text: import collections
  
  import numpy as np
  import sklearn.metrics
  
  
  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
      """
      # raise NotImplementedError()
      assert y_true.size > 0
      assert not np.any(np.isnan(y_true))
      assert not np.any(np.isnan(y_pred))
      return sum(((y_true - y_pred) ** 2) / y_true.size)
  
  
  def misclassification_error(y_true: np.ndarray, y_pred: np.ndarray,
                              normalize: bool = True) -> float:
      """
      Calculate misclassification 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
      """
      res = np.sum(y_true != y_pred)
      return res if not normalize else res / y_true.size
      # raise NotImplementedError()
  
  
  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
      """
      miss = misclassification_error(y_true, y_pred, False)
      return (y_true.size - miss) / y_true.size
      # raise NotImplementedError()
  
  
  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()
  