<div align='center'>
<img height='180px' width='420px' src='https://github.com/reinanbr/adriana/blob/main/logo.jpg?raw=true'>
<p> A powerfull lib, for development in machine learning</p>
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<hr>
## Install:
```sh
$ pip install adrianna -U
```
## Examples:
### Bases
#### Neuro V1 (binary)
```py
from adrianna.neuro.base_v1 import NeuralNetwork
import numpy as np
# Exemplo de uso
if __name__ == "__main__":
# Dados de entrada e saída
X = np.array([[3, 1, 2], [1, 24, 5], [2, 42, 5], [2, 23, 3]])
y = np.array([[1], [0], [1], [0]])
# Criação e treinamento da rede neural
input_size = X.shape[1] # Número de colunas das listas
hidden_size = 4 # Número de neurônios na camada oculta
output_size = y.shape[1] # Número de saídas
neural_net = NeuralNetwork(input_size, hidden_size, output_size, learning_rate=0.1)
neural_net.train(X, y, epochs=10000)
# Fazendo previsões
predictions = neural_net.predict(X)
predictions = np.round(predictions).astype(int)
print("Predictions:")
print(predictions)
```
Results:
```sh
...
Epoch 8000, Loss: 0.2617957
Epoch 8100, Loss: 0.1830651
Epoch 8200, Loss: 0.1800935
Epoch 8300, Loss: 0.2585192
Epoch 8400, Loss: 0.3310709
Epoch 8500, Loss: 0.3215849
Epoch 8600, Loss: 0.1803035
Epoch 8700, Loss: 0.1802555
Epoch 8800, Loss: 0.1807692
Epoch 8900, Loss: 0.3312975
Epoch 9000, Loss: 0.1800601
Epoch 9100, Loss: 0.2642676
Epoch 9200, Loss: 0.1930688
Epoch 9300, Loss: 0.3279387
Epoch 9400, Loss: 0.1871483
Epoch 9500, Loss: 0.1809427
Epoch 9600, Loss: 0.2635577
Epoch 9700, Loss: 0.1855325
Epoch 9800, Loss: 0.1796770
Epoch 9900, Loss: 0.1800897
Predictions:
[[1]
[0]
[1]
[1]]
```
<img src='https://reysofts.com.br/apis/engine/libs/analisys_lib.php?lib_name=adrianna'>
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"description": " <div align='center'>\n\n <img height='180px' width='420px' src='https://github.com/reinanbr/adriana/blob/main/logo.jpg?raw=true'>\n\n\n<p> A powerfull lib, for development in machine learning</p>\n<a href='#'><img alt=\"CodeFactor Grade\" src=\"https://img.shields.io/codefactor/grade/github/reinanbr/dreams?logo=codefactor\">\n</a><img alt=\"CircleCI\" src=\"https://img.shields.io/circleci/build/github/reinanbr/dreams\">\n<img alt=\"Code Climate maintainability\" src=\"https://img.shields.io/codeclimate/maintainability-percentage/reinanbr/dreams\">\n<!-- \n<br/>\n<a href='https://pypi.org/project/dreams/'><img src='https://img.shields.io/pypi/v/dreams'></a>\n<a href='#'><img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/dreams\"></a>\n<br/>\n<img alt=\"PyPI - License\" src=\"https://img.shields.io/pypi/l/dreams?color=orange\">\n<img alt=\"GitHub Pipenv locked Python version\" src=\"https://img.shields.io/github/pipenv/locked/python-version/reinanbr/dreams\"> -->\n\n\n<!-- redes sociais -->\n<br/>\n<a href='https://instagram.com/reysofts/'><img src='https://shields.io/badge/insta-reysofts-darkviolet?logo=instagram&style=flat'></a>\n</div>\n\n<br>\n\n<a href=\"https://www.buymeacoffee.com/ReinanBr\" target=\"_blank\"><img height='30px' widht='100px' src=\"https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png\" alt=\"Buy Me A Coffee\" style=\"height: 30px !important;width: 100px !important;\" ></a>\n\n<hr>\n\n## Install:\n\n```sh\n$ pip install adrianna -U\n```\n\n## Examples:\n\n### Bases\n\n#### Neuro V1 (binary)\n\n```py\n\nfrom adrianna.neuro.base_v1 import NeuralNetwork\nimport numpy as np\n\n\n# Exemplo de uso\nif __name__ == \"__main__\":\n # Dados de entrada e sa\u00edda\n X = np.array([[3, 1, 2], [1, 24, 5], [2, 42, 5], [2, 23, 3]])\n y = np.array([[1], [0], [1], [0]])\n \n # Cria\u00e7\u00e3o e treinamento da rede neural\n input_size = X.shape[1] # N\u00famero de colunas das listas\n hidden_size = 4 # N\u00famero de neur\u00f4nios na camada oculta\n output_size = y.shape[1] # N\u00famero de sa\u00eddas\n \n neural_net = NeuralNetwork(input_size, hidden_size, output_size, learning_rate=0.1)\n neural_net.train(X, y, epochs=10000)\n \n # Fazendo previs\u00f5es\n predictions = neural_net.predict(X)\n predictions = np.round(predictions).astype(int)\n print(\"Predictions:\")\n print(predictions)\n\n```\nResults:\n\n```sh\n...\nEpoch 8000, Loss: 0.2617957\nEpoch 8100, Loss: 0.1830651\nEpoch 8200, Loss: 0.1800935\nEpoch 8300, Loss: 0.2585192\nEpoch 8400, Loss: 0.3310709\nEpoch 8500, Loss: 0.3215849\nEpoch 8600, Loss: 0.1803035\nEpoch 8700, Loss: 0.1802555\nEpoch 8800, Loss: 0.1807692\nEpoch 8900, Loss: 0.3312975\nEpoch 9000, Loss: 0.1800601\nEpoch 9100, Loss: 0.2642676\nEpoch 9200, Loss: 0.1930688\nEpoch 9300, Loss: 0.3279387\nEpoch 9400, Loss: 0.1871483\nEpoch 9500, Loss: 0.1809427\nEpoch 9600, Loss: 0.2635577\nEpoch 9700, Loss: 0.1855325\nEpoch 9800, Loss: 0.1796770\nEpoch 9900, Loss: 0.1800897\n\nPredictions:\n[[1]\n [0]\n [1]\n [1]]\n\n\n```\n\n<img src='https://reysofts.com.br/apis/engine/libs/analisys_lib.php?lib_name=adrianna'>\n",
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