Search Results for "jack-graph"
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Showing 14 open source projects for "jack-graph"
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Plotly Graphing Library for MATLAB
Plotly Graphing Library for MATLAB
Create interactive charts in your web browser with MATLAB® and Plotly. -
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Algorithms Math Models
MATLAB implementations of algorithms
...The repository gathers implementations and case studies across many topics commonly used in contest solutions: optimization (linear, integer, goal and nonlinear programming), heuristic and metaheuristic methods (simulated annealing, genetic algorithms, immune algorithms), neural networks and time-series methods, interpolation and regression, graph theory, cellular automata, grey systems, fuzzy models, partial/ordinary differential equations, and multivariate analysis, among others. The codebase is organized into topic folders (e.g., HeuristicAlgorithm, IntegerProgramming, NeuralNetwork, TimeSeries) and includes dozens of worked examples and links to textbook/source materials that the author used to assemble the collection. -
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lottie vectors
Create, display and process 2D vectors in a 3D window.
Lottie Vectors is an application for Matlab that alows you to do some pretty neat things -with vectors. More exactly -displaying them in ways that hopefully will allow you to explore and better understand your vector data. The basic idea is simple, take a vector defined in one of a few different types of data formats and map it on the screen. Add another vector and you start to form a 'route'. Each route or position vector can be accompanied with a 'force' vector. This can be used to... -
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CFNet
Training a Correlation Filter end-to-end allows lightweight networks
CFNet is the official implementation of End-to-end representation learning for Correlation Filter based tracking (CVPR 2017) by Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, and Philip H. S. Torr. The framework combines correlation filters with deep convolutional neural networks to create an efficient and accurate visual object tracker. Unlike traditional correlation filter trackers that rely on hand-crafted features, CFNet learns feature representations directly from data in an end-to-end fashion. ... -
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This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
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GTK+ IOStream
GTK+iostream, Data plots, ORB, Neural Networks, WSOLA
...You can also plot like so : Plot figure; figure.plot(x, y, x.size()); Plotting utilises GtkDataBox.sf.net ORB utilises www.zeroc.com Feed forward neural networks with different activation functions. Audio playback/recording and digital signal processing (DSP) - utilising Jack www.jackaudio.org -
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Cell decomposition is one of the first applicable solution for path planning - robot path planning. The algorithm aims to find free areas (cells not occupied by obstacles) in the configuration space, and build a graph of adjacency for these cells.
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Flexible Brain Graph Visualizer
Matlab code to visualise brain graphs from connectivity matrices
This very configurable script allows you to plot a 3D (MNI space) visualisation of a brain graph, with edges represented by cylinders and vertices represented by spheres. To aid orientation, a cortical mesh can be added, as can convex hull outlines. The colouring and sizing scheme is fully configurable for both edges and vertices. You can see some example output in <http://www.ncbi.nlm.nih.gov/pubmed/22677149> <http://www.ncbi.nlm.nih.gov/pubmed/20541019> <http://www.ncbi.nlm.nih.gov/pubmed/24562717> -
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nail_systems_biology
NAIL is a toolset for network analysis in the life sciences
The NAIL (Network Analysis and Inference Library) project is a set of tools for solving problems in the life sciences using network (graph) approaches. NAIL includes methods for creating networks, analysing and comparing networks, and for visualising or presenting the results. These methods are designed as self-contained platform-independent components which can be called either from another program, or from a command line. Modelling biological systems as networks (graphs) is becoming a common approach in the life sciences. ... -
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METAlab GTG
Matlab toolbox to run a GLM on graph theoretic properties in MRI data
This Matlab toolbox runs a GLM on graph theoretic network properties in brain networks. The GLM accepts continuous & categorical between-participant predictors and categorical within-participant predictors. Significance is determined via non-parametric permutation tests. The toolbox allows testing of both fully connected and thresholded networks (based on a range of thresholds). -
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Matlab Community Detection Toolbox
CDTB is a MATLAB toolbox which performs Community Detection
We present the Community Detection Toolbox (CDTB), a MATLAB toolbox which can be used to perform community detection. The CDTB contains several functions from the following categories. 1. graph generators; 2. clustering algorithms; 2. cluster number selection functions; 4. clustering evaluation functions. Furthermore, CDTB is designed in a parametric manner so that the user can add his own functions and extensions. The CDTB can be used in at least three ways. The user can employ the functions from the MATLAB command line; or he can write his own code, incorporating the CDTB functions; or he can use the Graphical User Interface (GUI) which automates the community detection and includes some data visualization options. -
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Utility to create and use a SRS database for pyroshock. Integrate search function to find the best SRS inside the whole database.
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DCTV
DUAL CONSTRAINED TV-BASED REGULARIZATION ON GRAPHS
...They play a key role in a variety of applications such as image denoising, compressive sensing and inverse problems in general. In this work, we extend the TV dual framework that includes Chambolle's and Gilboa-Osher's projection algorithms for TV minimization. We use a flexible graph data representation that allows us to generalize the constraint on the projection variable. We show how this new formulation of the TV problem may be solved by means of fast parallel proximal algorithms. On denoising and deblurring examples, the proposed approach is shown not only to perform better than recent TV-based approaches, but also to perform well on arbitrary graphs instead of regular grids. ... -
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An adaptive neural network and evolutionary algorithms approach to the machine learning tasks, based on the modular graph grammars. Tested on the "two spirals problem" and other tasks. Implemented in Matlab and C++.
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