cwcf | Source code for paper Classification | Machine Learning library

by jaromiru Python Version: Current License: MIT

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

kandi X-RAY | cwcf Summary

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

This is a source code for AAAI 2019 paper Classification with Costly Features using Deep Reinforcement Learning wrote by Jaromír Janisch, Tomáš Pevný and Viliam Lisý: paper / slides / poster / code / blog. Updated version available: There is an enhanced version of the article under name Classification with Costly Features as a Sequential Decision-Making Problem (paper), which analyzes more settings (hard budget, lagrangian optimization of lambda and missing features). The code is available in the lagrange branch of this repository.

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cwcf has a low active ecosystem.

It has 33 star(s) with 17 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 221 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of cwcf is current.

Quality

cwcf has 0 bugs and 0 code smells.

Security

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

cwcf code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cwcf 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

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

cwcf 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.

Top functions reviewed by kandi - BETA

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

Train the model
Copy weights from another model to another
Predict a model
Performs a single step
Calculate perf agent performance
Perform one step
Predict the given numpy array s
Perform an action on the brain
Calculate the cost of a given action
Generate sample data
Concatenate the state matrix
Calculate the actions for each term
Run a single episode
Perform an action on the brain
Adds a value to the accumulator
Download data from CIFAR10
Print the elapsed time
Logs the q of the model
Prepare data
Reset agent state
Update the epsilon
Calculate the epsilon
Set the learning rate
Lower LR

Get all kandi verified functions for this library.

cwcf Key Features

No Key Features are available at this moment for cwcf.

cwcf Examples and Code Snippets

No Code Snippets are available at this moment for cwcf.

Community Discussions

Trending Discussions on cwcf

Building a deep reinforcement learning with a cnn q - approximation

QUESTION

Building a deep reinforcement learning with a cnn q - approximation

Asked 2020-Apr-03 at 06:38

I'm new in DRL. Starting from this code https://github.com/jaromiru/cwcf, I would like to substitute the MLP used for the q function approximation with a CNN, but I don't know how to do. Can anybody help me? Thanks

...

ANSWER

Answered 2020-Apr-03 at 06:38

Try going through this it has a detailed explanation on how to build DQN to solve the CartPole problem. You can also have a look at this which has implementations of many DRL algorithms

Then you can replace the code in agent.py present in repo with DQN agent code

Source https://stackoverflow.com/questions/60955922

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

Vulnerabilities

No vulnerabilities reported

Install cwcf

You can download it from GitHub.
You can use cwcf 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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