LSTM_tsc | An LSTM for time-series classification | Time Series Database library

by RobRomijnders Python Version: Current License: MIT

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

kandi X-RAY | LSTM_tsc Summary

LSTM_tsc is a Python library typically used in Database, Time Series Database, Neural Network applications. LSTM_tsc has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However LSTM_tsc build file is not available. You can download it from GitHub.

An LSTM for time-series classification

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Quality

Security

License

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Support

LSTM_tsc has a low active ecosystem.

It has 367 star(s) with 145 fork(s). There are 32 watchers for this library.

It had no major release in the last 6 months.

There are 13 open issues and 3 have been closed. On average issues are closed in 222 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of LSTM_tsc is current.

Quality

LSTM_tsc has 0 bugs and 0 code smells.

Security

LSTM_tsc has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

LSTM_tsc code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

LSTM_tsc 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

LSTM_tsc releases are not available. You will need to build from source code and install.

LSTM_tsc has no build file. You will be need to create the build yourself to build the component from source.

LSTM_tsc saves you 49 person hours of effort in developing the same functionality from scratch.

It has 129 lines of code, 4 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Initializes LSTM .
Load training data .
Sample from training data .

Get all kandi verified functions for this library.

LSTM_tsc Key Features

No Key Features are available at this moment for LSTM_tsc.

LSTM_tsc Examples and Code Snippets

No Code Snippets are available at this moment for LSTM_tsc.

Community Discussions

Trending Discussions on LSTM_tsc

TypeError when working with lists in Python

QUESTION

TypeError when working with lists in Python

Asked 2017-Jul-31 at 16:25

I am trying to adapt the code to my own data.

...

ANSWER

Answered 2017-Jul-31 at 16:25

From line 36, it seems that Data is not a list but is actually an array:

DATA = np.concatenate((data_train,data_test_val),axis=0)

And as you can see in numpy documentation, concatenate() returns an array not a list.

Source https://stackoverflow.com/questions/45420455

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

Vulnerabilities

No vulnerabilities reported

Install LSTM_tsc

You can download it from GitHub.
You can use LSTM_tsc 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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