import numpy as np

class Loss:
    @staticmethod
    def mean_squared_error(Y, A):
        """ Mean Squared Error (MSE) """
        return np.mean((Y - A) ** 2)
    
    @staticmethod
    def mean_absolute_error(Y, A):
        """ Mean Absolute Error (MAE) """
        return np.mean(np.abs(Y - A))
    
    @staticmethod
    def huber_loss(Y, A, delta=1.0):
        """ Huber Loss """
        error = Y - A
        is_small_error = np.abs(error) <= delta
        squared_loss = 0.5 * error ** 2
        linear_loss = delta * (np.abs(error) - 0.5 * delta)
        return np.where(is_small_error, squared_loss, linear_loss).mean()
    
    @staticmethod
    def binary_cross_entropy_loss(Y, A):
        """ Binary Cross-Entropy Loss """
        m = Y.shape[1]
        return -np.sum(Y * np.log(A + 1e-8) + (1 - Y) * np.log(1 - A + 1e-8)) / m
    
    @staticmethod
    def categorical_cross_entropy_loss(Y, A):
        """ Categorical Cross-Entropy Loss (for softmax) """
        m = Y.shape[1]
        return -np.sum(Y * np.log(A + 1e-8)) / m
    
    @staticmethod
    def hinge_loss(Y, A):
        """ Hinge Loss (used in SVM) """
        return np.mean(np.maximum(0, 1 - Y * A))
    
    @staticmethod
    def kl_divergence(P, Q):
        """ Kullback-Leibler Divergence """
        return np.sum(P * np.log(P / (Q + 1e-8)))
    
    @staticmethod
    def poisson_loss(Y, A):
        """ Poisson Loss """
        return np.mean(A - Y * np.log(A + 1e-8))
    
    @staticmethod
    def cosine_proximity_loss(Y, A):
        """ Cosine Proximity Loss """
        dot_product = np.sum(Y * A, axis=0)
        norms = np.linalg.norm(Y, axis=0) * np.linalg.norm(A, axis=0)
        return -np.mean(dot_product / (norms + 1e-8))

    @classmethod
    def get_function_name(cls, func):
        return func.__name__

    @classmethod
    def get_all_loss_names(cls):
        return [name for name, func in cls.__dict__.items() 
                if callable(func) and not name.startswith("__") and 
                not name in ['get_function_name', 'get_all_loss_names']]