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webpack dependency scanner output a topologic graphic
Topologic is a cloud native framework for building real time processing graphs on Kubernetes.
Directed Graph
Undirected Graph
Topological sorting of Acyclic Dependency Graphs
Topological data structures for the DeepCausality project
biquad filters from the surge synthesizer
Topological tools for geometric structures - homology, persistent homology, Morse theory, and manifold analysis
A graph-based LLM white-box optimization toolbox: topology validation, Lie group orthogonalization, tensor ring compression
Cova's geometric and topological library
Flow topology graph structures for ObzenFlow
Topological sorting algorithm for directed graphs.
High-performance quantum circuit scheduler for multi-job quantum computing with OpenQASM 3
First principles, minimally dependent, geometric and topologically focused math library
D-dimensional Delaunay triangulations and convex hulls in Rust, with exact predicates, multi-level validation, and bistellar flips
Topological Data Analysis for neural networks - persistent homology and topological features
Topological sorting using Tarjan's algorithm
Topological Inventory
Common classes for topological-inventory collectors/operations
Topological Inventory Persister
Core Models and Schema for the Topological Inventory Service..
Topological Inventory Ingress API
Generic implementation of DFS for topological sorting.
An ffi wrapper for GEOS, a C++ port of the Java Topology Suite (JTS).
topology.
RubyNEAT -- Neural Evolution of Augmenting Topologies for Ruby. By way of an enhanced form of Genetic Algorithms -- the NEAT algorithm, populations of neural nets are evolved to handle pre-defined goals. RubyNEAT is the first implementation of the NEAT algorithm for Ruby, and it leverages Ruby's power to implement the NEAT algorithm in a way that would be difficult to do in other languages. The 'activation function' is largely standalone. Basically, activation is achieved by functional programming. Meaning, once your network is evolved, you can extract it as source code you can then utilize without the RubyNEAT engine. RubyNEAT can be used for nearly any Machine Learning task you can dream of, because it's also extensible and modular. See http://rubyneat.com for the details.
Build and resolve dependency graphs using topological sort, detect cycles, generate parallel execution batches, query dependencies and dependents, find shortest paths, and extract subgraphs.
Knife-topo uses a JSON file to capture a topology of nodes, which can be loaded into Chef and bootstrapped
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