semvec/pypi-packages huggingface dataset:
ddcal 1.2.1
clustering packages with DDCAL algorithm
Requires Python: >=3.9

# Overview

A heuristic one dimensional clustering algorithm called DDCAL (Density Distribution Cluster Algorithm) that is based on iterative feature scaling.
The algorithm aims as first order to even distribute data into clusters by considering as well as second order to minimize the variance inside each cluster and maximizing the distances between clusters.

The algorithm is designed to be used for visualization, e.g., on choropleth maps.


# Basic Usage
```
pip install -i https://pypi.org/simple/ ddcal
```

```
from clustering.ddcal import DDCAL

# load data
frequencies = [0, 1, 1, 1, 5, 5, 5, 30, 88]

# initialize parameters
ddcal = DDCAL(n_clusters=3, feature_boundary_min=0.1, feature_boundary_max=0.49,
                  num_simulations=20, q_tolerance=0.45, q_tolerance_increase_step=0.5)

# execute DDCAL algorithm
ddcal.fit(frequencies)

# print/use results
print(ddcal.sorted_data)
print(ddcal.labels_sorted_data)
```

# Supplemental Material

Supplemental material for the paper **DDCAL: Evenly Distributing Data into High Density Clusters based on Iterative Feature Scaling** can be found in the folder:

```
supplemental
```

# Synthetic Data Sets

The synthetic data sets, which were used in the paper **DDCAL: Evenly Distributing Data into High Density Clusters based on Iterative Feature Scaling** which includes a description on each data set, can be found in the folder:

```
tests/data
```
# Acknowledgements
"ICT of the Future" program - an initiative of the Federal Ministry for Climate Protection, Environment, Energy, Mobility, Innovation and Technology (BMK)

<p align="center">
  <img src="https://github.com/luxmar/DDCAL/blob/main/images/BMK_Logo_EN.jpg?raw=true" width="350" title="hover text">
  <img src="https://github.com/luxmar/DDCAL/blob/main/images/FFG_Logo_EN_RGB_1000px.png?raw=true" width="350" alt="accessibility text">
</p>


# SPRINGER NATURE Link/DOI

https://doi.org/10.1007/s00357-022-09428-6


# Citation
```
@article{cite-key,
	author = {Lux, Marian and Rinderle-Ma, Stefanie},
	journal = {Journal of Classification},
	number = {1},
	pages = {106--144},
	title = {DDCAL: Evenly Distributing Data into Low Variance Clusters Based on Iterative Feature Scaling},
	volume = {40},
	year = {2023}}
```
easyfed_testing_p 0.1.1
Tool for automatizing the deployment of easy federated learning examples
Requires Python: >=3.12

## easyfed

- Tell about architecture compatibility right now
- You should have access via ssh to the machines


### Pros:
 - True reproducibility with nix.
 - No need to install python, torch, etc, on the others machines, just works like magic!
 - Easier implementation of federated learning scatter and gather workflow
 - You can allways migrate your code to nvidia flare if you want!

 ## Fix:

Why hardcode on prod\_00/admin@pablofraile.net on the fed\_admin.json information, the :
admin: {
    ...
    username: "admin@pablofraile.net",
    ..
}