#在python中可以使用numpy,简单的实现向量的运算
#z=np.dot(w.t,x)+b

#numpy examples
import numpy as np
A=np.array([[56.0,0.0,4.4,68.0],
          [1.2,104.0,52.0,8.0],
          [1.8,135.0,99.0,0.9]])
print(A)

[[  56.     0.     4.4   68. ]
 [   1.2  104.    52.     8. ]
 [   1.8  135.    99.     0.9]]

In [4]:

cal=A.sum(axis=0)
print(cal)

[  59.   239.   155.4   76.9]

In [6]:

percentage=100*A/cal.reshape(1,4)
percentage

Out[6]:

array([[ 94.91525424,   0.        ,   2.83140283,  88.42652796],
       [  2.03389831,  43.51464435,  33.46203346,  10.40312094],
       [  3.05084746,  56.48535565,  63.70656371,   1.17035111]])

In [7]:

cal.reshape(1,4)

Out[7]:

array([[  59. ,  239. ,  155.4,   76.9]])

In [8]:

cal.shape

Out[8]:

(4,)

In [9]:

A.shape

Out[9]:

(3, 4)

In [10]:

percentage=100*A/cal
percentage

Out[10]:

array([[ 94.91525424,   0.        ,   2.83140283,  88.42652796],
       [  2.03389831,  43.51464435,  33.46203346,  10.40312094],
       [  3.05084746,  56.48535565,  63.70656371,   1.17035111]])

In [11]:

cal

Out[11]:

array([  59. ,  239. ,  155.4,   76.9])

----

broadcasting会自动根据向量的大小进行补充。

比如一个是n*m，与一个1*m的相加/减/乘/除那么这个1*m（或者n*1）会自动复制n份（或者m份），变成n*m维数。然后对应元素做相应的处理。

DL study4 numpy的broadcasting方法

DL study4 numpy的broadcasting方法

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