semvec/pypi-packages huggingface dataset:
kNNp 0.0.1
A kNN classifier optimized by P systems
Keywords: knn,theoretical computer science,machine learning,p systems





# kNN-P Classifier (Under Development)

[![Licence](https://img.shields.io/github/license/Ileriayo/markdown-badges?style=for-the-badge)](./LICENSE)


## Overview

This repository contains an implementation of the kNN-P classifier, an enhanced version of the k-nearest neighbors algorithm utilizing membrane computing. kNN-P is designed for parallel and distributed computing, which can improve the performance of the original k-nearest neighbors algorithm for classification tasks.

**Please note that this project is currently under development.**

## Features

- Implementation of the kNN-P classifier.
- Designed for parallel and distributed computing.
- Improved performance for classification tasks.

## Installation

You can install this package using pip:

```bash
pip install knnp
```

## Usage

```python
from knnp.p_systems import kNN_P

# Create an instance of kNN-P
knn_p = kNN_P(n=100, d=2, q=10, m=5, k=3, maxstep=100)

# Load your training data (features) and class labels
training_data = ...
class_labels = ...

# Train the classifier
knn_p.fit(training_data, class_labels)

# Load your test data
test_data = ...

# Make predictions
predictions = knn_p.predict(test_data)

# Evaluate the predictions and calculate classification metrics
...
```

## Contributing

Contributions to this project are welcome. Please follow these guidelines for contributing:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes.
4. Push your branch to your fork.
5. Create a pull request with a clear description of your changes.

## License

This project is open-source and available under the [MIT License](https://opensource.org/licenses/MIT).

## Contact

If you have questions or need further assistance, please feel free to reach out to [Khushiyant](mailto:khushiyant2002@gmail.com).

---

**Please note that this project is under development. Use it with caution, and contributions are encouraged.**
gdrive-datastore 0.0.1.5
A package to facilitate storing data on Google Drive.
Requires Python: >=3.7

# gdrive-datastore

A package that facilitates storing data to Google Drive

## build

To build distribution, simply run:

> python -m build

## install locally for testing

To install localling for testing, simply run:

> python -m pip install .

## test locally

To test locally, simply run:

> pytest tests

## upload to test pypi

Before uploading, make sure you have a `.pypirc` file in your home directory that contains an API token for the test pypi site.  If this did not exist, the dev container would have created a directory of that name in its place.  You will hve to remove the created directory before creating the file.

To upload to test pypi, simply run:

> python -m twine upload --repository testpypi dist/*

## install from test pypi

To install from test pypi, simply run: 

> python -m pip install --index-url https://test.pypi.org/simple/ --no-deps gdrive-datastore

## upload to pypi

Before uploading, make sure you have a `.pypirc` file in your home directory that contains an API token for the test pypi site.  If this did not exist, the dev container would have created a directory of that name in its place.  You will hve to remove the created directory before creating the file.

To upload to test pypi, simply run:

> python -m twine upload dist/*

## install from pypi

To install from pypi, simply run: 

> python -m pip install gdrive-datastore
knnp 0.0.1
A kNN classifier optimized by P systems
Keywords: knn,theoretical computer science,machine learning,p systems





# kNN-P Classifier (Under Development)

[![Licence](https://img.shields.io/github/license/Ileriayo/markdown-badges?style=for-the-badge)](./LICENSE)


## Overview

This repository contains an implementation of the kNN-P classifier, an enhanced version of the k-nearest neighbors algorithm utilizing membrane computing. kNN-P is designed for parallel and distributed computing, which can improve the performance of the original k-nearest neighbors algorithm for classification tasks.

**Please note that this project is currently under development.**

## Features

- Implementation of the kNN-P classifier.
- Designed for parallel and distributed computing.
- Improved performance for classification tasks.

## Installation

You can install this package using pip:

```bash
pip install knnp
```

## Usage

```python
from knnp.p_systems import kNN_P

# Create an instance of kNN-P
knn_p = kNN_P(n=100, d=2, q=10, m=5, k=3, maxstep=100)

# Load your training data (features) and class labels
training_data = ...
class_labels = ...

# Train the classifier
knn_p.fit(training_data, class_labels)

# Load your test data
test_data = ...

# Make predictions
predictions = knn_p.predict(test_data)

# Evaluate the predictions and calculate classification metrics
...
```

## Contributing

Contributions to this project are welcome. Please follow these guidelines for contributing:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes.
4. Push your branch to your fork.
5. Create a pull request with a clear description of your changes.

## License

This project is open-source and available under the [MIT License](https://opensource.org/licenses/MIT).

## Contact

If you have questions or need further assistance, please feel free to reach out to [Khushiyant](mailto:khushiyant2002@gmail.com).

---

**Please note that this project is under development. Use it with caution, and contributions are encouraged.**
```
PCAonGPU 0.1
A GPU-based Incremental PCA implementation.
Requires Python: >=3.10

# PCAonGPU

A powerful implementation of Incremental Principal Components Analysis that runs on GPU, built on top of PyTorch.

## Installation

1. Clone the repository:

`git clone https://github.com/YourUsername/PCAonGPU.git`

2. Navigate to the cloned directory:

`cd PCAonGPU`

3. Install the required dependencies:

`pip install -r requirements.txt`

## Usage

```python
from gpu_pca import PCAonGPU

# Create an instance
model = PCAonGPU(n_components=5)

# Fit the model (either using `fit` or `partial_fit`)
model.fit(your_data)

# Transform the data
transformed_data = model.transform(your_data)
```

## Benchmark

SKlearn on an AMD Ryzen 9 5900X 12-Core Processor
 vs PCAonGPU on an Nvidia 4090

Data size: 5000 samples of 5000 dimensional data:
```
> python tests/benchmark_gpu_pca.py 
test_sklearn_pca took 21.78324556350708 seconds to complete its execution.
test_gpu_pca took 6.523377895355225 seconds to complete its execution.
```

Data size: 50000 samples of 5000 dimensional data.
```
> python tests/benchmark_gpu_pca.py 
test_sklearn_pca took 65.70944833755493 seconds to complete its execution.
test_gpu_pca took 11.030456304550171 seconds to complete its execution.
```