Showing 5 open source projects for "offline tasks management"

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  • 1
    tsfresh

    tsfresh

    Automatic extraction of relevant features from time series

    tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. Further tsfresh is compatible with pythons pandas and scikit-learn APIs, two important packages for Data Science endeavours in python....
    Downloads: 4 This Week
    Last Update:
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  • 2
    Free Resource Leveling Algorithm
    Free and easy to implement algorithm that can be used for resource leveling in Gantt projects. Uses resources assignment policies, availibility calendars, tasks\' priorities, dependencies, sizings, complexity due to human communication.
    Downloads: 0 This Week
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  • 3
    PHPXref is a developers tool, written in Perl, that will cross reference and extract inline documentation from a collection of PHP scripts. It generates simple HTML output suitable for browsing offline.
    Downloads: 0 This Week
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  • 4
    KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
    Downloads: 0 This Week
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    Native application identity and user-based security for your Azure cloud

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  • 5
    cosmos

    cosmos

    Algorithms that run our universe | Your personal library of every algo

    Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.
    Downloads: 0 This Week
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