Numpy实现大矩阵减去小矩阵的方便运算
把一个向量加到矩阵的每一行:
调用numpy库
完成cs231作业1，numpy
参考知乎CS231n课程笔记翻译：Python Numpy教程
使用一重循环
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
import numpy as np
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x) # Create an empty matrix with the same shape as x
# Add the vector v to each row of the matrix x with an explicit loop
for i in range(4):
y[i, :] = x[i, :] + v
# Now y is the following
# [[ 2 2 4]
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]
print y
使用二重循环就是有点没必要了
但是要是大矩阵减去小矩阵还是可以用的，速度偏慢就是了
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
y = np.empty_like(x) # Create an empty matrix with the same shape as x
# Add the vector v to each row of the matrix x with an explicit loop
for i in range(4):
for j in range(4)
y[i, j] = x[i, j] + v[j]
# Now y is the following
# [[ 2 2 4]
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]
不使用循环，使用了numpy的广播机制
>> a = np.arange(15).reshape(3,5)
>> print(a)
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
>> b = np.arange(5)
>> print(b)
array([0, 1, 2, 3, 4])
>> a-b
array([[ 0, 0, 0, 0, 0],
[ 5, 5, 5, 5, 5],
[10, 10, 10, 10, 10]])
>>> b
array([0, 1, 2, 3, 4])
>>> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
#使用过后a,b的大小没有变换
还可以创建一个新的数组，使用numpy 的tile可以实现数组的叠加
np.tile(x,y)
x表示纵向的叠加，y表示横向的复制
import numpy as np
# We will add the vector v to each row of the matrix x,
# storing the result in the matrix y
x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])
v = np.array([1, 0, 1])
vv = np.tile(v, (4, 1)) # Stack 4 copies of v on top of each other
print vv # Prints "[[1 0 1]
# [1 0 1]
# [1 0 1]
# [1 0 1]]"
y = x + vv # Add x and vv elementwise
print y # Prints "[[ 2 2 4
# [ 5 5 7]
# [ 8 8 10]
# [11 11 13]]"