text: import numpy as np
  
  
  def mean_square_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
      """
      Calculate MSE loss
  
      Parameters
      ----------
      y_true: ndarray of shape (n_samples, )
          True response values
      y_pred: ndarray of shape (n_samples, )
          Predicted response values
  
      Returns
      -------
      MSE of given predictions
      """
  
      n_samples = y_pred.shape[0]
      error = y_pred - y_true
  
      return np.sum(error ** 2) / n_samples
  
  
  def misclassification_error(y_true: np.ndarray, y_pred: np.ndarray,
                              normalize: bool = True) -> float:
      """
      Calculate mis-classification loss
  
      Parameters
      ----------
      y_true: ndarray of shape (n_samples, )
          True response values
      y_pred: ndarray of shape (n_samples, )
          Predicted response values
      normalize: bool, default = True
          Normalize by number of samples or not
  
      Returns
      -------
      Misclassification of given predictions
      """
      return (np.count_nonzero(y_true != y_pred)
              / (y_true.shape[0] if normalize else 1))
  
  
  def accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
      """
      Calculate accuracy of given predictions
  
      Parameters
      ----------
      y_true: ndarray of shape (n_samples, )
          True response values
      y_pred: ndarray of shape (n_samples, )
          Predicted response values
  
      Returns
      -------
      Accuracy of given predictions
      """
      return 1 - misclassification_error(y_true, y_pred)
  
  
  def cross_entropy(y_true: np.ndarray, y_pred: np.ndarray) -> float:
      """
      Calculate the cross entropy of given predictions
  
      Parameters
      ----------
      y_true: ndarray of shape (n_samples, )
          True response values
      y_pred: ndarray of shape (n_samples, )
          Predicted response values
  
      Returns
      -------
      Cross entropy of given predictions
      """
      raise NotImplementedError()
  