liota | Little IoT Agent is an open source project offering

 by   vmware Python Version: 0.4.1 License: Non-SPDX

X-RayKey FeaturesCode SnippetsCommunity Discussions(1)VulnerabilitiesInstallSupport

kandi X-RAY | liota Summary

kandi X-RAY | liota Summary

liota is a Python library typically used in Edge Computing applications. liota has no bugs, it has no vulnerabilities, it has build file available and it has low support. However liota has a Non-SPDX License. You can install using 'pip install liota' or download it from GitHub, PyPI.

Little IoT Agent (liota) is an open source project offering some convenience for IoT solution developers in creating IoT Edge System data orchestration applications. Liota has been generalized to allow, via modules, interaction with any data-center component, over any transport, and for any IoT Edge System. It is easy-to-use and provides enterprise-quality modules for interacting with IoT Solutions.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              liota has a low active ecosystem.
              It has 337 star(s) with 127 fork(s). There are 48 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 58 open issues and 21 have been closed. On average issues are closed in 116 days. There are 10 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of liota is 0.4.1

            kandi-Quality Quality

              liota has 0 bugs and 0 code smells.

            kandi-Security Security

              liota has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              liota code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              liota has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              liota releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              liota saves you 3555 person hours of effort in developing the same functionality from scratch.
              It has 7606 lines of code, 541 functions and 124 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
            Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of liota
            Get all kandi verified functions for this library.

            liota Key Features

            No Key Features are available at this moment for liota.

            liota Examples and Code Snippets

            No Code Snippets are available at this moment for liota.

            Community Discussions

            Trending Discussions on liota

            How to predict the output in a tensorflow model?

            QUESTION

            How to predict the output in a tensorflow model?
            Asked 2017-Jun-28 at 18:11

            I am building a tensorflow model which should give output as 0 or 1 in case of some parameters or features being exceeded. I have the training data set, and I have trained the model but the given a set of data the predictions are wrong. The accuracy of the model is being given as 94% still, the predictions are wrong. Thanks in advance for help. Here is the code:

            ...

            ANSWER

            Answered 2017-Jun-28 at 13:44

            It does not seem like you split your training data to test your model on unseen data. If this is true, the 94% is only on your training data and your model has overfitted. You should test your accuracy on the 'validation set' (around 20% of your input data is usually representative enough, depending on your amount of data, of course) after each epoch and stop when the training accuracy keeps increasing and the validation accuracy decresing.

            Edit after your comment

            In case you did split the data, did you do it in a stratified way? Can you plot a distribution of your the classes in all the sets? These should be similar to each other. Take a look at StratifiedShuffleSplit from sklearn.

            Source https://stackoverflow.com/questions/44801521

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install liota

            Liota requires a Python 2.7.9+ environment already installed.

            Support

            Want to hack on Liota and add your own DCC component? Awesome! Just fork the project in order to start contributing the code.
            Find more information at:

            Reuse Trending Solutions

            build-a-realtime-voice-to-image-generator-using-generative-ai
            build-a-realtime-voice-to-image-generator-using-generative-ai
            Build a Realtime Voice-to-Image Generator using Generative AI
            image-resizing-using-opencv-in-python
            image-resizing-using-opencv-in-python
            Image Resizing using OpenCV in Python
            build-your-own-gpt4all-content-generator
            build-your-own-gpt4all-content-generator
            Build your own Custom GPT Content Generator (Open-Source ChatGPT Alternative)
            validate-an-email-address-in-javascript
            validate-an-email-address-in-javascript
            How to Validate an Email Address in JavaScript
            age-calculator-using-javascript
            age-calculator-using-javascript
            Age Calculator using JavaScript
            addressing-bias-in-ai---toolkit-for-fairness,-explainability-and-privacy
            addressing-bias-in-ai---toolkit-for-fairness,-explainability-and-privacy
            Addressing Bias in AI - Toolkit for Fairness, Explainability and Privacy
            javascript-node-js-payment-processing
            javascript-node-js-payment-processing
            15 best JavaScript Node.js Payment libraries
            build-credit-risk-predictor-using-federated-learning
            build-credit-risk-predictor-using-federated-learning
            Build Credit Risk predictor using Federated Learning
            top-10-javascript-tours-and-guides-libraries-in-2023
            top-10-javascript-tours-and-guides-libraries-in-2023
            10 Best JavaScript Tours and Guides Libraries in 2023
            disease-predictor
            disease-predictor
            Disease Predictor using Pandas & Scikit
            python-face-recognition
            python-face-recognition
            28 best Python Face Recognition libraries

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            Install
          • PyPI

            pip install liota

            • CLONE
          • HTTPS

            https://github.com/vmware/liota.git

          • CLI

            gh repo clone vmware/liota

          • sshUrl

            git@github.com:vmware/liota.git

            • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Open Weaver – Develop Applications Faster with Open Source

            kandi

            Community and Support

            Company

            Follow

            • © 2023 Open Weaver Inc.