docs: def evaluations_scipy(ty, pv):
  	"""
  	evaluations_scipy(ty, pv) -> (ACC, MSE, SCC)
  	ty, pv: ndarray
  
  	Calculate accuracy, mean squared error and squared correlation coefficient
  	using the true values (ty) and predicted values (pv).
  	"""
  	if not (scipy != None and isinstance(ty, scipy.ndarray) and isinstance(pv, scipy.ndarray)):
  		raise TypeError("type of ty and pv must be ndarray")
  	if len(ty) != len(pv):
  		raise ValueError("len(ty) must be equal to len(pv)")
  	ACC = 100.0*(ty == pv).mean()
  	MSE = ((ty - pv)**2).mean()
  	l = len(ty)
  	sumv = pv.sum()
  	sumy = ty.sum()
  	sumvy = (pv*ty).sum()
  	sumvv = (pv*pv).sum()
  	sumyy = (ty*ty).sum()
  	with scipy.errstate(all = 'raise'):
  		try:
  			SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy))
  		except:
  			SCC = float('nan')
  	return (float(ACC), float(MSE), float(SCC))