Deeplearning specialization向量化代码（亲测可用）

这是vectorization课程学习的代码笔记，vscode+python编写，另外小虎也在MATLAB使用bsxfun函数进行广播。

Vectorization

• 时间对比
• 封装函数算法
• brocasting广播
• 用bsxfun广播

时间对比

import time
import numpy as np

a = np.random.rand(1000000)
b = np.random.rand(1000000)

tic = time.time()
c = np.dot(a, b)
toc = time.time()

print(c)
print("vectorized version:" + str(1000*(toc-tic)) + "ms")

c = 0
tic = time.time()
for i in range(1000000):
    c += a[i]*b[i]
toc = time.time()

print(c)
print("vectorized version:" + str(1000*(toc-tic)) + "ms")

封装函数算法

import time
import numpy as np

import math

v = np.array([1, 2, 3, 4])

tic = time.time()
u1 = np.zeros((4, 1))
for i in range(4):
    u1[i] = math.exp(v[i])
toc = time.time()

print(u1)
print(str(1000 * (toc-tic)) + "ms")

tic = time.time()
u2 = np.exp(v)
toc = time.time()

print(u2)
print(str(1000 * (toc-tic)) + "ms")

# np.log(v), np.abs(v) ,np.maximum(v,0), v**2

brocasting广播

import time
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]])

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

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

用bsxfun广播

A=[[56.0, 0.0, 4.4, 68.0];
    [1.2, 104.0, 52.0, 8.0];
    [1.8, 135.0, 99.0, 0.9]];

cal=sum(A,1)

fun=@(a,b)100*a./b;
percentage=bsxfun(fun,A,cal)

其实小虎觉得没必要用bsxfun，如下，直接算就好了。

A=[[56.0, 0.0, 4.4, 68.0];
    [1.2, 104.0, 52.0, 8.0];
    [1.8, 135.0, 99.0, 0.9]];

cal=sum(A,1)

percentage=100*A./cal