semvec/pypi-packages huggingface dataset:
code-merger 1.0.2
A tool for merging code files into a single markdown document.
Requires Python: >=3.6

# code-merger

![Code Merger](https://img.shields.io/badge/language-python-blue) ![version](https://img.shields.io/badge/version-1.0-brightgreen) ![license](https://img.shields.io/badge/license-MIT-blue)

## 🚀 Overview

**code-merger** is a powerful and flexible command-line tool designed to merge files of specified extensions into a single Markdown document. Perfect for pasting your project as a reference to an LLM with a long context (like GPT-4o or Google Gemini) or for generating comprehensive project documentation or combining multiple code files into one easily navigable format.

## 📜 Features

- Support for a wide range of file extensions.
- Automatic generation of project folder structure.
- Ability to skip specified folders.
- File contents are seamlessly merged and formatted in Markdown.
- Output can be saved to a file or copied directly to the clipboard.

## 🛠 Installation

### Using pip

You can easily install code-merger from PyPI (Python Package Index):

```sh
pip install code-merger
```

### From Source

Clone this repository, install via pip using the flag -e to customize it.

```sh
git clone https://github.com/your-username/code-merger.git
cd code-merger
pip install -e .
```

## 🚀 Usage

code-merger is simple to use right from your command line.

### Command-Line Interface

```sh
merge [-e EXTENSIONS] [-f FILENAME] [-s SKIP_FOLDERS]
```

### Options

- `-e`, `--extensions`: List of file extensions to merge. Defaults to a comprehensive list of common coding languages. If not specified, scans for all supported extensions.
- `-f`, `--filename`: Name of the output file. If omitted, the merged content will be copied to the clipboard.
- `-s`, `--skip-folders`: List of folder names to skip during the merge process.

## 📌 Examples

### Merge Specific Extensions

```sh
merge -e py js html
```

### Save Merged Content to File

```sh
merge -e py js html -f merged_documentation.md
```

### Skip Specific Folders

```sh
merge -e py js html -s node_modules .git
```

### Use Default Extensions and Copy to Clipboard

```sh
merge
```

## Supported Extensions

code-merger supports an extensive list of file extensions including but not limited to:

- Python (`.py`)
- JavaScript (`.js`, `.jsx`, `.tsx`, `.ts`)
- HTML (`.html`)
- CSS (`.css`, `.scss`)
- YAML (`.yaml`, `.yml`)
- JSON (`.json`)
- Markdown (`.md`)
- C (`.c`)
- C++ (`.cpp`)
- Java (`.java`)
- Ruby (`.rb`)
- Go (`.go`)
- Rust (`.rs`)
- Shell scripts (`.sh`)
- Perl (`.pl`)
- PHP (`.php`)
- Swift (`.swift`)
- Kotlin (`.kotlin`)
- R (`.r`)
- Objective C (`.m`, `.h`)

## 🛡️ License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## 👏 Contributing

We welcome contributions! If you have any ideas, suggestions, or improvements, feel free to open an issue or submit a pull request.

## 📧 Contact

For any questions, feel free to reach out.
lmnr-baml 0.40.1
LMNR BAML for Python

# LMNR BAML

This is a hard fork of BoundaryML's [BAML](https://github.com/BoundaryML/baml).

This is a very stripped down version that does only:
- Schema validation
- LLM prompting (for structured output)
- LLM output parsing (validation against schema)

The only required types from BAML AST for this are:
- Enum
- Class

and their dependencies.

Laminar's use cases are limited to that, so the rest of the library was removed.

However, we need to interface those functions with Python, so this library exposes
a few more interfaces through Pyo3.
WeightedLDA 0.0.1
Linear discriminant analysis with weights associated with each observation
Requires Python: >=3.7

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

# WeightedLDA

This code provides a `scikit-learn` class that extends the `sklearn.discriminant_analysis.LinearDiscriminantAnalysis` analysis to allow for weighting of each sample.  For general use of the analysis, please refer to the `scikit-learn` webpage:

[https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html](https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html)

`WeightedLDA` provides the same basic utilities of the `scikit-learn` class except for the following limitations:
- Only `solve="svd"` is supported.
- The priors cannot be defined a priori.
- The `feature_names_in_` attribute is not included.

The following code shows standard usage given sample data `X`, cluster identification `y`, and weights `wgt`:

```python
from WeightedLDA import WeightedLDA

lda = WeightedLDA()

lda.fit(X, y, wgts=wgts)
```

Calling `fit` without the `wgts` variable produces a fit the same as the `scikit-learn` class.

I tried to replicate the results numerically with the `scikit-learn` class, and was able to do so with all examples except when the sample data has a nullspace.  Even then, the resulting `predict` method produces consistent results and the `transform` method is numerically consistent when the data does not extend into the nullspace of the sampled data.

### TODO

Write the `fit` method using `PyTorch`.
durakgame 1.0.4
A simple Durak card game simulator
Requires Python: >=3.8

# Durak

A simple implementation of the Russian's [Durak](https://en.wikipedia.org/wiki/Durak) card game.

For example usage see [this](https://github.com/Artem-Goldenberg/Durak/blob/main/tests/Usage.ipynb)