text: """
  Cost functions of Machine Learning
  """
  
  import numpy as np
  
  
  def calculate_mse_cost(y_pred: np.ndarray, y: np.ndarray) -> float:
      """Calculate error for regression model with mean squared error
  
      Args:
          y_pred (np.ndarray): predicted y value, y^.
          y (np.ndarray): actual y value. 
  
      Returns:
          float: mean squared error cost.
      """
      residual = y_pred - y
      diff_squared = np.square(residual)
      mse_cost = np.mean(diff_squared) / 2
      return float(mse_cost)
  
  
  def calculate_entropy_cost(y_pred: np.ndarray, y: np.ndarray) -> float:
      """Calculate entropy error for classification model
  
      Args:
          y_pred (np.ndarray): predicted y value, y^.
          y (np.ndarray): actual y value.
  
      Returns:
          float: entorpy error cost.
      """
  
      part_1 = y * np.log(y_pred)
  
      part_2 = (1 - y) * np.log(1 - y_pred)
  
      cost = (-1 / y_pred.shape[0]) * np.sum(part_1 + part_2)
      return cost
  