semvec/pypi-packages huggingface dataset:
fklearn 4.0.1
Functional machine learning
Requires Python: <3.12,>=3.8

# fklearn: Functional Machine Learning

![PyPI](https://img.shields.io/pypi/v/fklearn.svg?style=flat-square)
[![Documentation Status](https://readthedocs.org/projects/fklearn/badge/?version=latest)](https://fklearn.readthedocs.io/en/latest/?badge=latest)
[![Gitter](https://badges.gitter.im/fklearn-python/community.svg)](https://gitter.im/fklearn-python/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
![Tests](https://github.com/nubank/fklearn/actions/workflows/push.yaml/badge.svg?branch=master)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

**fklearn** uses functional programming principles to make it easier to solve real problems with Machine Learning.

The name is a reference to the widely known [scikit-learn](https://scikit-learn.org/stable/) library.

**fklearn Principles**

1. Validation should reflect real-life situations.
2. Production models should match validated models.
3. Models should be production-ready with few extra steps.
4. Reproducibility and in-depth analysis of model results should be easy to achieve.


[Documentation](https://fklearn.readthedocs.io/en/latest/) |
[Getting Started](https://fklearn.readthedocs.io/en/latest/getting_started.html) |
[API Docs](https://fklearn.readthedocs.io/en/latest/api/modules.html) |
[Contributing](https://fklearn.readthedocs.io/en/latest/contributing.html) |


## Installation

To install via pip:

```
pip install fklearn
```

You can also install from the source:

```sh
git clone git@github.com:nubank/fklearn.git
cd fklearn
git checkout master
pip install -e .
```

## License

[Apache License 2.0](LICENSE)
hermano 0.1.2
LLM-powered CLI tool for daily tasks
Requires Python: >=3.8

# Hermano (LLM-powered Assistant for Operations)

[![Test](https://github.com/hermano-tools/hermano-cli/actions/workflows/test.yml/badge.svg)](https://github.com/hermano-tools/hermano-cli/actions/workflows/test.yml)
[![CLI Test](https://github.com/hermano-tools/hermano-cli/actions/workflows/cli-test.yml/badge.svg)](https://github.com/hermano-tools/hermano-cli/actions/workflows/cli-test.yml)

A powerful CLI tool that leverages large language models to automate daily tasks.

## Features

- EPUB token counter: Calculate how many tokens an EPUB file would use when sent to an LLM
- More features coming soon!

## Installation

### Using pip

```bash
pip install hermano
```

### Using Homebrew

```bash
brew tap manohar/hermano
brew install hermano
```

## Configuration

Hermano requires API keys for various LLM services. You can set these as environment variables:

```bash
export OPENAI_API_KEY="your-api-key"
export DEEPSEEK_API_KEY="your-api-key"
export GEMINI_API_KEY="your-api-key"
```

Alternatively, you can create a `.env` file in your home directory:

```
OPENAI_API_KEY=your-api-key
DEEPSEEK_API_KEY=your-api-key
GEMINI_API_KEY=your-api-key
```

## Usage

### EPUB Token Counter

```bash
hermano epub tokens path/to/your/book.epub
```

Options:
- `--model`: Specify the model to calculate tokens for (default: "gpt-4")
- `--format`: Output format (text, json) (default: "text")

## Development

### Setup with uv (recommended)

[uv](https://github.com/astral-sh/uv) is a fast Python package installer and resolver that we recommend for development:

```bash
# Install uv
pip install uv

# Clone the repository
git clone https://github.com/manohar/hermano.git
cd hermano

# Create and activate a virtual environment 
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install the package in development mode
uv pip install -e .

# Install development dependencies
uv pip install -r requirements-dev.txt
```

### Traditional Setup

```bash
git clone https://github.com/manohar/hermano.git
cd hermano
pip install -e ".[dev]"
```

### Testing

```bash
pytest
```

## License

MIT
cosave 0.9.6
A command-line tool to save and execute commands with variables.
Requires Python: >=3.6
Keywords: command-line, automation, productivity, shell, scripting


cosave is a command-line tool that allows you to save and execute frequently used commands with placeholders for variables. 
This can be extremely useful for developers and system administrators who often run complex commands and want to avoid retyping them or remembering intricate syntax.

**Key Features:**

- **Save Commands:** Store your frequently used commands with names.
- **Variables:** Define variables within your commands using `[variable_name]` syntax.
- **Execute Commands:** Run saved commands by name, providing values for the variables on the fly.
- **List Commands:** Easily list all your saved commands.
- **Update Commands:** Modify existing saved commands.
- **Delete Commands:** Remove commands you no longer need.
- **Backup and Restore:** Backup your command library to a file and restore it later.
- **Output Control:** Choose whether to display the output of executed commands.

**Example Use Cases:**

- Quickly initialize projects with custom setups.
- Automate repetitive tasks with variable parameters.
- Share command recipes with colleagues.
- Keep a personal library of useful commands.
NNSketch 0.1.0
A library to draw neural networks based on user parameters
Requires Python: >=3.6

# ndraw

`ndraw` is a Python package that allows users to visualize neural networks easily.

## Installation

Install `ndraw` using `pip`:

```bash
pip install nndraw
```

## Usage

```python
from ndraw.drawing import draw_neural_network

# Define the neural network structure
parameters = {
    "input_neurons": (20, "lightblue", True),  # 20 input nodes, colored light blue, with labels
    "hidden_layers": [
        (3, "ReLU", "lightgreen", True),  # 3 hidden nodes, ReLU activation, light green, labeled
        (4, "ReLU", False)  # 4 hidden nodes, ReLU activation, default color, no labels
    ],  
    "output_neurons": (2, "Softmax", "red", True),  # 2 output nodes, Softmax activation, red, labeled
    "directed_edges": False  # If False, edges are undirected; if True, edges are directed
}  

# Call the function with unpacked parameters
draw_neural_network(**parameters)
```

### Parameters Explained
`input_neurons` - Tuple `(num_nodes, color, labels)`, defining the number of input neurons, their color, and whether labels are displayed.
`hidden_layers` - List of tuples `(num_nodes, activation, color, labels)`, where each tuple defines a hidden layer.
`output_neurons` - Tuple `(num_nodes, activation, color, labels)`, specifying the number of output neurons, their activation function, color, and labels.
`directed_edges`  - Boolean (`True` or `False`), indicating whether edges between neurons should be directed (arrows) or undirected (lines).

### Notes
- If a color is not provided, the default color (e.g., `"grey"`) is used.
- Activation functions are for visualization purposes only (they are not applied computationally).

## License

This project is licensed under the MIT License.
NNDraw 0.1.0
A library to draw neural networks based on user parameters
Requires Python: >=3.6

# ndraw

`ndraw` is a Python package that allows users to visualize neural networks easily.

## Installation

Install `ndraw` using `pip`:

```bash
pip install nndraw
```

## Usage

```python
from ndraw.drawing import draw_neural_network

# Define the neural network structure
parameters = {
    "input_neurons": (20, "lightblue", True),  # 20 input nodes, colored light blue, with labels
    "hidden_layers": [
        (3, "ReLU", "lightgreen", True),  # 3 hidden nodes, ReLU activation, light green, labeled
        (4, "ReLU", False)  # 4 hidden nodes, ReLU activation, default color, no labels
    ],  
    "output_neurons": (2, "Softmax", "red", True),  # 2 output nodes, Softmax activation, red, labeled
    "directed_edges": False  # If False, edges are undirected; if True, edges are directed
}  

# Call the function with unpacked parameters
draw_neural_network(**parameters)
```

### Parameters Explained
`input_neurons` - Tuple `(num_nodes, color, labels)`, defining the number of input neurons, their color, and whether labels are displayed.
`hidden_layers` - List of tuples `(num_nodes, activation, color, labels)`, where each tuple defines a hidden layer.
`output_neurons` - Tuple `(num_nodes, activation, color, labels)`, specifying the number of output neurons, their activation function, color, and labels.
`directed_edges`  - Boolean (`True` or `False`), indicating whether edges between neurons should be directed (arrows) or undirected (lines).

### Notes
- If a color is not provided, the default color (e.g., `"grey"`) is used.
- Activation functions are for visualization purposes only (they are not applied computationally).

## License

This project is licensed under the MIT License.