TrafficPredict | Pytorch implementation for `` TrafficPredict : Trajectory | Machine Learning library

by huang-xx Python Version: Current License: No License

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

kandi X-RAY | TrafficPredict Summary

TrafficPredict is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. TrafficPredict has no bugs, it has no vulnerabilities and it has low support. However TrafficPredict build file is not available. You can download it from GitHub.

Pytorch implementation for the paper: TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents (AAAI), Oral, 2019. The repo has been forked initially from Anirudh Vemula's repository for his paper Social Attention: Modeling Attention in Human Crowds (ICRA 2018). If you find this code useful in your research then please also cite Anirudh Vemula's paper.

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TrafficPredict has a low active ecosystem.

It has 99 star(s) with 38 fork(s). There are 8 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 3 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TrafficPredict is current.

Quality

TrafficPredict has no bugs reported.

Security

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

License

TrafficPredict 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

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

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

Top functions reviewed by kandi - BETA

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

Train the model
Reads the graph from source_batch
Generate a sequence of nodes
Gaussian likelihood
Calculate the correlation matrix
Sample a 2D batch of 2d Gaussian
Compute the edges for the given nodes
Get the correlation matrix for the given outputs
Sample a Gaussian
Print the graph
Print node information
Print the edge information
Sample from the network
Computes the edges for the given nodes
Sample a 2D Gaussian
Preprocess the training data
Return the number of the object type
Forward computation
Compute the final output layer
Calculate final error separation between nodes and expected nodes
Calculate mean error
Get the sequence of nodes
Configure the logger
Reads the graph from a source batch

Get all kandi verified functions for this library.

TrafficPredict Key Features

No Key Features are available at this moment for TrafficPredict.

TrafficPredict Examples and Code Snippets

No Code Snippets are available at this moment for TrafficPredict.

Community Discussions

Trending Discussions on TrafficPredict

Custom filters in convolutional network with keras

QUESTION

Custom filters in convolutional network with keras

Asked 2017-Aug-09 at 12:37

I am attempting to create a convolutional network with keras in which

...

ANSWER

Answered 2017-Aug-09 at 12:37

The problem lies in the way the inputs are sliced. The LSTM Layers are expecting a Layer object as input and you are feeding a Tensor object. You could try to add a lambda layer (or two in the example) that slices the inputs in order to feed the LSTM layers. Something like:

Source https://stackoverflow.com/questions/45569938

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

Vulnerabilities

No vulnerabilities reported

Install TrafficPredict

You can download it from GitHub.
You can use TrafficPredict 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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