我有2个基本上做同样事情的函数，我想比较它们的运行时性能。
def calculate_bonus_by_table(data, value):
cdf = data["cdf"]
# CAUTION: the following loop doesn't do bound checking
i = 0
while (value > cdf[i]): i += 1
return data['bonus'][i]
def calculate_bonus_by_regression(data, value):
max_slope = len(data['bonus']) - 1
slope = int((value - 1) / 6)
if slope > max_slope:
slope = max_slope
slope += 1
return (0.205078125 * slope**2) + (0.68359375 * slope) - 70.888671875
data = json.load(open('bonus.json'))
上面使用的JSON文件的片段
{ "cdf": [6, 12, 18, 24, 30, 36, ...], "bonus": [-70, -68, -66, -64, -62, ...] }
在iPython中，我将该功能单独计时
%timeit calculate_bonus_by_table(data, 199)
1000000 loops, best of 3: 1.64 µs per loop
%timeit calculate_bonus_by_regression(data, 199)
The slowest run took 7.99 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 604 ns per loop
多次运行两个函数的timeit总能给出类似的结果。 .._ by_regression函数结果总是会提示缓存。
如果一个缓存而另一个不缓存，我如何比较2？为什么_by_regression函数被缓存而_by_table不是？在生产环境中使用_by_regression性能和缓存是否成立，或者我应该假设性能最差（从上面开始，7.99 * 604ns = 4.83us）？
由于