[英]Use numba to speed up for loop
From what I've read, numba can significantly speed up a python program. 从我读过的内容来看,numba可以显着加快python程序的速度。 Could my program's time efficiency be increased using numba?
使用numba可以提高我的程序的时间效率吗?
import numpy as np
def f_big(A, k, std_A, std_k, mean_A=10, mean_k=0.2, hh=100):
return ( 1 / (std_A * std_k * 2 * np.pi) ) * A * (hh/50) ** k * np.exp( -1*(k - mean_k)**2 / (2 * std_k **2 ) - (A - mean_A)**2 / (2 * std_A**2))
outer_sum = 0
dk = 0.000001
for k in np.arange(dk,0.4, dk):
inner_sum = 0
for A in np.arange(dk, 20, dk):
inner_sum += dk * f_big(A, k, 1e-5, 1e-5)
outer_sum += inner_sum * dk
print outer_sum
Yes, this is the sort of problem that Numba really works for. 是的,这是Numba真正工作的那种问题。 I changed your value of
dk
because it wasn't sensible for a simple demonstration. 我改变了你的
dk
值,因为它对于一个简单的演示来说是不明智的。 Here is the code: 这是代码:
import numpy as np
import numba as nb
def f_big(A, k, std_A, std_k, mean_A=10, mean_k=0.2, hh=100):
return ( 1 / (std_A * std_k * 2 * np.pi) ) * A * (hh/50) ** k * np.exp( -1*(k - mean_k)**2 / (2 * std_k **2 ) - (A - mean_A)**2 / (2 * std_A**2))
def func():
outer_sum = 0
dk = 0.01 #0.000001
for k in np.arange(dk, 0.4, dk):
inner_sum = 0
for A in np.arange(dk, 20, dk):
inner_sum += dk * f_big(A, k, 1e-5, 1e-5)
outer_sum += inner_sum * dk
return outer_sum
@nb.jit(nopython=True)
def f_big_nb(A, k, std_A, std_k, mean_A=10, mean_k=0.2, hh=100):
return ( 1 / (std_A * std_k * 2 * np.pi) ) * A * (hh/50) ** k * np.exp( -1*(k - mean_k)**2 / (2 * std_k **2 ) - (A - mean_A)**2 / (2 * std_A**2))
@nb.jit(nopython=True)
def func_nb():
outer_sum = 0
dk = 0.01 #0.000001
X = np.arange(dk, 0.4, dk)
Y = np.arange(dk, 20, dk)
for i in xrange(X.shape[0]):
k = X[i] # faster to do lookup than iterate over an array directly
inner_sum = 0
for j in xrange(Y.shape[0]):
A = Y[j]
inner_sum += dk * f_big_nb(A, k, 1e-5, 1e-5)
outer_sum += inner_sum * dk
return outer_sum
And then timings: 然后时间:
In [7]: np.allclose(func(), func_nb())
Out[7]: True
In [8]: %timeit func()
1 loops, best of 3: 222 ms per loop
In [9]: %timeit func_nb()
The slowest run took 419.10 times longer than the fastest. This could mean that an intermediate result is being cached
1000 loops, best of 3: 362 µs per loop
So the numba version is approx 600 times faster on my laptop. 所以numba版本在我的笔记本电脑上快了大约600倍。
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