Showing 13 open source projects for "graphs"

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  • 1
    PyTorch Geometric

    PyTorch Geometric

    Geometric deep learning extension library for PyTorch

    It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
    Downloads: 0 This Week
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  • 2
    TAVIA

    TAVIA

    Open statistics and scientific graphing

    TAVIA is a free and open-source desktop application for statistical analysis, scientific graphing, reproducible workflows, and research project management. It combines editable data tables, statistical tests, regression, dose-response analysis, publication-quality graphs, reports, project history, and reproducibility tools in one workspace. Analyses and graphs remain linked to their source data so results can be refreshed when data changes. TAVIA supports Windows, Linux, and macOS, includes a command-line analysis interface, encrypted project files, plugin support, automatic update checking, and built-in documentation. ...
    Downloads: 7 This Week
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  • 3
    Digraph3

    Digraph3

    A collection of python3 modules for Algorithmic Decision Theory

    ...Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs.
    Downloads: 1 This Week
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  • 4
    Minsky

    Minsky

    System dynamics program with additional features for economics

    Minsky brings system dynamics and monetary modelling to economics. Models are defined using flowcharts on a drawing canvas (as are Matlab's Simulink, Vensim, Stella, etc). Minsky's unique feature is the "Godley Table", which uses double entry bookkeeping to generate stock-flow consistent models of financial flows. Minsky is good for demonstrating mathematics too, with the most "math-like" interface in system dynamics. Sign up to Minsky's Patreon page (for as little as $1 a month) at...
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    4.8 out of 5 stars 6 Reviews
    Downloads: 37 This Week
    Last Update:
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    VAERity

    VAERity

    Uncovering truth in data

    VAERity is a free, open source tool to graphically explore the VAERS data set. It aims to eventually expand in scope to allow fast querying of arbitrary large datasets. It utilizes vaex and pandas as required to provide a balance of speed and query flexibility.
    Downloads: 0 This Week
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  • 6
    CasADi
    A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.
    5.0 out of 5 stars 3 Reviews
    Downloads: 30 This Week
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  • 7
    SciEnPlot

    SciEnPlot

    Data Plotting and Analysis for Science and Engineering

    ... - Implemented a 3d surface plot (GLSurface) based on OpenGL (ScienPlot v1.3.2 and above) - ColorMap surface, trisurface, Pie, Polar plots, and 3D height field, 3dBar, scatter plots (under developing), and more - Column by column plotting/calculation - LaTex commands enclosed by $ symbols can be used for the labels in Graph - Accept txt(Text) and csv(Comma separated values) formatted data - Save, copy, print Graph - Use spread sheets to display data - Textboard to organize the results - Graphs in a publishable quality - Source codes based on: Python Numpy Scipy Matplotlib WxPython Visvis etc. - Special functions - Drag and drop data files - Python console is back (since v1.3.3), capable of reusing column data - Debye and Guinier models for SANS / SAX data - More apps in our Web below
    5.0 out of 5 stars 2 Reviews
    Downloads: 1 This Week
    Last Update:
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  • 8
    ILNumerics.Net
    math lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET. Sources from SVN, binaries: http://ilnumerics.net
    Downloads: 0 This Week
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  • 9
    The igraph library

    The igraph library

    Library for creating and manipulating graphs

    This is a library for creating and manipulating graphs with focus on speedy operations for large, sparse graphs.
    Downloads: 2 This Week
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  • 10
    pyGraph is tool that hopefully will help graph theory students by analizing,transforming and creating graphs Dependencies: python and pygame
    Downloads: 0 This Week
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  • 11
    Latex editor with document structure tree view and project handling. Latex output allows direct jump to warnings/errors. Projects folders gives support for figures/graphs. Editor component includes usual features like search/replace and syntax highlight.
    5.0 out of 5 stars 1 Review
    Downloads: 1 This Week
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  • 12
    Package for general implementation of graphs in Python. Provides generic data structures, graph operators, and common algorithms.
    Downloads: 0 This Week
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  • 13
    ExoPlanet

    ExoPlanet

    GUI based toolkit for running common Machine Learning algorithms.

    ...With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. ...
    Downloads: 0 This Week
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