evaluations(ty, pv, useScipy) -> (ACC, MSE, SCC)
	ty, pv: list, tuple or ndarray
	useScipy: convert ty, pv to ndarray, and use scipy functions for the evaluation

	Calculate accuracy, mean squared error and squared correlation coefficient
	using the true values (ty) and predicted values (pv).

def evaluations(ty, pv, useScipy = True):
	"""
	evaluations(ty, pv, useScipy) -> (ACC, MSE, SCC)
	ty, pv: list, tuple or ndarray
	useScipy: convert ty, pv to ndarray, and use scipy functions for the evaluation

	Calculate accuracy, mean squared error and squared correlation coefficient
	using the true values (ty) and predicted values (pv).
	"""
	if scipy != None and useScipy:
		return evaluations_scipy(scipy.asarray(ty), scipy.asarray(pv))
	if len(ty) != len(pv):
		raise ValueError("len(ty) must be equal to len(pv)")
	total_correct = total_error = 0
	sumv = sumy = sumvv = sumyy = sumvy = 0
	for v, y in zip(pv, ty):
		if y == v:
			total_correct += 1
		total_error += (v-y)*(v-y)
		sumv += v
		sumy += y
		sumvv += v*v
		sumyy += y*y
		sumvy += v*y
	l = len(ty)
	ACC = 100.0*total_correct/l
	MSE = total_error/l
	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))