semvec/pypi-packages huggingface dataset:
lightSOM 1.5.0
Self Organizing Maps Package
Requires Python: >=3.7

lightSOM
-----
A Python Library for Self Organizing Map (SOM)

1. The library implement fast  online training. The implementation of the algorithm is carefully done through matrix calculations, such as `scipy` sparse matrix and `numexpr` for calculation of Euclidean distance.
2. PCA (or RandomPCA (default)) initialization, using `sklearn` or random initialization.
3. component plane visualization (different modes).
4. Hitmap.
5. U-Matrix visualization.
6. 1-d or 2-d SOM with only rectangular, planar grid. (works well in comparison with hexagonal shape, when I was checking in Matlab with somtoolbox).


Quality Measures
----------------

After the SOM has been trained, the map needs to be evaluated to find out if it has been optimally trained, or if further training is required. The SOM quality is usually measured with two criteria: quantization error (QE) and topographic error (TE). The QE is the average distance between each data point and its BMU, and TE represents the proportion of all data for which the first and second BMU are not adjacent with respect to the measurement of topology preservation (Kohonen, 2001).

### Dependencies:
SOMPY has the following dependencies:
- numpy
- scipy
- scikit-learn
- matplotlib
- pandas

Ecamples
--------

there are various notebooks in the examples directory
### Installation:
```Python
pip install lightSOM
```
velocity-optimization 1.3.1
Optimizes (Maximizes) the velocity profile for a vehicle respecting physical constraints and runtime-variable input parameters.
Requires Python: >=3.5

# Velocity optimization

![Velocity Optimization.](https://raw.githubusercontent.com/TUMFTM/velocity_optimization/master/vel.png)

Optimizes (Maximizes) the velocity profile for a vehicle in real-time respecting physical constraints (e.g., power,
force, combined acceleration, ...). Takes into account a variable friction potential between road and tires. Max.
power input can be space-variable (for, e.g., energy strategy purposes).

### Disclaimer

This software is provided as-is and has not been subject to a certified safety validation. Autonomous Driving is a highly complex and dangerous task. In case you plan to use this software on a vehicle, it is by all means required that you assess the overall safety of your project as a whole. By no means is this software a replacement for a valid safety-concept. See the license for more details.

### Documentation

The public documentation is available through [ReadTheDocs](https://velocity-optimization.readthedocs.io/en/latest/index.html).

The internal development documentation can be found on [GitLab](https://roborace.pages.gitlab.lrz.de/modules/velocity_optimization/index.html).

### Publication

The preprint of the accepted version of the corresponding paper is available on [arXiv](https://arxiv.org/abs/2012.13586).

The paper is available on [IEEE eXplore](https://doi.org/10.1109/TIV.2020.3047858) and will soon be in the printed
version of the IEEE Transactions on Intelligent Vehicles:

    @article{Herrmann2020,
     author = {Herrmann, Thomas and Wischnewski, Alexander and Hermansdorfer, Leonhard and Betz, Johannes and
     Lienkamp, Markus},
     year = {2021},
     title = {{Real-Time Adaptive Velocity Optimization for Autonomous Electric Cars at the Limits of Handling}},
     pages = {665--677},
     volume = {6},
     number = {4},
     issn = {2379-8904},
     journal = {{IEEE Transactions on Intelligent Vehicles}},
     doi = {10.1109/TIV.2020.3047858}
    }
dots-code-generator 0.1.0
DOTS code generator
Requires Python: >=3.8

# dots-code-generator

# Usage
Add path with config_txt.py and path to "dots" python module to PYTHONPATH.

Call dcg.py:
cd examples
mkdir -p out
../bin/dcg.py -T templates -C config_txt -o out some_types.dots -v

Generated files will be places in directory "out".


# Update DOTS grammar

DOTS grammer is defined in "dots.lark". If the grammar is updated, a new
parser has to be generated with the "lark" python module:

python -m lark.tools.standalone dots.lark > dots/dots_parser.py