直接使用Python来实现向量的相加
# -*-coding:utf-8-*-
#向量相加
def pythonsum(n):
a = range(n)
b = range(n)
c = []
for i in range(len(a)):
a[i] = i**2
b[i] = i**3
c.append(a[i]+b[i])
return a,b,c
print pythonsum(4),type(pythonsum(4))
for arg in pythonsum(4):
print arg
从这里这个输出结果可以看得出来，return多个值时，是以列表的形式返回的
([0, 1, 4, 9], [0, 1, 8, 27], [0, 2, 12, 36])
[0, 1, 4, 9]
[0, 1, 8, 27]
[0, 2, 12, 36]
使用numpy包实现两个向量的相加
def numpysum(n):
a = np.arange(n) ** 2
b = np.arange(n) ** 3
c = a + b
return a,b,c
(array([0, 1, 4, 9]), array([ 0, 1, 8, 27]), array([ 0, 2, 12, 36]))
[0 1 4 9]
[ 0 1 8 27]
[ 0 2 12 36]
比较用Python实现两个向量相加和用numpy实现两个向量相加的情况
size = 1000
start = datetime.now()
c = pythonsum(size)
delta = datetime.now() - start
# print 'The last 2 elements of the sum',c[-2:]
print 'pythonSum elapsed time in microseconds',delta.microseconds
size = 1000
start1 = datetime.now()
c1 = numpysum(size)
delta1 = datetime.now() - start1
# print 'The last 2 elements of the sum',c1[-2:]
print 'numpySum elapsed time in microseconds',delta1.microseconds
从下面程序运行结果我们可以看到在处理向量是numpy要比Python计算高出不知道多少倍
pythonSum elapsed time in microseconds 1000
numpySum elapsed time in microseconds 0
以上这篇关于Python中的向量相加和numpy中的向量相加效率对比就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。
