller Library is a generic platform-independent C++ framework that allows finite state machines and multi-action planning generalisations thereof to be realised. The structure and implementation of this library focuses on the application of finite state machines to real-time control loops, but can be reasonably adapted for virtually any other application, even completely unrelated to control systems. An emphasis has been placed on having very low overhead so as not to hurt... -
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instantFSM
C++ inline state machine library
instantFSM is a header-only C++ state machine library. Its goal is to make state machine declaration and use easy enough that it can be integrated into your projects painlessly. Have a look atp the wiki where you will find a tutorial, samples, and a cheatsheet to get accustomed with instantFSM. The only instantFSM header file is downloadable from the files section or you can check it out from the git repository. -
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A project that aims to create reusable components (C++ libraries, COM components, and Edit controls) for Phonetic Transliteration of Indian languages, such as Telugu, Tamil, Kannada etc.
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HSM [Now Moved to GitHub]
C++ Hierarchical State Machine framework library
HSM has now moved to GitHub: https://github.com/amaiorano/hsm -
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Provides fast and convinient way to create and manage hierarchical finite-state machine with orthogonal regions support.
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stupidGUI is a lite tool for u to create gui applications use c++ under win32. All of the controls are rendered by the lib,the default render engine is gdi ,u can even use direct3d replaced! Just make it Pretty,Small,and Stupid!
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SCXML which means State Chart XML defines an easy way to describe a finite state machine through an XML file. SCXML is currently a working draft by the W3C. SSCXML (Simple SCXML) is a C++ library implementing the draft.
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DBNL
Dynamic Bayesian Network Library
DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary...
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