Python 中 rand vs normal . numpy . random
原文:https://www . geesforgeks . org/rand-vs-normal-numpy-random-python/
在本文中，我们将详细研究 numpy . rand . rand()方法和 Numpy.random.normal()方法之间的主要区别。
-
About random: For random we are taking .rand() numpy.random.rand(d0, d1, …, dn) : creates an array of specified shape and fills it with random values. Parameters :
```py 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:
```py Array of defined shape, filled with random values.
```
-
About normal: For random we are taking .normal() numpy.random.normal(loc = 0.0, scale = 1.0, size = None) : creates an array of specified shape and fills it with random values which is actually a part of Normal(Gaussian)Distribution. This is Distribution is also known as Bell Curve because of its characteristics shape. Parameters :
```py 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 mnk samples are drawn. If size is None(by default), then a single value is returned.
```
返回:
```py Array of defined shape, filled with random values following normal distribution.
```
代码 1:随机构建 1D 阵列
```py
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:
```py 1D Array filled with random values : [ 0.84503968 0.61570994 0.7619945 0.34994803 0.40113761]
```
代码 2:按照高斯分布随机构建 1D 阵列
```py
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:
```py 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]]]
```
代码 3 : Python 程序，演示了 NumPy 中随机与正常的图形表示
```py
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 * std2) ), 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:
```py 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]
```
重要提示: 在代码 3 中，图 1 清楚地显示了高斯分布，因为它是从通过 random.normal()方法生成的值创建的，因此遵循高斯分布。 图 2 没有遵循任何分布，因为它是根据 random.rand()方法生成的随机值创建的。
注意: 代码 3 不会在在线-ID 上运行。请在您的系统上运行它们来探索工作。 。 本文由 Mohit Gupta_OMG 供稿😀 。如果你喜欢 GeeksforGeeks 并想投稿，你也可以使用contribute.geeksforgeeks.org写一篇文章或者把你的文章邮寄到 contribute@geeksforgeeks.org。看到你的文章出现在极客博客主页上，帮助其他极客。
如果你发现任何不正确的地方，或者你想分享更多关于上面讨论的话题的信息，请写评论。
