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]) <type 'tuple'>
- [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])) <type 'function'>
- [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
-
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