def random_walker(n: int, length: int) -> np.ndarray:
arr = np.zeros((n, length), dtype = float)
for i in range(n):
idx = 100
for j in range(length):
idx += idx*random.gauss(0.00015, 0.02)
arr[i, j] = idx
return arr
@njit
def nb_random_walker(n: int, length: int) -> np.ndarray:
arr = np.zeros((n, length), dtype = float)
for i in range(n):
idx = 100
for j in range(length):
idx += idx*random.gauss(0.00015, 0.02)
arr[i, j] = idx
return arr
我现在一个接一个地执行随机模拟运行，并计算两次运行的模拟持续时间。您可以在下面的代码中看到这一点。
# --- monte carlo run without numba ------------------
starttime = time.time()
arr = random_walker(10000, 365)
endtime = time.time()
print("monte carlo random walk without NUMBA: ")
print(endtime-starttime)
# --- monte carlo run with numba --------------------
starttime = time.time()
arr = nb_random_walker(10000, 365)
endtime = time.time()
print("monte carlo random walk with NUMBA: ")
print(endtime-starttime)
输出如下：
monte carlo random walk without NUMBA:
1.9181511402130127
monte carlo random walk with NUMBA:
0.6535243988037109
在这种情况下，
numba
将 monte-carlo 模拟运行时间几乎提高了
3 倍
。顺便说一下，这就是随机游走的样子：
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