liota | Little IoT Agent is an open source project offering

by vmware-archive Python Version: v0.4.1 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

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 download it from GitHub.

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

Support

liota has a low active ecosystem.

It has 336 star(s) with 127 fork(s). There are 47 watchers for this library.

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 v0.4.1

Quality

liota has 0 bugs and 0 code smells.

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.

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.

Reuse

liota releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

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 has reviewed liota and discovered the below as its top functions. This is intended to give you an instant insight into liota implemented functionality, and help decide if they suit your requirements.

Runs the plugin
Create a pint model
Calculate acceleration
Set static variables
Format metric data
Parse a unit string
Returns the hierarchy of the registered entity
This method is called when a message is received
Start collecting metrics
Format the values in Graphite DCC
Publish a metric
Stop the worker thread
Creates a relationship
Called when a connection is received
Creates the TLS context
Establish a socket connection
Called when the client is disconnected
Return the number of disk usage stats
Receive messages from the queue
Registers the given entity
Start the thread
Get list of data files
Start the worker thread
Gets the power of the cyclist
Create a relationship between the given entity
Cleanup the registry

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

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