semvec/pypi-packages huggingface dataset:
dummy-problems 0.1.1
Solving simple problems using machine learning
Requires Python: <4.0,>=3.10

# dummy-problems
Solving "simple" problems using machine learning to improve my understanding (and have some fun!).

# Project structure
I am mostly following the structure from https://python-poetry.org/docs/basic-usage/#project-setup and https://github.com/Lightning-AI/deep-learning-project-template.

There are three key elements: **dataloaders**, **models**, and **evaluation**. Additionally, **visualisation** tools will help with development and results.

## Dataloaders


## Models


## Evaluation 


## Visualisation


# Installation
## Docker (recommended)
To install the required dependencies and run the code, use a Docker container:
```
cd docker
docker compose up
```

## Pip (experimental)
To install as a Python package, run:
```
pip install dummy-problems
```

Note: published following: https://www.digitalocean.com/community/tutorials/how-to-publish-python-packages-to-pypi-using-poetry-on-ubuntu-22-04

# Example 1: generating a synthetic dataset and benchmarking different classifiers.
Firstly, run `notebooks/synthetic_data_generation.ipynb` to generate a dataset of uppercase grayscale images. Parameters can be easily modified to increase/reduce the size of the images and/or the randomness of the dataset.

Secondly, ...
QuantumNatFS 0.0.1
A python package for implementing feature selection using quantum-based nature inspired algorithms

# QuantumBasedGeneticAlgorithm
Implements Quantum Based Genetic Algorithm for Feature Selection

How to use:

Step 1: Preprocess the dataset (Encoding, Imputing, Removing null values etc...)

Step 2: Split the dataset into X, y where both X and y are PandasDataFrame

Step 3: run the QGA_Algorithm 

Step 4: initialize the algorithm

              qga = QGA(X, y, n_of_pop, n_of_gen) # where X and y are dependent and independent variables. And n_of_pop and n_of_gen is numnber of population and number of generations
              
Step 5: Run generation 0

              Q = qga.run_gen_0()
              
Step 6: Run QGA

              best_individuals = qga.run_qga(Q[0]) # Q[0] because Q is a numpy array in a tuple... we only need numpy array
              
Step 7: Build a new dataset with reduced features

              qga.build_new_dataset(best_individuals)