LSTM---Stock-prediction | long term short term memory recurrent neural network | Machine Learning library
kandi X-RAY | LSTM---Stock-prediction Summary
kandi X-RAY | LSTM---Stock-prediction Summary
LSTM---Stock-prediction is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. LSTM---Stock-prediction has no bugs, it has no vulnerabilities and it has low support. However LSTM---Stock-prediction build file is not available. You can download it from GitHub.
A long term short term memory recurrent neural network to predict forex data time series
A long term short term memory recurrent neural network to predict forex data time series
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Quality
Security
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LSTM---Stock-prediction has a low active ecosystem.
It has 301 star(s) with 147 fork(s). There are 51 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 1402 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of LSTM---Stock-prediction is current.
Quality
LSTM---Stock-prediction has 0 bugs and 0 code smells.
Security
LSTM---Stock-prediction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
LSTM---Stock-prediction code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
LSTM---Stock-prediction 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.
Reuse
LSTM---Stock-prediction releases are not available. You will need to build from source code and install.
LSTM---Stock-prediction has no build file. You will be need to create the build yourself to build the component from source.
LSTM---Stock-prediction saves you 857 person hours of effort in developing the same functionality from scratch.
It has 1962 lines of code, 109 functions and 7 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed LSTM---Stock-prediction and discovered the below as its top functions. This is intended to give you an instant insight into LSTM---Stock-prediction implemented functionality, and help decide if they suit your requirements.
- Read data from a csv file
- Preprocess data
Get all kandi verified functions for this library.
LSTM---Stock-prediction Key Features
No Key Features are available at this moment for LSTM---Stock-prediction.
LSTM---Stock-prediction Examples and Code Snippets
No Code Snippets are available at this moment for LSTM---Stock-prediction.
Community Discussions
Trending Discussions on LSTM---Stock-prediction
QUESTION
Numpy Array creation causing "ValueError: invalid literal for int() with base 10: 'n'"
Asked 2018-Feb-25 at 23:33
I'm trying to run a predictive RNN from this repo https://github.com/jgpavez/LSTM---Stock-prediction. "python lstm_forex.py"
It seems to be having trouble creating an empty Numpy array
The function giving me problems, starting with the line 'days', fourth from the bottom.
...ANSWER
Answered 2018-Feb-25 at 23:33You're trying to int() the string 'n' in your assertion. To get the same error:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install LSTM---Stock-prediction
You can download it from GitHub.
You can use LSTM---Stock-prediction 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 LSTM---Stock-prediction 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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