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Weighted Graphs for LightGraphs.jl. Please pay attention to the fact that zero-weight edges are discarded by add_edge!. This is due to the way the graph is stored (a sparse matrix). A possible workaround is to set a very small weight instead.

https://github.com/JuliaGraphs/SimpleWeightedGraphs.jlTags | lightgraphs weighted-edges weighted-graphs julia |

Implementation | Julia |

License | MIT |

Platform |

Until an issue with one of our dependencies is resolved, LightGraphs will not work with any Julia 0.7 or 1.0 version that has been built from source on OSX or other systems with a compiler more modern than GCC7. If you use LightGraphs with Julia 0.7 or 1.0, please download a Julia binary. LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type.

julia graph lightgraphs graph-theory graph-generation graph-analytics graph-algorithmsOpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. FSTs have key applications in speech recognition and synthesis, machine translation, optical character recognition, pattern matching, string processing, machine learning, information extraction and retrieval among others.

finite-state automata transducers fst stateThis repository holds the code to a new kind of RNN model for processing sequential data. The model computes a recurrent weighted average (RWA) over every previous processing step. With this approach, the model can form direct connections anywhere along a sequence. This stands in contrast to traditional RNN architectures that only use the previous processing step. A detailed description of the RWA model has been published in a manuscript at https://arxiv.org/pdf/1703.01253.pdf. Because the RWA can be computed as a running average, it does not need to be completely recomputed with each processing step. The numerator and denominator can be saved from the previous step. Consequently, the model scales like that of other RNN models such as the LSTM model.

recurrent-neural-networks sequential-data time-series research rwa-model recurrent-weighted-average deep-memoryJulia.jl aggregates and curates decibans of knowledge resources for programming in Julia, an all-purpose programming language that addresses the needs of high-performance numerical analysis and computational science. For Base packages, check if the package you seek is listed in the built-in package manager on github, or check METADATA for registered Julia packages, then use the built-in package manager to install it after checking the requirements for respective versions. Pkg3.jl is an alpha next-generation package manager for Julia that creates a Manifest.toml file that records the exact versions of each dependency and their transitive dependencies.

julia julialang awesome-listJGraphT is a Java graph library that provides mathematical graph-theory objects and algorithms. It includes directed, undirected, weighted, unweighted etc. Graphs could be created based on Strings, URLs, XML documents.

chart tool graph visualization chart-library-java mathematics math graph-theoryBirdwatcher is a data analysis and OSINT framework for Twitter. Birdwatcher supports creating multiple workspaces where arbitrary Twitter users can be added and their Tweets harvested through the Twitter API for offline storage and analysis. Birdwatcher comes with several modules which can be envoked to further enrich collected data or work with it, e.g. Retrieving user's Klout score, generating social graphs between users and weighted word clouds based on their Tweets. Birdwatcher is written in Ruby and requires at least version 1.9.3 or above. To check which version of Ruby you have installed, simply run ruby --version in a terminal.

security osint twitter-api frameworkSwiftGraph is a pure Swift (no Cocoa) implementation of a graph data structure, appropriate for use on all platforms Swift supports (iOS, macOS, Linux, etc.). It includes support for weighted, unweighted, directed, and undirected graphs. It uses generics to abstract away both the type of the vertices, and the type of the weights. It includes copious in-source documentation, unit tests, as well as search functions for doing things like breadth-first search, depth-first search, and Dijkstra's algorithm. Further, it includes utility functions for topological sort, Jarnik's algorithm to find a minimum-spanning tree, detecting a DAG (directed-acyclic-graph), and enumerating all cycles.

graph data-structure graph-algorithms dijkstra-algorithm topological-sort breadth-first-search depth-first-search prims-algorithmdatasketch gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy. datasketch must be used with Python 2.7 or above and NumPy 1.11 or above. Scipy is optional, but with it the LSH initialization can be much faster.

bbit-minhash lsh-forest jaccard-similarity hyperloglog lsh minhash weighted-quantiles top-k search data-sketches data-summaryThis repository contains an implementation of "Importance Weighted Actor-Learner Architectures", along with a dynamic batching module. This is not an officially supported Google product. For a detailed description of the architecture please read our paper. Please cite the paper if you use the code from this repository in your work.

DISCLAIMER: This tool neither is, nor should be construed as an offer, solicitation, or recommendation to buy or sell any cryptoassets. With these exchanges, you can easily build yourself your own CryptoETF.

crypto coin cryptocoins etf cli node portfolio capitalization-weightedThis library is built around the concept of mathematical graph theory (i.e. it is not a charting library for drawing a graph of a function). In essence, a graph is a set of nodes with any number of connections in between. In graph theory, vertices (plural of vertex) are an abstract representation of these nodes, while connections are represented as edges. Edges may be either undirected ("two-way") or directed ("one-way", aka di-edges, arcs). Depending on how the edges are constructed, the whole graph can either be undirected, can be a directed graph (aka digraph) or be a mixed graph. Edges are also allowed to form loops (i.e. an edge from vertex A pointing to vertex A again). Also, multiple edges from vertex A to vertex B are supported as well (aka parallel edges), effectively forming a multigraph (aka pseudograph). And of course, any combination thereof is supported as well. While many authors try to differentiate between these core concepts, this library tries hard to not impose any artificial limitations or assumptions on your graphs.

Graphviz4Net provides WPF control that is capable of generating "nice looking" graph layouts with sub-graphs, curved edges with arrows, edges between sub-graphs and more. Nodes, edges and all other elements in the graph are fully customizable and can contain any other WPF cont...

graph graphviz layout visualization wpf-controlsPyGraphistry is a visual graph analytics library to extract, transform, and load big graphs into Graphistry's cloud-based graph explorer. It supports unusually large graphs for interactive visualization. The client's custom WebGL rendering engine renders up to 8MM nodes and edges at a time, and most older client GPUs smoothly support somewhere between 100K and 1MM elements. The serverside OpenCL analytics engine supports even bigger graphs.

Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation (i.e. the computation from parameters and data to loss) using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the restricted modeling languages of static computational graph systems like Theano, Torch, Caffe and Tensorflow. GPU operation is supported by simply using the KnetArray type instead of regular Array for parameters and data. Knet builds a dynamic computational graph by recording primitive operations during forward calculation. Only pointers to inputs and outputs are recorded for efficiency. Therefore array overwriting is not supported during forward and backward passes. This encourages a clean functional programming style. High performance is achieved using custom memory management and efficient GPU kernels. See Under the hood for more details.

News: Turing.jl is now Julia 1.0 compatible now! Be aware that some things still might fail. Turing was originally created and is now managed by Hong Ge. Current and past Turing team members include Hong Ge, Adam Scibior, Matej Balog, Zoubin Ghahramani, Kai Xu, Emma Smith, Emile Mathieu, Martin Trapp. You can see the full list of on Github: https://github.com/TuringLang/Turing.jl/graphs/contributors.

machine-learning probabilistic-programming mcmc-sampler julia-language artificial-intelligence bayesian-inferenceðŸš§ Dash.jl is a work-in-progress. Feel free to test the waters and submit issues. Built on top of Plotly.js, React and HTTP.jl, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Julia code.

react productivity finance data-science bioinformatics dashboard julia web-app modeling plotly data-visualization dash gui-framework charting no-javascript technical-computing plotly-dash no-vbaTensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within

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