semvec/pypi-packages huggingface dataset:
connect-4-cli 0.0.9
A CLI implementation of the classic connect 4 game

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# Connect 4 CLI
The classic 4-in-a-line game, also called connect 4, playable from the CLI or shell.

## Details
This is a simple implementation of the classic connect 4 game. It can be played directly from the CLI or shell.

## Installation
The game is available as a `pip` package. Download it with the following command:
```
pip install connect-4-cli
```

Preferably, install in [virtual environment](https://docs.python.org/3/library/venv.html).
In Windows, for example:
1. Run the following commands from the CLI:
```
py -m venv venv
```
2. Activate the virtual environment with the `activate` script inside the `./venv/Scripts/` folder:
   - Using command window: 
   ```
   call venv/Scripts/activate
   ```
   - Or using `powershell`:
   ```
   ./venv/Scripts/activate.ps1
   ```
3. `pip` install the package.

## How to play
Run the following command (with activated virtual environment if installed there) to start the game:
```
connect-4-cli
```
Play the game using keyboard keys, as prompted in the game.
custom-neuralnet 0.0.4
Custom Neural Networks
Requires Python: >=3.6

# Custom Neural Networks
* Create a customized Feedforward Neural Network by changing the number of layers, activation functions, loss function and optimizer.
* Refer to the documentation of any class/method by using help(class/method)
* Please refer to this [repo](https://github.com/Taarak9/Neural-Networks) for more information.

## Installation
```bash
$ [sudo] pip3 install custom-neuralnet
``` 
## Usage
```python3
>>> from customneuralnet import FNN
```
### Creating a Feedforward Neural Network
```python3
# number of input nodes
n_inputs = 27
loss_fn = "ce"
nn = FNN(n_inputs, loss_fn)

# Add a layer with 9 nodes and activation function ReLU
nn.add_layer(9, "relu")
# Add a layer with 3 nodes and activation function sigmoid
nn.add_layer(3, "sigmoid")

# Note the last layer you added will be the output layer of the NN
# Compile the nn
nn.compile(training_data, epochs, mini_batch_size, eta, gamma=None, optimizer="GD", mode="batch", shuffle=True, test_data, task="classification")
```