keras-wtte-rnn | Demo Weibull Time-to-event Recurrent Neural Network in Keras | Machine Learning library

by daynebatten Python Version: Current License: MIT

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

kandi X-RAY | keras-wtte-rnn Summary

keras-wtte-rnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. keras-wtte-rnn has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However keras-wtte-rnn build file is not available. You can download it from GitHub.

Demo Weibull Time-to-event Recurrent Neural Network in Keras

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keras-wtte-rnn has a low active ecosystem.

It has 206 star(s) with 73 fork(s). There are 13 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 2 have been closed. On average issues are closed in 0 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-wtte-rnn is current.

Quality

keras-wtte-rnn has 0 bugs and 0 code smells.

Security

keras-wtte-rnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-wtte-rnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-wtte-rnn is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

keras-wtte-rnn releases are not available. You will need to build from source code and install.

keras-wtte-rnn has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

keras-wtte-rnn saves you 39 person hours of effort in developing the same functionality from scratch.

It has 105 lines of code, 5 functions and 1 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Build sensor data .
Calculates the log - likelihood .
Computes the log - likelihood of a continuous log - likelihood .
Activate activation matrix .
Load a file .

Get all kandi verified functions for this library.

keras-wtte-rnn Key Features

No Key Features are available at this moment for keras-wtte-rnn.

keras-wtte-rnn Examples and Code Snippets

No Code Snippets are available at this moment for keras-wtte-rnn.

Community Discussions

Trending Discussions on keras-wtte-rnn

Reshaping tensors to train with a Weibull distribution in R Keras

QUESTION

Reshaping tensors to train with a Weibull distribution in R Keras

Asked 2019-Oct-16 at 18:42

I'm attempting to adapt the Weibull distribution first used here: https://github.com/daynebatten/keras-wtte-rnn

A similar problem with translating this into R was tackled here, but it's not getting me to my solution: https://github.com/rstudio/keras/issues/354

My situation follows the following form. Namely, it's survival data, with either a failure time or the survival time to date, but with just one row per case, which doesn't fit either of the above cases (the original, for example, is the well-known jet engine failure data, and each engine has multiple rows containing data on multiple rounds of measurements over time.)

...

ANSWER

Answered 2019-Oct-16 at 18:42

I don't understand the details of this problem, but I see a basic problem here in weibull_loglik_continuous:

Source https://stackoverflow.com/questions/58355215

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

Vulnerabilities

No vulnerabilities reported

Install keras-wtte-rnn

You can download it from GitHub.
You can use keras-wtte-rnn 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

If you've got any questions or comments about what I've posted here, you can obviously open pull requests or issues as appropriate. Also, I've posted a quick blurb about this on my blog: Recurrent Neural Networks for Churn Prediction. I'm usually pretty good about following up on blog comments, so feel free to ask away on there as well.

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