toulouse 0.0.1

A Python Package for Card Games and Machine Learning

Toulouse: A Python Package for Card Games and Machine Learning 🃏

Toulouse is a Python package designed for creating and manipulating standard playing cards and decks, with a focus on applications in machine learning. It offers a simple yet powerful interface for card game simulation and analysis, providing binary representations of cards suitable for machine learning models.

Installation

Toulouse can be installed easily via pip. Open your terminal and run the following command:

pip install toulouse

Ensure you have Python 3.8 or higher installed on your system to use Toulouse.

Classes

Card

The Card class represents a single playing card.

Inputs:

• value (int): The value of the card (1-13, where 1 is Ace, 11 is Jack, 12 is Queen, and 13 is King in a case of 52 deck size).
• suit (int): The suit of the card (0-3, representing Spades ♠️, Hearts ♥️, Diamonds ♦️, and Clubs ♣️, respectively).
• deck_size (int, optional): The size of the deck, default is 52.
• language (str, optional): The language for card representation, default is 'en' (English).

You can choose between 'en' 🇬🇧, 'fr' 🇫🇷, 'it' 🇮🇹, 'es' 🇪🇸 and 'de' 🇩🇪

Deck

The Deck class represents a collection of Card objects.

Inputs:

• cards (List[Card], optional): A list of Card objects to initialize the deck. Default is None, which initializes a standard deck.
• new (bool, optional): If True, creates a new standard deck. Default is False.
• sort_type (str, optional): Determines how the deck is sorted or shuffled ('sort' for sorted, 'shuffle' for shuffled). Default is 'sort'.
• deck_size (int, optional): The size of the deck, affects card distribution if new is True. Default is 52.
• language (str, optional): The language for card representations within the deck. Default is 'en'.

Use Cases

Manipulating Cards and Decks

• Creating a New Deck: deck = Deck(new=True)
• Drawing Cards from the Deck: drawn_cards = deck.draw(5)
• Adding a Card to the Deck: deck.append(Card(1, 0)) (Adds an Ace of Spades)
• Removing a Card from the Deck: deck.remove(some_card)
• Shuffling the Deck: Set deck.sort_type = 'shuffle' then deck.update_sort()

Machine Learning Use Case

For machine learning applications, especially in game simulation and strategy analysis, the binary representation of cards can be utilized as features for models.

Binary Representation:

Each card can be represented as a binary vector where only one element is set to 1, and the rest are 0. This one-hot encoding allows the model to easily process card information.

card = Card(1, 0)  # Ace of Spades
binary_representation = card.state

This representation can be used as input for neural networks, decision trees, or any other machine learning model to analyze card games or simulate strategies.

Example: Training a Model

Imagine you're building a model to predict the outcome of a card game. You could use the binary representations of drawn cards as features:

features = np.array([card.state for card in drawn_cards])
# Assume labels are the outcomes you want to predict
model.fit(features, labels)

This simplistic example shows how you might begin to incorporate card data into a machine learning model. For more complex games or analysis, you might combine features from multiple cards, include game state information, or use embeddings.

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This README provides a basic overview of the Toulouse package, its installation, class inputs, and use cases.