semvec/pypi-packages huggingface dataset:
image-puzzle-game 1.0.0
A 3x3 grid image puzzle game built using the Pillow library.
Requires Python: >=3.10
Keywords: game,games,image,jupyter,notebook,pillow,play,puzzle

# image-puzzle-game
![logo](https://raw.githubusercontent.com/francisohara24/image-puzzle-game/master/data/image-puzzle-game-logo.png)

A 3 x 3 grid semi-interactive image puzzle game built entirely using the Pillow Python library.

- Designed to be executed in an Interactive Python notebook environment.

## Demo
1. Access the demo notebook [here](https://colab.research.google.com/github/francisohara24/image-puzzle-game/blob/master/src/image_puzzle_game/demo.ipynb)
2. Log into your Google Account.
3. Press the round play (run) button.
3. Enjoy the game!


## How to Play
1. Enter the number of the tile you would like to move (displayed on the tiles from 1 to 8).
2. Enter the position on the grid you would like to move the tile to.
    - The 3 x 3 grid is labelled with positions from 1 to 9 starting from the upper left corner down to the lower right.
3. Each tile can only be moved either vertically or horizontally to an adjacent empty position but not diagonally.
4. Move the tiles until they have aligned to form the correct image.
vsstg 3.0.1
VapourSynth packages from Setsugennoao
Requires Python: >=3.11

# vs-stg

### PyPi package for quickly updating and installing all my packages.

<br>

<br><br>
## How to install

Install `vsstg` with the following command:

```sh
pip install vsstg
```

<br>

## How to use

Install all my packages:
```sh
python -m vsstg
```

Install latest git version of all my packages:
```sh
python -m vsstg latest
```
dhea-dheo 0.0.2
A small example package
Requires Python: >=3.6


# Make sure you have upgraded version of pip
Windows
```
py -m pip install --upgrade pip
```

Linux/MAC OS
```
python3 -m pip install --upgrade pip
```

## Create a project with the following structure
```
packaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir tests
```

## pyproject.toml 

This file tells tools like pip and build how to create your project

```
[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"
```
build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.

build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.


# Setup.cfg setup
Using setup.cfg is a best practice, but you could have a dynamic setup file using setup.py

```
[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = src

```
# Running the build
### Make sure your build tool is up to date
Windows
```
py -m pip install --upgrade build
```
Linux/MAC OS
```
python3 -m pip install --upgrade build
```


### Create the build
```
py -m build
```

### Upload to pypi
```
python3.9 -m twine upload --repository cobapypi dist/*
```














### References
https://packaging.python.org/tutorials/packaging-projects/
NeuralX 1.0.0
a user-friendly Python package for neural networks.
Requires Python: >=3.11

# NeuralX

NeuralX is a Python package for creating neural networks with a wide range of features, including gradient checks, regularization, dropout, mini batch, and various optimization methods. This package is designed to be beginner-friendly, making it easy for users to understand neural network structures and build powerful models.

## Features

- **Gradient Checks:** Ensure the correctness of your neural network's gradients with built-in gradient check tools.
- **Regularization:** Apply L1 and L2 regularization to prevent overfitting and improve model robustness.
- **Dropout:** Implement dropout layers for more effective regularization and enhanced generalization.
- **Optimization Methods:** Choose from different optimization methods, including the popular Adam optimizer, to train your models efficiently.

## Installation

You can install NeuralX using `pip`:

```bash
pip install neuralx
```

## Usage

To get started with NeuralX, follow these steps:

1. Import the necessary modules from NeuralX.
2. Build your neural network architecture, specifying layers, activation functions, and other parameters.
3. Train your model on your dataset using the chosen optimization method.
4. Evaluate the model's performance using the provided tools, including the confusion matrix implementation.

Here's a simple example of how to use NeuralX to create a basic neural network:

```python
import neuralx as nx

# create layers description
layers = [
        (training_set.shape[0], None, None, None),
        (5, 'he', 'relu', 1),  # no_of_units, normalization_method, activation_function, dropout_keeping_probability
        (2, 'he', 'relu', 1),
        (1, 'he', 'sigmoid', 1)
    ]

# Create a neural network
model = nx.NeuralNetwork(layers)

# Train the model
model.train(
    training_set,
    training_set_labels,
    no_of_epochs=1000,
    optimization={'name': 'momentum', 'beta': 0.9},
    is_mini_batch=True,
    learning_rate=0.001
)

# Evaluate the model using the confusion matrix
cm = nx.ConfusionMatrix(model, test_set, test_set_labels)
print(cm)
print(cm.statistics())
```

## Contributing

We welcome contributions from the community. Feel free to submit bug reports, feature requests, or even pull requests. Together, we can make NeuralX even better!

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.