semvec/pypi-packages huggingface dataset:
provrandom 0.0.1
Prove the outcome of randomness!


provrandom - Prove the outcome of randomness!
===================================================================

`provrandom` is a Python package that provides a fair random number generator designed for applications that would be able to use fair randomness. The package allows you to generate random numbers and information in a way that can be verified by both the server and the client, ensuring fairness and transparency in the process.

Features
--------

- Generate provably fair random numbers based on server seeds, client seeds, and nonces.
- Easy-to-use interface for obtaining random numbers and related information.
- Transparent and verifiable randomness to guarantee fairness in any applications.
- Flexible customization of seeds and nonces for a wide range of use cases.

Installation
------------

You can install `provrandom` using pip:

```bash
pip install provrandom
```

**Usage:**
```python
from provrandom import Random

# Create an instance of the Random class
random_instance = Random()

# Example usage of methods:
print("Random information:", random_instance.random_information)
print("Random hash:", random_instance._hash())
print("Random number between 10 and 20:", random_instance.number(10, 20))
print("Random float between 1.0 and 5.0:", random_instance._float(1.0, 5.0))
print("Random string of length 8:", random_instance.string(8))

# Example usage of _random_container method to generate a list of random numbers
random_numbers_list = random_instance._random_container(list, 5, 0, 100)
print("Random numbers list:", random_numbers_list)

```
thyroid-model-damir-bogdan 0.1.0
Pretrained thyroid model and pipeline for classification
Requires Python: >=3.10.6


Thyroid classification package 

This package contains a sklearn pipeline that incorporates necessary preprocessing steps and a XGBoost classification model 
used to perform a classification for the Thyroid data set. 

To use the package use the following commands:

```python
# To import use:
from thyroid_model_damir_bogdan.load_pipeline import *
from thyroid_model_damir_bogdan.load_label_encoder import load_label_encoder

# To load pipeline and encoder use:
pipeline = load_pipeline()
encoder = load_label_encoder()

# To predict use:
y_pred = pipeline.predict(X) # X is a dataframe object of features
y_encoded = encoder.inverse_transform(y_pred)
```
thyroid-model-damir 0.1.2
Pretrained thyroid model and pipeline for classification
Requires Python: >=3.7.17


Thyroid classification package 

This package contains a sklearn pipeline that incorporates necessary preprocessing steps and a XGBoost classification model 
used to perform a classification for the Thyroid data set. 

To use the package use the following commands:

```python
# To import use:
from thyroid_model_damir.load_pipeline import *
from thyroid_model_damir.load_label_encoder import load_label_encoder

# To load pipeline and encoder use:
pipeline = load_pipeline()
encoder = load_label_encoder()

# To predict use:
y_pred = pipeline.predict(X) # X is a dataframe object of features
y_encoded = encoder.inverse_transform(y_pred)
```