semvec/pypi-packages huggingface dataset:
bnf-string-generator 0.1.0
A tool for generating random strings based on Backus-Naur Form (BNF) grammar.
License: MIT
Keywords: BNF, generator, grammar, random, string

# BNF String Generator

## Description

This is a tool for generating random strings from grammars defined in Backus-Naur Form (BNF). It operates by iteratively expanding non-terminal symbols into their corresponding production rules until only terminal symbols remain.

### Install from PyPI:

```bash
pip install bnf-string-generator
```

### Install from Source:

Clone the repository and install:

```bash
git clone https://github.com/yourusername/bnf-string-generator.git
cd bnf-string-generator
pip install .
```

## Usage

You can generate random strings by providing a BNF grammar. Here’s an example:

### Example:

```python
from bnf_string_generator import bnf_string_generator

bnf_grammar = """
<greeting> ::= <salutation> | <salutation> <intro> <EOL> <reply>
<reply> ::= <salutation>
<salutation> ::= "Hello" | "Hi" | "Hey" | "Greetings"
<intro> ::= ", I'm " <name>
<name> ::= "Alice" | "Bob" | "Charlie" | "Sam" | "Emma" | "John"
"""

random_string = bnf_string_generator(bnf_grammar)
print(random_string)
```

This will generate a random greeting like:

```
Hello, I'm Bob
```
iencoder 0.2
Custom encoder for handling categorical variables with special encoding techniques.
License: MIT
Requires Python: >=3.7

# i-encoding

## Authors

- [Anida Nezovic](https://github.com/anezovic1)
- [Dr Aida Brankovic](https://scholar.google.it/citations?user=Lh3kj1MAAAAJ&hl=en)
- [Dr Jin Yoon](https://scholar.google.com.au/citations?user=Ol5i7bcAAAAJ&hl=en)

## Overview

The <code>IEncoder</code> is a custom encoder designed to transform categorical variables into numerical representations using a unique encoding technique. The <code>fit_transform</code> method is a key part of its functionality, combining the fitting and transformation processes into a single step.

### Purpose

The method encodes categorical features into numerical values by mapping each category to a unique angular representation. This approach ensures a compact and continuous numerical representation of categorical variables while excluding a target column.

### How It Works

The method starts by validating the input data <code>X</code>. It checks the format, dimensionality and ensures no invalid values (like NaN or inf) are present. Using the <code>fit</code> method, it identifies the categorical features in the dataset.

Each category is mapped to a unique angle in radians using a circular mapping strategy (2Ï€ divided by the number of categories).

If the <code>target_column</code> parameter is specified, the transformed dataset excludes the target column, as it is not meant to be encoded.

The final transformed dataset is returned as a pandas DataFrame, preserving the original feature names.

## Requirements

The package depends on the following libraries:

- numpy
- pandas
- scikit-learn

## Installation

i-encoder is on PyPi and can be installed using pip:

```bash
pip install iencoder
```

## Contact

If you have any questions, suggestions or feedback, feel free to reach out:

- Email: nezovicanida@gmail.com

## License
This project is licensed under the [MIT License](LICENSE).