Evaluate the Running Time of the Scripts

TimeDifferentialTypes.py

def ClassicalDifferential(x):
  return differentialfx(x)

def NumericalDifferential(x):
  return differentiate(f,x,h=1e-6)

def TorchDifferential(x):
  x = torch.autograd.Variable(torch.Tensor([0.5]),requires_grad=True)
  y = fnew(x)
  y.backward()
  return float(x.grad)

def RunDifferentiation(func):
  DIFFS = []
  for i in range(1,100):
    num = i/100
    diff = func(num)
    DIFFS.append(diff)
    
def RunMultiple(func,n):
  for i in range(n):
    x= RunDifferentiation(func)
  return 0

import time

def TimeFuncRun(func, n=100):
  start = time.time()
  ans = RunMultiple(func,n)
  return (time.time()-start)/n

n = 100
print('Over ',n,' Iterations :')
print('ClassicalDifferential :', TimeFuncRun(ClassicalDifferential, n), 'seconds.')
print('NumericalDifferential :', TimeFuncRun(NumericalDifferential, n), 'seconds.')
print('TorchDifferential :', TimeFuncRun(TorchDifferential, n), 'seconds.')

n = 1000
print('Over ',n,' Iterations :')
print('ClassicalDifferential :', TimeFuncRun(ClassicalDifferential, n), 'seconds.')
print('NumericalDifferential :', TimeFuncRun(NumericalDifferential, n), 'seconds.')
print('TorchDifferential :', TimeFuncRun(TorchDifferential, n), 'seconds.')