semvec/pypi-packages huggingface dataset:
trc_cli 0.1.5
Technology Risk and Governance CLI to provision demo workspaces.
Requires Python: <4.0,>=3.12

# `trc`

TRC CLI

**Usage**:

```console
$ trc [OPTIONS]
```

**Options**:

* `-p, --provision`: Provisions the workspace and adds sample data.
* `--debug`: Set the logs to DEBUG
* `--install-completion`: Install completion for the current shell.
* `--show-completion`: Show completion for the current shell, to copy it or customize the installation.
* `--help`: Show this message and exit.
alzheimer_classification 0.2.0
Detecting the stage of Alzheimer's based on MRI images.
Requires Python: <4.0,>=3.9

# Stage of Alzheimer's classification.

The purpose of this project is to detect the stage of Alzheimer's based on MRI images.

# Dataset

The models are designed to be trained on [Alzheimer's Dataset ( 4 class of Images)](https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images) from Kaggle.
It has already been downloaded and is available in the dataset/ directory.

<p align="center">
  <img src="images/train_dataset.png" alt="Train Dataset" width="45%" />
  <img src="images/test_dataset.png" alt="Test Dataset" width="45%" />
</p>

# Usage

### Requirements
* Python 3.11+
* Poetry

### Training a model

    python train.py -m vgg16 -f model_filename
        -m/--model_name: Name of the model to train (vgg16 or cnn).
        -f/--filename: Filename to save the trained model.

### Predicting with a model

    python predict.py -m vgg16 -f model_filename -i ../dataset/test/ModerateDemented/27.jpg
        -m/--model_name: Name of the model to use for prediction (vgg16 or cnn).
        -f/--filename: Filename of the trained model.
        -i/--image_path: Path to the image file to predict.

# Development
## Pre-commits
Install pre-commits
https://pre-commit.com/#installation

If you are using VS-code install the extension https://marketplace.visualstudio.com/items?itemName=MarkLarah.pre-commit-vscode

To make a dry-run of the pre-commits to see if your code passes run
```
pre-commit run --all-files
```


## Adding python packages
Dependencies are handled by `poetry` framework, to add new dependency run
```
poetry add <package_name>
```
MoSort 0.1.5
A Python library that provides various sorting algorithms including Bubble Sort, Quick Sort, Merge Sort, Insertion Sort, Selection Sort,Tim Sort, and Heap Sort. Written by Mohammad E. Eslami.
Requires Python: >=3.6

# MoSort

MoSort is a Python library that provides implementations of several different sorting algorithms: Bubble Sort, Quick Sort, Merge Sort, Insertion Sort, Selection Sort, Tim Sort, and Heap Sort.

## What is it?

MoSort is a Python package that provides fast, flexible, and efficient implementations of various sorting algorithms. It aims to be a comprehensive tool for sorting arrays in different ways, making it easy to use and integrate into your projects.

## Main Features

Here are all the sorting algorithms that MoSort provides:

**Bubble Sort:** A simple comparison-based sorting algorithm.  
**Quick Sort:** An efficient, comparison-based, divide-and-conquer sorting algorithm.  
**Merge Sort:** A stable, comparison-based, divide-and-conquer sorting algorithm.  
**Insertion Sort:** A simple, comparison-based, stable sorting algorithm.  
**Selection Sort:** A simple, in-place comparison-based sorting algorithm.  
**Heap Sort:** A comparison-based sorting algorithm based on binary heap data structure.  
**Tim Sort:** Python's default sort uses Tim Sort, which is a combination of both merge sort and insertion sort.

## Installation

You can install MoSort using pip:

```bash
pip install MoSort
```

## Usage

Once installed, you can import and use each sorting algorithm provided by MoSort. Here's an example of how to use Quick Sort:

```bash
from MoSort import ms

array = [64, 25, 12, 22, 11]
sorted_array = ms.quick_sort(array)    #Replace quick_sort with any other sorting algorithm from MoSort library to use it accordingly.
print("Sorted array using Quick Sort:", sorted_array)
```


## Contributing
Contributions, bug reports, and feature requests are welcome! 

## License

This project is licensed under the MIT License.