我目前有 2 个不同的函数，它们带有矢量化选项：我正在尝试将它们的运行时间与计算的迭代次数进行比较acc_rej_sine(max_iter, algorithm=None)。analytical_sine(max_iter, algorithm=None)即比较所有4种方法；2 个循环，2 个矢量化。基本上我的代码是这样的：
def analytical_sine(max_iter, algorithm=None):
if algorithm is None:
count = 0
analytical_hist = []
for i in range(max_iter):
count += 1
progress = round((count/max_iter)*100)
sys.stdout.write('\r' + str(progress) + '%')
uni_dist = np.random.uniform(0, 1)
arccos = np.arccos(1 - 2*uni_dist)
analytical_hist.append(arccos)
elif algorithm is "vectorise":
analytical_hist = np.arccos(1 - 2*np.random.uniform(0, 1, max_iter))
return analytical_hist
def acc_rej_sine(max_iter, algorithm=None):
x = np.random.uniform(0, np.pi, max_iter)
y = np.random.rand(max_iter)
if algorithm is None:
accepted_x = []
j = count = 0
for i in range(max_iter):
count += 1
progress = round((count/max_iter)*100)
sys.stdout.write('\r' + str(progress) + '%')
if y[i] <= np.sin(x[i]):
accepted_x.append(x[i])
j += 1
elif algorithm is "vectorise":
accepted_x = np.extract((y <= np.sin(x)), x)
return accepted_x
def runtime(func, runs, max_iter, algorithm=None):
time = []
for i in range(runs):
start = timer()
func()
end = timer()
time.append((end-start))
error = np.std(time)
time = sum(time)/runs
return time, error
def time_analysis():
time1, time2, time3, time4 = [], [], [], []
error1, error2, error3, error4 = [], [], [], []
for i in np.arange(1, 8, 1):
max_iter = 10**i
time, error = runtime(analytical_sine, 5, int(max_iter))
time1.append(time)
error1.append(error)
time, error = runtime(analytical_sine, 5, int(max_iter), "vectorise")
time2.append(time)
error2.append(error)
time, error = runtime(acc_rej_sine, 5, int(max_iter))
time3.append(time)
error3.append(error)
time, error = runtime(acc_rej_sine(max_iter), 5, int(max_iter), "vectorise")
time4.append(time)
error4.append(error)
return [time1, time2, time3, time4], [error1, error2, error3, error4]
# to run the code I would probably do something like this
time, error = time_analysis()
#then if I wanna plot number of iterations vs run time with errors I would probably do something along the lines of
plt.plot(max_iter, time) # max_iter would be a list of [10**i for i in np.arange(1, 8, 1)]
plt.errorbar(error)
所以我的想法是我的runtime()函数将允许我放入我想要比较的 4 个函数中的任何一个（目前还没有工作，我不能/还没有弄清楚为什么），并运行它 5次，计算出平均运行时间和标准偏差作为误差。作为回报，我的time_analysis()函数将runtime()针对不同的max_iter（最大迭代）运行不同的函数，就像[10, 1E2, 1E3, 1E4, 1E5, 1E6, 1E7]这样我可以根据运行时间绘制最大迭代。然而，整个方法看起来相当麻烦和不优雅，因为我time_analysis()需要我反复计算时间和错误并将其附加到列表中。有没有更好的计时方法？（而且我的runtime()还不行，哈哈，因为我的algorithm论点似乎没有做点什么callable）