 import numpy as np
from sklearn.linear_model import LinearRegression

class BloxflipCrashPredictor:
    """
    Class to handle the Bloxflip crash predictor model.

    Attributes:
    - model: LinearRegression
        The trained linear regression model.
    """

    def __init__(self):
        """
        Constructor to instantiate the BloxflipCrashPredictor class.
        Initializes an empty linear regression model.
        """
        self.model = LinearRegression()

    def train_model(self, X, y):
        """
        Trains the linear regression model using the given training data.

        Parameters:
        - X: numpy.ndarray
            The input features for training the model. Each row represents a set of 32 past games.
        - y: numpy.ndarray
            The target variable (next game's outcome) for training the model.

        Raises:
        - ValueError:
            Raises an error if the number of rows in X does not match the length of y.
        """
        if len(X) != len(y):
            raise ValueError("Number of rows in X should match the length of y.")

        self.model.fit(X, y)

    def predict_next_game_result(self, X):
        """
        Predicts the outcome of the next game using the trained model.

        Parameters:
        - X: numpy.ndarray
            The input features for predicting the next game's outcome. Each row represents a set of 32 past games.

        Returns:
        - numpy.ndarray:
            The predicted outcome for each set of past games.
        """
        return self.model.predict(X)

# Example usage:

# Creating an instance of BloxflipCrashPredictor
predictor = BloxflipCrashPredictor()

# Generating random training data for demonstration purposes
np.random.seed(0)
X_train = np.random.rand(1500, 32)
y_train = np.random.randint(0, 2, size=1500)

# Training the model
predictor.train_model(X_train, y_train)

# Generating random test data for demonstration purposes
X_test = np.random.rand(10, 32)

# Predicting the outcome of the next game using the trained model
predictions = predictor.predict_next_game_result(X_test)

print("Predictions for the next game's outcome:")
for i, prediction in enumerate(predictions):
    print(f"Set {i+1}: {prediction}")

created 1 year ago

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