semvec/pypi-packages huggingface dataset:
Artifice 0.1.0
A homemade machine learning platform modeled after TensorFlow
Requires Python: >=3.7

# Artifice

***Artifice*** is a homemade machine learning platform modeled after TensorFlow, co-developed and co-written by <a href="https://williambidle.github.io/" target = "_blank">William Bidle</a> and <a href="https://ilanazane.github.io/" target = "_blank">Ilana Zane</a>. 

### Installation

To get started with ***Artifice***, copy the following command and paste it into your command line:

    pip install Artifice

To test the installation, run the following code into your Python editor of choice:

    from Artifice import Artifice
    
    layer_sequence = [1,'ReLU', 2, 'sigmoid', 3]
    loss_function = 'MSLE'

    nn = Artifice.NN(layer_sequence, loss_function)

    print('activation func library:\n', nn.activation_funcs_library, '\n')
    print('loss func library:\n', nn.loss_funcs_library, '\n')
    print('current weights:\n', nn.weights, '\n')
    print('current activation functions:\n', nn.activation_funcs, '\n')
    print('current loss function:\n', nn.loss_func_label, ':', nn.loss_func, '\n')
    print('traing error:\n', nn.training_err, '\n')

If there are no errors, then you have successfully installed ***Artifice***! The full list of functions, their usage, as well as some examples can be found within the ***Artifice.py*** file.

### List of available activation functions

For a given value, $x$, different activation functions are definined by the following.

- "sigmoid" : 

$$\frac{1}{1 + e^{-x}}$$

- 'tanh' : 

$$tanh(x)$$

- 'ReLU' : 

$$f(x) = \begin{cases}
x & \text{if } x \geq 0,\\
0  & \text{if } x < 0.
\end{cases}$$

### List of avaliable loss functions

For a given network output vector, $\vec{y}^{out}$, and true value vector, $\vec{y}^{true}$, with $N$ components each, different loss functions are definined by the following.

- Mean Squared Error ("MSE") : 

$$\sum_{i}^N(y_i^{out} - y_i^{true})^2$$

- Mean Absolute Error ("MAE") : 

$$\sum_{i}^N|y_i^{out} - y_i^{true}|$$

- "MAPE" : 
$$100 * \sum_{i}^N|\frac{y_i^{out} - y_i^{true}}{y_i^{out} + y_i^{true}}|$$

- Mean Squared Logarithmic Error ("MSLE") : 

$$\sum_{i}^N(log(y_i^{out} + 1) - log(y_i^{true} + 1))^2$$ 

- Binary Cross-Entropy ("BCE") : 

$$\sum_{i}^N(y_i^{true}*log(y_i^{out}) + (1 - y_i^{true})*log(1 - y_i^{out}))$$

- "Poisson" : 

$$\sum_{i}^N(y_i^{out} - y_i^{true} * log(y_i^{out}))$$

### Examples

Detailed examples on how to use ***Artifice*** can be found in the ***Artifice_Tutorial.ipynb*** Jupyter Notebook.
FormanceHQ 1.0.20230301
Formance Stack API
Requires Python: >=3.7
Keywords: OpenAPI,OpenAPI-Generator,Formance Stack API

    Open, modular foundation for unique payments flows  # Introduction This API is documented in **OpenAPI format**.  # Authentication Formance Stack offers one forms of authentication:   - OAuth2 OAuth2 - an open protocol to allow secure authorization in a simple and standard method from web, mobile and desktop applications. &lt;SecurityDefinitions /&gt;   # noqa: E501
theorytoolkit 1.0.2
This package refers to the topic of automata theory, which includes DFA, NDFA, Mealy machines, and Moore machines.
Keywords: python,DFA,NDFA,Mealy machines,Moore machines,Automata,Theory of Computation


## theorytoolkit

The package contains a set of tools and algorithms for theoretical computer science, which could include automata theory as well as other topics.

## Examples of How To Use (theorytoolkit)

1. Design a program for accepting decimal number divisible by 2.

```py
from theorytoolkit import DFA
result = DFA()
result.addState("A", {"0": "A", "1": "B"}, initial_state=True, final_state=True)
result.addState("B", {"0": "A", "1": "B"})
print(result.decimalNumberDivisibleByTwo("10")) # Decimal number of "10" is 2
print(result.decimalNumberDivisibleByTwo("110")) # Decimal number of "10" is 6
print(result.decimalNumberDivisibleByTwo("101")) # Decimal number of "10" is 5
```
ifdai2 0.0.3
A small example package
Requires Python: >=3.7

# Executer les tests unitaires
```bash
python -m unittest discover --pattern=*.test.py
```

## Environement d'execution des tests unitaires du package
Afin d'exécuter les tests unitaire dans les meilleur conditions vous devez vous munir d'un serveur RabbitMQ
avec les spécifications suivant :

- Nom d'utilisateur : **guest**
- Mot de passe : **guest**
- Role de l'utilisateur **guest** : **administrator**


- Adresse IP du serveur RabbitMQ : **127.0.0.1**
- Ouvrir le port **5672**

## Build package
```bash
python -m build
```