DARNN | Stage Attention-Based Recurrent Neural Network | Machine Learning library
kandi X-RAY | DARNN Summary
kandi X-RAY | DARNN Summary
DARNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Neural Network applications. DARNN has no bugs, it has no vulnerabilities and it has low support. However DARNN build file is not available. You can download it from GitHub.
An implementation of the paper. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. Yao Qin, Dongjin Song, Haifeng Cheng, Wei Cheng, Guofei Jiang, Garrison. W. Cottrell International Joint Conference on Artificial Intelligence (IJCAI), 2017. run in tf 1.3. Used as baseline in the paper A Memory-Network Based Solution for Multivariate Time-Series Forecasting .
An implementation of the paper. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. Yao Qin, Dongjin Song, Haifeng Cheng, Wei Cheng, Guofei Jiang, Garrison. W. Cottrell International Joint Conference on Artificial Intelligence (IJCAI), 2017. run in tf 1.3. Used as baseline in the paper A Memory-Network Based Solution for Multivariate Time-Series Forecasting .
Support
Quality
Security
License
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Support
DARNN has a low active ecosystem.
It has 66 star(s) with 12 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 4 open issues and 0 have been closed. On average issues are closed in 99 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of DARNN is current.
Quality
DARNN has 0 bugs and 0 code smells.
Security
DARNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
DARNN code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
DARNN does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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DARNN releases are not available. You will need to build from source code and install.
DARNN has no build file. You will be need to create the build yourself to build the component from source.
DARNN saves you 456 person hours of effort in developing the same functionality from scratch.
It has 1076 lines of code, 33 functions and 8 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed DARNN and discovered the below as its top functions. This is intended to give you an instant insight into DARNN implemented functionality, and help decide if they suit your requirements.
- Train the model on the given horizon
- RNN layer
- Compute Pearson correlation coefficient
- Root Mean Square Error Ratio
- Mean absolute percentage error
- Calculate Pearson correlation coefficient
- Calculate the Root Error Ratio
Get all kandi verified functions for this library.
DARNN Key Features
No Key Features are available at this moment for DARNN.
DARNN Examples and Code Snippets
No Code Snippets are available at this moment for DARNN.
Community Discussions
Trending Discussions on DARNN
QUESTION
ImportError: libkfusion.so: cannot open shared object file: No such file or directory
Asked 2019-Jun-27 at 03:48
I have been trying to reproduce a framework from a paper where it uses Kinect Fusion library.
When I am running a test script, I got the errors:
...ANSWER
Answered 2018-Jun-17 at 17:56Any environment variables set in your local environment are lost when you run sudo. For example, if my local environment includes:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DARNN
You can download it from GitHub.
You can use DARNN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use DARNN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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