Package: toulouse
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.InstallationToulouse can be installed easily via pip. Open your terminal and run the following command:pipinstalltoulouseEnsure you have Python 3.8 or higher installed on your system to use Toulouse.ClassesCardTheCardclass 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' 🇩🇪DeckTheDeckclass represents a collection ofCardobjects.Inputs:cards(List[Card], optional): A list ofCardobjects to initialize the deck. Default isNone, 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 ifnewis True. Default is 52.language(str, optional): The language for card representations within the deck. Default is 'en'.Use CasesManipulating Cards and DecksCreating 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:Setdeck.sort_type = 'shuffle'thendeck.update_sort()Machine Learning Use CaseFor 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 Spadesbinary_representation=card.stateThis 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 ModelImagine 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.stateforcardindrawn_cards])# Assume labels are the outcomes you want to predictmodel.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.This README provides a basic overview of the Toulouse package, its installation, class inputs, and use cases.