semvec/pypi-packages huggingface dataset:
pqagent 0.0.12
A user friendly package for training quality prediction models
Requires Python: <4,>=3.11

⚠️ Note: This package is currently in active development. It is not yet feature-complete and lacks comprehensive documentation. Users should expect changes and potential instability.

# PQagent
PQagent is a Python package designed to streamline the development and training of predictive
quality models using neural networks. Built on the powerful PyTorch framework, PQagent offers a comprehensive
suite of tools for data preprocessing, model training, and evaluation, enabling users to construct robust predictive
models tailored to their specific quality assurance needs.

For more details, please study the [Documentation](https://pqagent-e03a29.pages.git-ce.rwth-aachen.de).

## 🧩 Features
*to be documented*


## 🚀 Quickstart Example
*to be documented*

## Installation
`pip install pqagent`

## ©️ License
This project is licensed under the [MIT License](https://git-ce.rwth-aachen.de/kls/pqagent/-/blob/main/LICENSE?ref_type=heads).
QuantumRandomWalks 0.0.5
This package was made to perform continous time quantum random walks on domain-specific networks
Requires Python: >=3.6

# QuantumRandomWalk package

This package was built as the final summary assignment for the University of Rhode Island Quantum Computinhg Graduate Certificate by Jose Hernandez. The original project utilized quantum random walks to calculate probability of visit per node with a start node x or probability of edge existence between all possible pair nodes.

The purpose of this package is to allow network scientists to programmatically apply continous time quantum random walks on their own networks without needing signficant quantum preparation or onboarding.m This is alos meant as a begginer-friendly introductionn to quantu circuits which is why every effort has been made to simplify parameters and inputs as much as possible. 

## Capabilities: 

__Included Capabilities:__

1. Quantum walk from a single start node evolving outward (e^{-iAt}).
2. Quantum walk from a single start node evolving inward (e^{iAt}).
3. Quantum walk from a superposition of all nodes.

__Planned Capabilities:__

1. Pre-processing for specific network formats such as Gephi-csv exports
2. Post-processing utilities for extracting summary statistics and analyzing distributions.

__Assumptions:__

**NOTE: A preprocessing module will be added before first major release to complete this preprocessing automatically for any network**

- The adjacency matrix should be square of size 2^n × 2^n.
- Nodes are indexed from 0 to 2^n - 1 and correspond to binary quantum states.
- If your graph doesn't fit this format, preprocessing will be needed.

## Content: 

__Classes:__

- `QuantumRandomWalk`: Encapsulates circuit setup, walk simulation (inward or outward), and measurement.
- `ResultsDataFrame`: Encapsulates all post-processing functions for the dataframe 

__Functions:__

- `perform_one_node_walk`: Performs a continuous-time quantum walk from a specific start node over time steps.
- `perform_superpositioned_walk`: Performs a continous-time quantum walk starting at a superposition of all nodes

## Dependencies: 

These should be preinstalled by your package menager once you pip install the package. If not here they are for reference:

- numpy
- pandas
- qiskit
- qiskit-aer
- scipy
- math
phylodata 0.51.0
This library allows you to access experiments on PhyloData using Python.
Requires Python: >=3.10

# 🧬 PhyloData Python Libary

## 📖 Usage

Check out the [documentation](https://phylodata.com/docs/python_first_steps) for more information.

## For Developers

## 🔧 Tech Stack

- **Python**: Requires Python 3.10+
- **Streamlit**: Web interface for data processing
- **UV**: Modern Python package manager and installer (no venv required)
- **Just**: Command runner used for development workflows

### 📋 Prerequisites

- Python 3.10+
- [UV](https://github.com/astral-sh/uv) for dependency management
- [Just](https://github.com/casey/just) for running commands

### 🛠️ Development Commands

Run these commands with `just`:

```bash
just test    # Run pytest tests with UV
just check   # Run ruff linter with auto-fix
just format  # Format code with ruff
```

In order to run a script using the dependencies, just preface it with `uv python`:

```bash
uv python my_script.py
```
CE-Module 0.1.0
A custom encoder using PyTorch
Requires Python: >=3.8

\# Custom\_Encoder



This is a Python package extending PyTorch’s `nn.Module`, designed to allow deep learning training models to quickly change encoders without modifying the overall model architecture. It aims to improve code reusability and flexibility by enabling seamless integration of any encoder model.



\## Features

\-  Easily change encoders in existing models without changing architecture

&nbsp;   

\-   Enhance code reusability and modularity

&nbsp;   

\-   Compatible with arbitrary encoder models for flexible use



\## Usage



For more detailed information and examples, please visit my GitHub page:



\[GitHub Repository Link](https://github.com/DEPhantom/Custom\_Encoder#custom\_encoder)