Showing 486 open source projects for "algorithms"

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  • 1

    golib

    C++ collection mostly for image processing

    libGo is a C++ class library containing all kinds of things that proved useful to me. Included are: - Linear algebra, using LAPACK and CBLAS - V4L(1) image grabber - Multithreading - Image containers (up to 3D) - Some simple optimisation code - Python embedding helper - Matlab interface - .. and other things, have a look at the HTML documentation! golib grew over many years, things I had use for have been added now and then. Some parts are better taken care of than others. If...
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  • 2
    AUVNetSim is a simulation library for testing acoustic networking algorithms, written in Python, making extensive use of the SimPy discrete event simulation package.
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  • 3
    The Meme Machine generates a visual and spoken narrative among connected nodes and the world wide web, in an expanding network using open source algorithms. The computers exhibit eerie self-deterministic behaviours.
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  • 4
    UANA (Universal Abstract Numerical Algorithm) is a c++ api for developers to write algorithms independant of the underlying data container classes.
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  • 5
    Distributions Networks Analyzer (DNA) is a tool for simulating (power flow) and analyzing electrical distribution networks. Users can easily implement and test their own power flow, optimal power flow and control algorithms.
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  • 6
    This project will design a CAD engine architecture which allows algorithms to be designed using Python and a robust GUI architecture.
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  • 7
    nBoost is a suite of boosting algorithms designed to solve binary classification problems on data that is not linearly separable by a convex combination of base hypotheses, i.e. noisy data. WARNING: Active development. Underlying algorithm is unstable.
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  • 8
    Monk Computer Vision

    Monk Computer Vision

    A low code unified framework for computer vision and deep learning

    ... - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection - https://github.com/Tessellate-Imaging/Monk_Object_Detection. Monk object detection is our take on assembling state of the art object detection, image segmentation, pose estimation algorithms at one place, making them low code and easily configurable on any machine. - Monk GUI - https://github.com/Tessellate-Imaging/Monk_Gui. ...
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  • 9

    PyOptFrame-LEGACY

    PyOptFrame-LEGACY is Python OptFrame v2. Newest version v5 on github.

    PyOptFrame-LEGACY is a Python version of OptFrame v2, proposed in 2011, now superseeded in 2021 by v5 on GitHub and PIP. The main objective is to provide the same interface to OptFrame C++ optimization framework, including classic metaheuristics such as genetic algorithms, simulated annealing, variable neighborhood search, first/best/multi-improvement, hill climbing, and multi-objective methods such as nsga-ii. See NEWEST version v5 on GitHub and PIP. Please try Official pyoptframe on https://pypi.org/project/optframe/ for OptFrame v5 (last updated 2022).
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  • 10
    litersta

    litersta

    Litersta - textual analytics - software

    ...Unlike database fields, which are easily queried, text contains unstructured data that must be parsed for key objects that can be transformed in to powerful metrics. Litersta - textual analytics - software leverages statistical algorithms to programmatically locate, and extract, overall document sentiment, word frequencies, and document similarities. The Litersta web application runs locally on your server - behind your firewall. This strategy keeps your data confidential and secure.
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  • 11
    ...These include the proportion of sequences with a gap, the level of residue similarity and, if several alignments for the same set of sequences are provided, the consistency level of columns among alignments. Moreover, trimAl allows to manually select a set of columns and sequences to be removed from the alignment. trimAl implements a series of automated algorithms that trim the alignment searching for optimum thresholds based on inherent characteristics of the input alignment, to be used so that the signal-to-noise ratio after alignment trimming phase is increased. Among trimAl’s additional features, trimAl allows getting the complementary alignment (columns that were trimmed), to compute statistics from the alignment, to select the output file format , to get a summary of trimAl’s trimming in HTML and SVG formats, and many other options.
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