code: def lins_ccc(y_true, y_pred):
      
  
      t = y_true.mean()
      p = y_pred.mean()
      St = y_true.var()
      Sp = y_pred.var()
      Spt = np.mean((y_true - t) * (y_pred - p))
  
      return 2 * Spt / (St + Sp + (t - p)**2)
docs: Lin's Concordance Correlation Coefficient.
  
      See https://en.wikipedia.org/wiki/Concordance_correlation_coefficient
  
      Parameters
      ----------
      y_true: ndarray
          vector of true targets
      y_pred: ndarray
          vector of predicted targets
  
      Returns
      -------
      float:
          1.0 for a perfect match between :code:`y_true` and :code:`y_pred`, less
          otherwise
  
      Example
      -------
      >>> y_true = np.random.randn(100)
      >>> lins_ccc(y_true, y_true) > 0.99  # Should be good predictor
      True
      >>> lins_ccc(y_true, np.zeros_like(y_true)) < 0.01  # Bad predictor
      True