semvec/pypi-packages huggingface dataset:
kaliyuga 1.0.0
Kaliyuga: The Hindu-inspired hacking marvel. Unleash its potent arsenal for advanced cybersecurity. Exploit vulnerabilities, crack passwords, and analyze networks with divine precision. Harness its forensic prowess and generate impactful reports. Empowering defenders in the digital age.
Requires Python: >=3.6
Keywords: mrfidal, kaliyuga, cybersecurity, hacking, network analysis, password cracking, forensics, exploitation, security, python

# Kali Yuga
number-of-terms-inside-a-square-root2 0.0.5
Application to Determine the Number of Terms Inside a Square Root
Requires Python: >=3.8

# suuzikadai
# Application to Determine the Number of Terms Inside a Square Root
任意の数字を入力することでその数字のルート内の項を求めることができる。
"You can determine the terms inside the square root of any given number by inputting the number."
number-of-terms-inside-a-square-root1 0.0.5
Application to Determine the Number of Terms Inside a Square Root
Requires Python: >=3.8

# suuzikadai
# Application to Determine the Number of Terms Inside a Square Root
任意の数字を入力することでその数字のルート内の項を求めることができる。
"You can determine the terms inside the square root of any given number by inputting the number."
evalRag 0.1.1
A library for evaluating Retrieval-Augmented Generation (RAG) systems
Requires Python: >=3.10

# RAG Evaluator

## Overview

RAG Evaluator is a Python library for evaluating Retrieval-Augmented Generation (RAG) systems. It provides various metrics to evaluate the quality of generated text against reference text.

## Installation

You can install the library using pip:

```bash
pip install evalRag
```

## Usage

Here's how to use the RAG Evaluator library:

```python
from evalRag import RAGEvaluation

# Initialize the evaluator
evaluator = RAGEvaluation()

# Input data
question = "What are the causes of climate change?"
response = "Climate change is caused by human activities."
reference = "Human activities such as burning fossil fuels cause climate change."

# Evaluate the response
metrics = evaluator.evaluate_all(question, response, reference)

# Print the results
print(metrics)
```

## Streamlit Web App

To run the web app:

- cd into streamlit app folder.
- Create a virtual env
- Activate
- Install all dependencies
- and run
```
streamlit run app.py
```

## Metrics

The following metrics are provided by the library:

- **BLEU**: Measures the overlap between the generated output and reference text based on n-grams.
- **ROUGE-1**: Measures the overlap of unigrams between the generated output and reference text.
- **BERT Score**: Evaluates the semantic similarity between the generated output and reference text using BERT embeddings.
- **Perplexity**: Measures how well a language model predicts the text.
- **Diversity**: Measures the uniqueness of bigrams in the generated output.
- **Racial Bias**: Detects the presence of biased language in the generated output.

## Testing

To run the tests, use the following command:

```
python -m unittest discover -s evalRag -p "test_*.py"
```
## License

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

## Contributing

Contributions are welcome! If you have any improvements, suggestions, or bug fixes, feel free to create a pull request (PR) or open an issue on GitHub. Please ensure your contributions adhere to the project's coding standards and include appropriate tests.

### How to Contribute

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes.
4. Run tests to ensure everything is working.
5. Commit your changes and push to your fork.
6. Create a pull request (PR) with a detailed description of your changes.

## Contact

If you have any questions or need further assistance, feel free to reach out via [email](mailto:siginamsettyphani@gmail.com).
number-of-terms-inside-a-square-root 0.0.5
Application to Determine the Number of Terms Inside a Square Root
Requires Python: >=3.8

# Application to Determine the Number of Terms Inside a Square Root
任意の数字を入力することでその数字のルート内の項を求めることができる。
"You can determine the terms inside the square root of any given number by inputting the number."
onepiece-classifier 0.0.3
package to predict onepiece image character
Requires Python: >=3.10

# Machine Learning Project Documentation

## Project Overview
This repository contains programs for classifying images. The images used for this project are images of Onepiece anime characters. There are 18 predicted anime characters. This project is packaged in the form of a python package so that it can be used by the public.

## Installation Guide
### Requirements
It is recommended to run this application within a virtual environment. First thing first is clone the repository

```sh
git clone https://github.com/lombokai/onepiece-classifier.git
```
### Virtual Environment Setup
create a virtual environment

```sh
python3 -m venv <virtual environment name>
```

command line to activate your virtual environment in linux
```sh
source <virtual environment name>/bin/activate
```

command line to activate your virtual environment in windows
```sh
<virtual environment name>\Scripts\activate
```

install package requirements
```sh
pip install -r requirements/main.txt
```

## Usage
### Example Usage
This package provide image to predict in `assets` directory. If you want to try predict an image, run predict.py script with command bellow
```
python3 predict.py <image path>
```

command example if you are working in parent directory of this repo

```sh
python3 predict.py assets/luffy.png
```

try to predict different image with command above

### Run the Application
install onepiece-classify package
```sh
pip install onepiece-classify
```

acces predict method from onepiece-classify pakcage

```python
from onepiece_classify.infer import ImageRecognition
```

instantiate the class with your model path. Download trained model [here](https://drive.google.com/file/d/1M1-1Hs198XDD6Xx-kSWLThv1elZBzJ0j/view?usp=sharing) and make sure you specify model path parameter in the location of downloaded model

```python
predictor = ImageRecognition(<model path>)
```

then you just predict your image with `predict` method
```python
predictor.predict(<your image path>)
```


## Data Description
### Data Sources
Data obtained from Kaggle [here](https://www.kaggle.com/datasets/ibrahimserouis99/one-piece-image-classifie). The data contains a collection of 18 onepiece character images, and is saved in jpg, jpeg and png formats.

## Contributing
When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.

## License

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
MLAlgos 1.0.0
5 ML Model are available to train bassed on provided dataset, user can select one regresion out of 5 for train.
Requires Python: >=3.9
Keywords: ML Regressions, MLRegressions Linear polynomial svr random-forest decision-tree regressors

Installation :

python 3.9 : pip install MLAlgos==1.0.0

python 3.10 : pip install MLAlgos==1.0.1

python 3.11 : pip install MLAlgos==1.0.2



Example:

from MLRegressions import Regressors

import pandas as pd

df = pd.read_csv('Sampledata.csv')

x = df.iloc[:,1:-1].values # Features

y = df.iloc[:,-1].values # Depended Variable

reg = Regressors(x,y,skip_regressor=[],poly_degree=5, test_size=0.2, random_state=0)

obj = reg.fit_models() # To train Models & return class obj [LinearRegression(), LinearRegression(),
 SVR(), DecisionTreeRegressor(random_state=0), RandomForestRegressor(n_estimators=10, random_state=0)]

Linear Regression     : obj[0].predict()

Polynomial Regression : obj[1].predict()

SVR                   : obj[2].predict()

DecisionTreeRegressor : obj[3].predict()

RandomForestRegressor : obj[4].predict()

data = reg.r2_score() # To get r2_scores data for train test set.

reg.plot_train_data() # To plot graphs for Trained set.