semvec/pypi-packages huggingface dataset:
real-random 0.1.1
Entropy-based random generator using microphone audio to produce real random floats, integers, strings, and choices.
Requires Python: >=3.7
Keywords: random, entropy, microphone, audio, non-deterministic, real randomness, true random, sound-based randomness

# real-random
![Pepy Total Downlods](https://img.shields.io/pepy/dt/real-random)

**real-random** is a Python package that generates random values using real-world entropy from ambient microphone audio. It converts raw audio noise into cryptographic hashes, which are then transformed into useful random outputs such as:

- Floating-point numbers in [0, 1)
- Integers within a range
- Random selections from sequences
- Random alphanumeric strings

This provides a non-deterministic alternative to traditional pseudorandom generators.

---

## Installation

```bash
pip install real-random
```

If you plan to build from source:

```bash
git clone https://github.com/croketillo/real-random
cd real-random
pip install .
```

## Usage
```python
from real_random import (
    real_random,
    real_random_int,
    real_random_float,
    real_random_choice,
    real_random_string,
)

print(real_random())                          # → 0.72623...
print(real_random_int(1, 10))                 # → 7
print(real_random_float(0.5, 2.5))            # → 1.934...
print(real_random_choice(["red", "blue"]))    # → "blue"
print(real_random_string(8))                  # → "aG9xB2dZ"
```

## How It Works

    Captures 1–2 seconds of microphone input (ambient noise).

    Normalizes and hashes the audio using SHA-256.

    Converts part of the hash into usable randomness.

    Uses that base randomness to build higher-level utilities.

If no audio signal is detected (silence), it falls back to generated synthetic noise to ensure entropy.


## Requirements

    A working microphone or audio input device

    Python 3.7+

    sounddevice, numpy

On Linux, make sure your user has access to audio input devices (PulseAudio or PipeWire is recommended).

## License

This project is licensed under the MIT License.
imbbag 1.3
Package contains a collection of bagging ensemble algorithms for imbalanced data classification

# ImbBag

## Description

ImbBag is a specialized package that integrates a variety of bagging ensemble methods specifically designed for imbalanced data classification. This package provides a scikit-learn-based framework that simplifies the usage of these methods, making it easier for researchers and practitioners to apply them in their work, whether dealing with binary or multi-class classification problems.

## Installation

```bash
pip install imbbag
```


## Requirements

The following Python packages are required.

* scikit-learn
* imblearn >= 1.2
* PyGAD == 3.0
* ARFS>2.2
* mlxtend
* patch_sklearn
* scikit-learn-intelex

Also, use Python 3.11


## Available Bagging Ensemble Algorithms in the ImbBag Package

* UnderBagging (UnderBag)
  * Multi-class
* Exactly Balanced Bagging (EBBag)
  * Binary-class
* OverBagging (OverBag)
  * Multi-class
* SMOTE Bagging (SMOTEBag)
  * Multi-class
* Roughly Balanced Bagging  (RBBag)
  * Binary-class
* Multi-class Roughly Balanced Bagging (MRBBag)
  * Multi-class
* Bagging Ensemble Variation (BEV)
  * Binary-class
* Lazy Bagging (LazyBag)
  * Multi-class
* Multi Random Balance Bagging (MultiRandBalBag)
  * Multi-class
* Neighborhood Balanced Bagging (NBBag)
  * Binary-class
* Probability Threshold Bagging (PTBag)
  * Multi-class
* Adaptive Synthetic Bagging (ADASYNBag)
  * Binary-class
* RSYN Bagging (RSYNBag)
  * Binary-class
* Resampling Ensemble Algorithm (REABag)
  * Multi-class
* Under-bagging K-NN (UnderBagKNN)
  * Multi-class
* Boundary Bagging (BBag)
  * Multi-class
* Bagging of Extrapolation-SMOTE SVM (BEBS)
  * Binary-class
* Evolutionary Under-sampling based Bagging (EUSBag)
  * Binary-class
* Random Balanced Sampling with Bagging (RBSBag)
  * Multi-class
* Cost-sensitive Bagging (CostBag)
  * Multi-class
  
  
  ## Credits

- ** Yousef Abdi 
- *University of Tabriz*
  
  
  ## License

This project licensed under the MIT License.


  ## Support

Report issues, ask questions, and provide suggestions using:

* [GitHub Issues](https://github.com/yousefabdi/ImbBag/issues)
* [GitHub Discussions](https://github.com/yousefabdi/ImbBag/discussions)
* Email: y.abdi [at] tabrizu [dot] ac [dot] ir

The project can be accessed at https://github.com/yousefabdi/imbbag