Skip to content

vs-uulm/eta-adaptive

main
Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
1 branch 0 tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
data
 
 
.gitignore
 
 
Distribution Model.ipynb
 
 
LICENSE
 
 
README.md
 
 
data.py
 
 
generate.py
 
 
helpers.py
 
 
models.py
 
 
plotting.py
 
 
requirements.txt
 
 
Modelling of k-growing graphs and privacy for eta-AD Models Structure Distribution Model Notebook Prepared Datafiles Python Files

README.md

Modelling of k-growing graphs and privacy for eta-AD

Binder

Models

The models we fit in this example are built in the following way:

\begin{align*} M_1(n,k) &= \alpha \ln(\beta n) + \frac{\gamma}{e^{\delta k}} + \epsilon, \ M_2(n,k) &= \frac{\alpha \ln(\beta n)}{e^{\gamma k}} + \delta \ln(\eta n) + \frac{\zeta}{e^{\gamma k}} + \epsilon, \ M_3(n,k) &= \frac{\alpha \ln(\beta n)}{e^{\gamma k}} + \epsilon, \ M_4(n,k) &= \frac{\alpha \ln(\beta n)}{e^{\gamma k}} - \frac{\alpha \delta k}{e^{\gamma k}} + \frac{\zeta}{e^{\eta k}} + \frac{\theta \ln(\iota n)}{(\kappa n)^{\lambda n}} +\nu \ln(\xi n). \end{align*}

Structure

Distribution Model Notebook

Followalong with the Distribution Model section of the paper. (You can run the interactive versin on Binder here: https://mybinder.org/v2/gh/vs-uulm/eta-adaptive/main?filepath=Distribution%20Model.ipynb )

Prepared Datafiles

There are three files provided in 'data/': 'normal_approx_' 1 through 3. These files are pickle files of normal distribution fits for various experimental results. Number 1 was used to fit the models, while 2 and 3 are used for validation.

These files are loaded via 'load_data' from 'helpers.py'. Their contents are also available convieniently via 'data.py' as prepared objects base, val1 and val2.

Python Files

  • 'models.py': implementation of the fittable and fitted models we use for calculating mu and sigma.
  • 'data.py': Provides the 3 datasets as canonical objects base, val1 and val2.
  • 'helpers.py': Provide some internal helper methods to process, load, transform etc. data.
  • 'generate.py': Generate a pickle file of normal distribution fits for random graphs. Results will be of compatible format to be loaded via 'load_data' from 'helpers.py'

About

No description, website, or topics provided.

Resources

Readme

License

MIT license
Activity

Stars

0 stars

Watchers

11 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages