semvec/pypi-packages huggingface dataset:
geo-optimizer 1.1.2
GEO: Genetic & Evolutionary Optimizer — A Neural Network Training framework.
Requires Python: >=3.10
Keywords: neural networks, evolution, optimizer, genetic algorithm, deep learning, pytorch, neuroevolution, geo

# GEO-OPTIMIZER
**Genetic & Evolutionary Optimizer for Neural Networks**

> A minimal, fully-customizable, mutation-based neural network optimizer inspired by evolutionary algorithms. No gradient descent. Just evolution.

---

## What is GEO-OPTIMIZER?

**GEO (Genetic and Evolutionary Optimizer)** is a lightweight PyTorch-based framework for training neural networks using **evolutionary strategies** instead of traditional backpropagation.

- **No gradients**
- **No backward()**
- Works with any activation/loss functions
- Optimizes with mutation, selection, and survival of the fittest

---

## How It Works

At each generation:
1. A **population** of mutated neural networks is generated.
2. Each network is evaluated using a **loss function**.
3. The **top 2 performers** are selected.
4. New weights are **blended and passed** to the next generation.

---

## Installation

```bash
pip install geo-optimizer
libgsp 0.0.0.dev0
High-Performance Linear/Non-Linear Filters and Window Functions Library in C++ with Python Bindings
Requires Python: >=3.8

<p align="center">
  <h3 align="center">
    <a href="https://github.com/mohammadraziei/libgsp" style="margin-right:10px; ">
      <img src="https://github.com/MohammadRaziei/libgsp/raw/master/docs/images/libgsp-logo-sq.svg" width="80px" alt="Logo">
    </a>
    <span style="transform: translateY(-30px);">libGSP</span>
  </h3>
  <h4 align="center"> a Graph Signal Processing Library </h4>
  <h5 align="center">
	Comprehensive Library for Digital Signal Processing in C++, Python
  </h4>
</p>

[![mohammadraziei - libgsp](https://img.shields.io/static/v1?label=mohammadraziei&message=libgsp&color=white&logo=github)](https://github.com/mohammadraziei/libgsp "Go to GitHub repo")
[![forks - libgsp](https://img.shields.io/github/forks/mohammadraziei/libgsp?style=social)](https://github.com/mohammadraziei/libgsp)

![Python](https://img.shields.io/badge/Python-3.8%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue)
![Cpp](https://img.shields.io/badge/C++-17-blue)


[![License](https://img.shields.io/badge/License-MIT-purple)](#license)
[![issues - libgsp](https://img.shields.io/github/issues/mohammadraziei/libgsp)](https://github.com/mohammadraziei/libgsp/issues)



## How to use the project?

```bash
cmake -B build 
cmake --build build 
ctest --test-dir build 
ctest --test-dir build --extra-verbose
```
intronator 0.2.1
A Python package for comprehensive splice site analysis and simulation
Requires Python: >=3.10
Keywords: bioinformatics, splicing, genomics, splice-sites, RNA, mutations

# Intronator

A Python package for intron analysis with splice site prediction capabilities.

## Features

- SpliceAI integration for splice site prediction
- Pangolin model support for enhanced predictions
- seqmat compatibility for sequence analysis
- Python 3.10+ support

## Installation

### Basic Installation
```bash
pip install intronator
```

### External Dependencies
The package requires SpliceAI and Pangolin models which must be installed separately:

#### SpliceAI
```bash
pip install spliceai
```

#### Pangolin
```bash
pip install git+https://github.com/tkzeng/Pangolin.git
```

#### Complete Installation (with external dependencies)
```bash
pip install intronator[external]
```

## Quick Start

```python
import intronator

# Check package status
print(intronator.hello_intronator())
print(intronator.check_seqmat_compatibility())
print(intronator.get_model_status())
```

## Requirements

- Python >= 3.10
- seqmat
- torch >= 1.10.0
- tensorflow >= 2.8.0
- SpliceAI (install separately)
- Pangolin (install separately)

## Notes

- SpliceAI and Pangolin models will be automatically loaded when the package is imported
- Model loading may take some time on first import
- GPU acceleration is automatically detected and used when available