parameters: (ty, pv)
return_statement: return (ACC, MSE, SCC)
docstring: evaluations(ty, pv) -> (ACC, MSE, SCC)
  
  	Calculate accuracy, mean squared error and squared correlation coefficient
  	using the true values (ty) and predicted values (pv).
docstring_summary: evaluations(ty, pv) -> (ACC, MSE, SCC)
docstring_tokens: evaluations ( ty pv ) - > ( ACC MSE SCC )
function: def evaluations(ty, pv):
  	"""
  	evaluations(ty, pv) -> (ACC, MSE, SCC)
  
  	Calculate accuracy, mean squared error and squared correlation coefficient
  	using the true values (ty) and predicted values (pv).
  	"""
  	if len(ty) != len(pv):
  		raise ValueError("len(ty) must 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 (ACC, MSE, SCC)
function_tokens: def evaluations ( ty , pv ) : if len ( ty ) != len ( pv ) : raise ValueError ( "len(ty) must 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 ( ACC , MSE , SCC )