semvec/pypi-packages huggingface dataset:
simpliml 1.4.0
Machine Learning, Artificial Intelligence, Mathematics
Requires Python: >=3.9
Keywords: Supervised Learning, Neural Networks, Reinforcement Learning, Gradient Descent, TensorFlow, Probability Distributions, PCA, Convex Optimization, Transformers, Bayesian Inference, Markov Chains, GANs, SVD, Entropy, Attention Mechanism

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**SimpliML** is a versatile machine learning library designed to be a one-stop solution for the entire data lifecycle. Whether you're preparing raw data or deploying advanced predictive models, *SimpliML* simplifies every step of the machine learning process.  

## Key Features  

- **Data Cleansing and Cleaning**  
  Simplify the preprocessing of raw data to ensure accurate and reliable model performance.  

- **Data Analysis**  
  Explore and analyze data with powerful, easy-to-use tools to uncover actionable insights.  

- **Model Execution and Prediction**  
  Train, validate, and deploy machine learning models seamlessly for accurate and efficient predictions.  

- **Forecasting and Optimization**  
  Perform precise forecasting and optimize your decision-making processes with ease.  

## Why Choose SimpliML?  

***SimpliML*** is designed for data scientists, ML engineers, and enthusiasts who need a reliable, efficient, and easy-to-use toolkit for managing the entire machine learning workflow.  

Get started today and unlock the full potential of your data with **SimpliML**!

- **Time Series**
- **Many more comming soon**

## Documentation
The official documentation is hosted on [Click Here](https://simpliml.readthedocs.io/).
bima 0.6.6
N-body simulation powered with Rust backend
Requires Python: >=3.12

N-body simulation library in Rust with Python bindings.

# Features
- [-] Integrators:
  - [x] Euler
  - [x] Runge-Kutta 4
  - [x] Bulirsch-Stoer
  - [x]Leapfrog (Symplectic)
  - [ ] Wisdom-Holman
- [ ] Adaptive time stepping.
- [ ] Collision detection and handling.
  - [ ] Solid body collisions.
  - [ ] Merging bodies.
- [-] Close encounter detection.
  - [x] Softening.
  - [x] Truncated.
  - [ ] Regularization.
- [ ] Force calculation.
  - [x] Direct summation.
  - [ ] Barnes-Hut tree (octree).
  - [ ] Fast multipole method.
- [ ] Parallelization.
  - [ ] CPU multi-threading.
  - [ ] GPU acceleration.

# Installation
```bash
pip install bima
```

# Quick Start

```python
from bima.body import Body
from bima import Config
import numpy as np
import bima
from matplotlib import pyplot as plt

v = np.sqrt(2)
arr = np.array([[1, -1, 0, 0, 0, -2/3 * v, 0], [2, 0.5, 0, 0, 0, 1/3 * v, 0]])
initial = bima.Initial.from_arr(arr)

config = Config(
    force=bima.ForceMethod.Direct,
    # integrator=bima.Integrator.Euler,
    integrator=bima.Integrator.LeapFrog,
    timestep=bima.TimestepMethod.Constant(0.1),
    # timestep=bima.TimestepMethod.Constant(1),
    close_encounter=bima.CloseEncounterMethod.Regularized,
)

sim = bima.Simulation(initial)

bodies = sim.in_memory.run(config, 100)
# bodies = sim.in_memory.run(config, 1)

# print(len(energy.t))


def plot(bodies: list[Body]):
    body0 = bodies[0]
    length = len(body0)
    sample_n = np.min([1000, length])
    skip = length//sample_n
    x0 = body0.x[::skip]
    y0 = body0.y[::skip]

    body1 = bodies[1]
    length = len(body1)
    skip = length//sample_n
    x1 = body1.x[::skip]
    y1 = body1.y[::skip]

    fig, ax = plt.subplots()
    ax.plot(x0, y0)
    ax.scatter(x0[-1], y0[-1])
    ax.plot(x1, y1)
    ax.scatter(x1[-1], y1[-1])
    ax.set_aspect("equal")
    plt.show()


# plot(bodies)

energy = bima.Energy.from_bodies(bodies)
e0 = energy.e[0]
plt.plot(energy.t, (e0-energy.e)/e0)
plt.show()
```


# License
GNU General Public License v3.0. See LICENSE for more details.

# Documentation

under construction
rs-metrics 0.6.0
Metrics for recommender systems
Requires Python: >=3.6

![PyPI](https://img.shields.io/pypi/v/rs_metrics?color=%2300ccff)

This package implements metrics common in recommendation systems.

## Installation

```bash
pip install rs_metrics
```

## Documentation

Docs are available [here](https://darel13712.github.io/rs_metrics/)
codemine 0.1.0
Add your description here
Requires Python: >=3.12

# Codemine

A Python project to embed Git repositories into a Pinecone vector database for semantic search.

## Features
- Embeds code chunks from Git repositories.
- Supports incremental updates (remove outdated chunks).
- Uses Tree-sitter for language-aware code splitting.
- Search embedded code chunks using natural language queries.

## Prerequisites

- Python >= 3.12
- [uv](https://github.com/astral-sh/uv) (recommended) or pip

## Setup

1.  **Clone the repository:**
    ```bash
    git clone <repository_url>
    cd codemine
    ```

2.  **Install dependencies:**

    Using `uv`:
    ```bash
    uv sync
    ```

    Using `pip`:
    ```bash
    pip install -e .
    ```

3.  **Configure Environment Variables:**

    Create a `.env` file in the root directory with the following variables:

    ```env
    PINECONE_API_KEY=your_pinecone_api_key
    GITHUB_TOKEN=your_github_token
    OPENAI_API_KEY=your_openai_api_key
    # Optional: Defaults to "https://openrouter.ai/api/v1"
    # OPENAI_BASE_URL=https://api.openai.com/v1
    ```

## Usage

The project installs a `codemine` CLI command.

### Embed a Repository

To embed a repository into the vector store:

```bash
codemine embed-repo \
  --repo-owner <owner> \
  --repo-name <repo> \
  [--create-index] \
  [--remove-outdated-chunks] \
  [--ignore-glob "**/tests/**"]
```

**Options:**
- `--repo-owner`: The owner of the GitHub repository (required).
- `--repo-name`: The name of the GitHub repository (required).
- `--create-index`: Create a new Pinecone index if it doesn't exist.
- `--remove-outdated-chunks`: Remove chunks that are no longer in the repository.
- `--ignore-glob`: Glob pattern to ignore files (can be used multiple times).

**Example:**
```bash
codemine embed-repo \
  --repo-owner jackharrington \
  --repo-name codebase-embedding \
  --create-index \
  --ignore-glob "**/__pycache__/**"
```

### Search Chunks

To search for code chunks:

```bash
codemine search-chunks --query "How does the embedding client work?"
```

**Options:**
- `--query`: The search query (required).