Python Numpy Numpy.random用法及代码示例

• 关于随机：对于随机，我们采用.rand()
  numpy.random.rand(d0，d1，...，dn)：
  创建指定形状的数组，然后
  用随机值填充它。
  参数：

  d0, d1, ..., dn : [int, optional]
  Dimension of the returned array we require, 
  
  If no argument is given a single Python float 
  is returned.
  

  返回：

  Array of defined shape, filled with random values.
  

• 关于正常：对于随机，我们采用.normal()
  numpy.random.normal(loc = 0.0，scale = 1.0，size = None)：创建一个指定形状的数组，并用随机值填充它，这实际上是Normal(Gaussian)Distribution的一部分。由于其特征形状，此分布也称为钟形曲线。
  参数：
  

  loc   : [float or array_like]Mean of 
  the distribution. 
  scale : [float or array_like]Standard 
  Derivation of the distribution. 
  size  : [int or int tuples]. 
  Output shape given as (m, n, k) then
  m*n*k samples are drawn. If size is 
  None(by default), then a single value
  is returned. 
  

  返回：

  Array of defined shape, filled with 
  random values following normal 
  distribution.
  

代码1：随机构造一维数组

# Python Program illustrating 
# numpy.random.rand() method 
   
import numpy as geek 
   
# 1D Array 
array = geek.random.rand(5) 
print("1D Array filled with random values : \n", array)

输出：

1D Array filled with random values : 
 [ 0.84503968  0.61570994  0.7619945   0.34994803  0.40113761]

代码2：根据高斯分布随机构造一维数组

# Python Program illustrating 
# numpy.random.normal() method 
   
import numpy as geek 
   
# 1D Array 
array = geek.random.normal(0.0, 1.0, 5) 
print("1D Array filled with random values "
      "as per gaussian distribution : \n", array) 
# 3D array 
array = geek.random.normal(0.0, 1.0, (2, 1, 2)) 
print("\n\n3D Array filled with random values "
      "as per gaussian distribution : \n", array)

输出：

1D Array filled with random values as per gaussian distribution : 
 [-0.99013172 -1.52521808  0.37955684  0.57859283  1.34336863]

3D Array filled with random values as per gaussian distribution : 
 [[[-0.0320374   2.14977849]]

 [[ 0.3789585   0.17692125]]]

Code3：Python程序，说明NumPy中随机与正常的图形表示

# Python Program illustrating 
# graphical representation of  
# numpy.random.normal() method 
# numpy.random.rand() method 
   
import numpy as geek 
import matplotlib.pyplot as plot 
   
# 1D Array as per Gaussian Distribution 
mean = 0 
std = 0.1
array = geek.random.normal(0, 0.1, 1000) 
print("1D Array filled with random values "
      "as per gaussian distribution : \n", array); 
  
# Source Code :  
# https://docs.scipy.org/doc/numpy-1.13.0/reference/ 
# generated/numpy-random-normal-1.py 
count, bins, ignored = plot.hist(array, 30, normed=True) 
plot.plot(bins, 1/(std * geek.sqrt(2 * geek.pi)) *
          geek.exp( - (bins - mean)**2 / (2 * std**2) ), 
          linewidth=2, color='r') 
plot.show() 
  
  
# 1D Array constructed Randomly 
random_array = geek.random.rand(5) 
print("1D Array filled with random values : \n", random_array) 
  
plot.plot(random_array) 
plot.show()

输出：

1D Array filled with random values as per gaussian distribution : 
 [ 0.12413355  0.01868444  0.08841698 ..., -0.01523021 -0.14621625
 -0.09157214]



1D Array filled with random values : 
 [ 0.72654409  0.26955422  0.19500427  0.37178803  0.10196284]

重要：
在代码3中，图1清楚地显示了高斯分布，它是根据通过random.normal()方法生成的值创建的，因此遵循高斯分布。
图2不遵循任何分布，因为它是根据random.rand()方法生成的随机值创建的。