semvec/pypi-packages huggingface dataset:
cypherx 1.3.1
Ferramenta de criptografia e descriptografia de textos
License: MIT
Requires Python: >=3.6.3
Keywords: cypherx,package,cryptography,cryptography package

# Cypherx

**Cypherx** Ã© uma ferramenta simples e descomplicada para criptografar e descriptografar textos

## Requerimentos
python 3.6.3 ou superior
argparse
rich
### Para desenvolvimento
setuptools
twine



## InstalaÃ§Ã£o

```python
pip install cypherx
```

Ou se prefirir: 
```bash
git clone https://github.com/yuritorresf/cypherx.git
cd cypherx
python setup.py install
```

## Uso

### Argumentos disponÃ­veis via linha de comando
```bash
cypherx -h

argumentos posicionais:
  start                             Inicia o programa em modo de interface grÃ¡fica via terminal.

argumentos opcionais:
  -h, --help                        Mostrar ajuda e sair
  -c, --caesar                      Criptografar com cifra de CÃ©sar
  -a, --atbash                      Criptografar com cifra de Atbash
  -e, --encrypt                     Encriptar mensagem
  -d, --decrypt                     Descriptografar mensagem

  -m MESSAGE, --message MESSAGE     Mensagem a ser criptografada ou descriptografada
  -k KEY, --key KEY                 Chave para criptografar ou descriptografar [Requerido para: CÃ©sar]
  -v, --version                     Mostre a versÃ£o do programa e sair
```

### Importando como mÃ³dulo
```python
# Exemplo de importaÃ§Ã£o de modÃºlo
import cypherx
cifra_cesar = cypherx.Caesar("Texto a ser criptografado", chave, modo)
```

### Para utilizar a interface grÃ¡fica via terminal
```bash
cypherx start
```

### Exemplos
```bash
cypherx -c -e -m "mensagem" -k 3
cypherx -a -e -m "mensagem"
cypherx -c -d -m "mensagem" -k 3
cypherx -a -d -m "mensagem"
```

## ContribuiÃ§Ã£o
Pull requests sÃ£o bem-vindos. Para mudanÃ§as importantes, abra um problema primeiro para discutir o que vocÃª gostaria de mudar.
adasamp-pareto 1.1
Adaptive bayes-sampling for multi-criteria optimization
License: MIT

***********************************************************************
adasamp-pareto: adaptive bayes-sampling for multi-criteria optimization
***********************************************************************

.. image:: https://github.com/RaoulHeese/adasamp-pareto/actions/workflows/tests.yml/badge.svg 
    :target: https://github.com/RaoulHeese/adasamp-pareto/actions/workflows/tests.yml
    :alt: GitHub Actions
	
.. image:: https://readthedocs.org/projects/adasamp-pareto/badge/?version=latest
    :target: https://adasamp-pareto.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status	
	
.. image:: https://img.shields.io/badge/license-MIT-lightgrey
    :target: https://github.com/RaoulHeese/adasamp-pareto/blob/main/LICENSE
    :alt: MIT License	

Adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. The algorithm aims to find the Pareto-optimal solution of

.. math::

   max [ y(x) ] s.t. f(x) = feasible
   
in an iterative procedure. Here, :math:`y(x)` denotes the multi-dimensional goals and :math:`f(x)` the binary feasibility of the problem (in the sense that certain design variables :math:`x` lead to invalid goals). All technical details can be found in the paper "Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification" (`<https://arxiv.org/abs/2008.12005>`_).

**Installation**

Install via ``pip`` or clone this repository. In order to use ``pip``, type:

.. code-block:: sh

    $ pip install adasamp-pareto
	
**Usage**

The class ``AdaptiveSampler`` is used to define and solve a problem instance. Simple example:

.. code-block:: python

  from adasamp import AdaptiveSampler

  # Create instance
  sampler = AdaptiveSampler(func,       # Problem definition: function returns (goals Y, feasibility f)
                            X_limits,   # Design variable limits to search solution in
                            Y_ref,      # Reference point, has to be dominated by any goal Y
                            iterations, # Number of solver iterations
                            Y_model,    # Regression model to predict goals Y
                            f_model)    # Classification model to predict feasibility f

  # Return the sampling suggestions X, the corresponding goals Y, and the corresponding feasibilities f.
  X, Y, f = sampler.sample()
  
Demo notebooks can be found in the `examples/` directory.
  
**Documentation**

Complete documentation is available: `<https://adasamp-pareto.readthedocs.io/en/latest>`_.

📖 **Citation**

If you find this code useful in your research, please consider citing:

.. code-block::

    @misc{heesebortzCITE2020,
		  title={Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification}, 
		  author={Raoul Heese and Michael Bortz},
		  year={2020},
		  eprint={2008.12005},
		  archivePrefix={arXiv},
		  primaryClass={stat.ML}
         }