dapo | Source code for the paper `` Divergence-Augmented Policy | Machine Learning library

by lns Python Version: Current License: No License

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kandi X-RAY | dapo Summary

dapo is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning applications. dapo has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This is an implementation of distributed reinforcement learning, used in several published work including [Divergence-Augmented Policy Optimization] and [Exponentially Weighted Imitation Learning for Batched Historical Data] The project depends on a custom distributed replay memory called [memoire] We remove the commit logs to protect sensitive IP and password information. Examples for how to use this project for (distributed) reinforcement learning can be found in example. For replicating the results of our paper, please refer to the scripts in tools. The main entry point is tools/gen_atari_env.py which can generate the shell script for running experiments in parallel, and plotting results with R.

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dapo has a low active ecosystem.

It has 36 star(s) with 4 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

dapo has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of dapo is current.

Quality

dapo has no bugs reported.

Security

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

License

dapo 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

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

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

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

Example demo
Log the mean value of a key
Configure OpenAI logging
Create an output format
Dump all kvars
Dumps the key - value pairs
Logs a key - value pair
Mean value for key - value
Log a message
Set log level
Log all output formats
Resets the logger
Create a new DataFrame from a flat array
Generate plots
Write a dict of key - value pairs
Calculate pd from a latent vector
Log keys and values in a dictionary
Return current directory
Compute the probability density from a latent vector

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dapo Key Features

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dapo Examples and Code Snippets

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Community Discussions

Trending Discussions on dapo

R: Variable lengths differ in Wilcoxon test

QUESTION

R: Variable lengths differ in Wilcoxon test

Asked 2020-Mar-27 at 00:44

Trying to see if there is a significant difference in DAPI between the DMSO treatment and the EPZ treatment. My dataset has three variables and each treatment group has a different number of individuals. Here's data I've made up to give a picture:

Treatment DAPI DAPO DAPU
DMSO 20 30 40
DMSO 24 26 42
DMSO 23 24 39
EPZ0.5uM 10 25 22
EPZ0.5uM 12 24 22
EPZ0.5uM 14 24 30
EPZ0.5uM 20 19 32

(apologies I don't know how to format a table on stackoverflow)

After importing the dataset I've done this: EPZDMSO<-EPZdata[which(EPZdata$Treatment=="DMSO"),] EPZ0.5uM<-EPZdata[which(EPZdata$Treatment=="EPZ0.5uM"),]

Tried to use wilcox.text: wilcox.test(EPZDMSO$DAPI~EPZ0.5uM$DAPI,data=EPZdata,mu=0,alt="two.sided",conf.int=T,conf.level=0.95,paired=FALSE,exact=T,correct=T)

But get this error message: Error in model.frame.default(formula = EPZDMSO$DAPI ~ EPZ0.5uM$DAPI, data = EPZdata) : variable lengths differ (found for 'EPZ0.5uM$DAPI')

Do I need to correct for unbalanced data?

Please help

(and please stay home and stay safe #Covid-19)

...

ANSWER

Answered 2020-Mar-27 at 00:44

You were close, just need to subset to the DAPI column.

Here's the man page, help(wilcox.test).

wilcox.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, exact = NULL, correct = TRUE, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x numeric vector of data values. Non-finite (e.g., infinite or missing) values will be omitted.

y an optional numeric vector of data values: as with x non-finite values will be omitted.

So to compare two groups with wilcox.test, x and y should be vectors of data.

Source https://stackoverflow.com/questions/60878127

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

Vulnerabilities

No vulnerabilities reported

Install dapo

You can download it from GitHub.
You can use dapo 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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