python随机数模块@numpy@随机数RandomGenerator@生成指定范围内的随机数序列@js随机数

文章目录

python随机数模块@numpy@随机数RandomGenerator@生成指定范围内的随机数序列@js随机数
生成自定范围内不重复的随机数序列
js@指定范围内的随机数
- reference link
- 指定范围内的随机数
python@得到不重复的指定范围内的随机数🎈
numpy&随机数🎈

python随机数模块@numpy@随机数RandomGenerator@生成指定范围内的随机数序列@js随机数

生成自定范围内不重复的随机数序列

公式

一般的

欲要得到[left,right)范围的随机数,可以:
- $令y=left+(right-left)\times{r},r\in[0,1) \\ 则y\in[left,right)$

特殊的

得到[0,right)半开区间内的随机数,通过 $right\times{r}$ 的方式得到,其中 $r\in[0,1)$
- $r\in[0,1) \\ y=right\times{r}\in[0,right)$

numpy接口@得到指定范围内的浮点数矩阵

使用uniform函数(均匀分布)

numpy.random.Generator.uniform — NumPy v1.24 Manual
python - How to get a random number between a float range? - Stack Overflow
假设我们要得到[4,7)内的随机浮点数矩阵

 import numpy.random as npr
rng=npr.default_rng()
size=(3,4)
C=rng.uniform(4,7,size)
print(f"{C=}")

 C=array([[6.0436931 , 5.63331156, 6.11905388, 5.77916688],
       [5.6442441 , 5.61249485, 6.79054321, 6.7742957 ],
       [4.80639433, 6.3189182 , 6.72264963, 4.94990386]])

使用上一节介绍的公式

 ##
import numpy.random as npr
#旧api
size=(3,4)# size 规格
A=npr.random(size)*(7-4)+4
#新api
rng=npr.default_rng()
B=rng.random(size)*(7-4)+4
print(f"{A=}\n{B=}")

output

 A=array([[0.73924313, 0.86760037, 0.18800622, 0.8370736 ],
       [0.10841024, 0.0564878 , 0.83902755, 0.64796633],
       [0.32000126, 0.21304282, 0.0333497 , 0.33100477]])
B=array([[0.93182649, 0.40417216, 0.06125742, 0.36432193],
       [0.48754533, 0.69363528, 0.34998984, 0.70583201],
       [0.41260264, 0.11074999, 0.83018146, 0.24863994]])

numpy得到指定范围内的整数矩阵

numpy得到[4,7]内的范围的整数矩阵(3行4列)

 import numpy.random as npr
#旧api
size=(3,4)# size 规格
A=npr.randint(4,7+1,size)
#新api
rng=npr.default_rng()
B=rng.integers(4,8,size=size)
print(f"{A=}\n{B=}")

output

 A=array([[6, 5, 7, 4],
       [4, 6, 4, 5],
       [5, 5, 6, 6]])
B=array([[6, 6, 6, 7],
       [6, 6, 7, 5],
       [6, 6, 6, 6]], dtype=int64)

js@指定范围内的随机数

reference link

Math.random() - JavaScript | MDN (mozilla.org)
- 示例 (mozilla.org)

指定范围内的随机数

 Math.floor(Math.random() * (max - min + 1)) + min

这里用到向下取整

python@得到不重复的指定范围内的随机数🎈

python自带实现(sample)

random — 生成伪随机数 — Python 3.10.4 文档

 population=('a', 'b', 'c', 'd')
rand_sample_immutable2 = random.sample( population,k=len(population))
print("@rand_sample_immutable2:", rand_sample_immutable2)
#output
#@rand_sample_immutable2: ['d', 'c', 'a', 'b']

简单的实现算法

Non-repetitive random number in numpy - Stack Overflow
打乱数组法
在python中,可以用shuffle函数进行打乱,然后返回这个被打乱的数组(部分或者全部)
返回随机抽取的对象
手动生成指定范围内的序列,存储在容器中(例如列表/数组)
打乱这个序列(执行shuffle操作/或者自行实现shuffle操作)
再以这些随机(乱序)数作为key/index,到容器中取出对象
例如:生成指定数目(譬如20个)值在20~100内的数(20个值不重复)

借助shuffle函数

 import random as rand
def get_range_randoms(low=20, high=100, size=10, is_contain_high=0, is_sorted=1):
    '''
 
    :param low: 随机数下界
    :type low:
    :param high: 随机数上界
    :type high:
    :param size: 需要取出多少个随机数
    :type size:
    :param is_contain_high:默认开区间; 0表示开区间;1表示闭区间
    :type is_contain_high:
    :param is_sorted: 默认排序;0表示排序;1表示排序;
    :type is_sorted:
    :return:
    :rtype:
    '''
    if is_contain_high:
        high += 1
    range_list = list(range(low, high))  # 如果需要闭区间,可以为upper_bound+1
    rand.shuffle(range_list)
    shuffled_list = range_list
    sized_list = shuffled_list[:size]
    # print(randon_list)
    ##
    # 可选(排序这些随机数)
    if (is_sorted):
        sized_list.sort()
 
    # 查看结果
    return sized_list
 
 
res=get_range_randoms(55, 177, 10, is_contain_high=0,is_sorted=1)
print(res)

python随机数模块

 # from random import random
import random
 
# 获取闭区间内的随机数
rand_int = random.randint(1, 10)
print("@rand_int:", rand_int)
rand_int = random.randrange(1, 10 + 1)
print("@rand_int:", rand_int)
# 获取0-2^k次幂内的整数(左闭右开)0...(111..1)
rand_bits = random.getrandbits(2)
print("@rand_bits:", rand_bits, type(rand_bits))
# 从给定的序列(集合)中随机选中一个元素
rand_choice = random.choice(['a', 'b', 'c', 'd'])
print("@rand_choice:", rand_choice)
# 从给定的序列中返回子集
rand_choices = random.choices(population=['a', 'b', 'c', 'd'], k=3)
print("@rand_choices:", rand_choices)
# rand_shuffle = random.shuffle(['a', 'b', 'c', 'd'])# 不恰当当用法,返回None;被随机排序的对象会发生改变!
seq_mutable= ['a', 'b', 'c', 'd']
seq_mutable_bak=seq_mutable.copy()
random.shuffle(seq_mutable)
 
print("@rand_shuffle:", seq_mutable,'<-',seq_mutable_bak)
rand_sample_immutable = random.sample(population=('a', 'b', 'c', 'd'), k=3)
print("@rand_sample_immutable:", rand_sample_immutable,'<-',('a', 'b', 'c', 'd'))
population=('a', 'b', 'c', 'd')
seq_sample = random.sample(population, k=len(population))
print("@rand_sample_immutable2:", seq_sample, '<-', population)

输出(某一次)

 @rand_int: 3
@rand_int: 9
@rand_bits: 3 <class 'int'>
@rand_choice: d
@rand_choices: ['a', 'a', 'c']
@rand_shuffle: ['d', 'b', 'a', 'c'] <- ['a', 'b', 'c', 'd']
@rand_sample_immutable: ['a', 'b', 'd'] <- ('a', 'b', 'c', 'd')
@rand_sample_immutable2: ['c', 'd', 'b', 'a'] <- ('a', 'b', 'c', 'd')

numpy.Generator.choice方法🎈

numpy.random.Generator.choice — NumPy v1.24 Manual
- ```
 import numpy.random as npr
rng=npr.default_rng()
RF=rng.choice(15,size=(3,4),replace=False)
print(RF)
```
- 关键是参数replace=False
  - Whether the sample is with or without replacement. Default is True, meaning that a value of a can be selected multiple times.
  - 这不能理解为元素去重
- 上述代码生成[0,14]范围内序列,返回3行4列的矩阵(元素不重复)
- output
  - ```
   array([[ 2,  5,  9, 14],
          [13, 10,  4,  3],
          [ 6,  7,  1,  0]], dtype=int64))
```

其他demo

 D=rng.integers(0,10,size=15)
RTD=rng.choice(D,size=(3,4))
RFD=rng.choice(D,size=(3,4),replace=False)
print(f'{D=}\n{RTD=}\n{RFD=}')

 D=array([8, 2, 8, 6, 1, 8, 3, 9, 7, 8, 6, 6, 1, 4, 7], dtype=int64)
RTD=array([[1, 1, 8, 1],
       [9, 7, 8, 8],
       [6, 8, 7, 6]], dtype=int64)
RFD=array([[4, 8, 2, 6],
       [6, 8, 8, 1],
       [9, 6, 7, 7]], dtype=int64)

numpy&随机数🎈

随机数模块api文档

Random Generator — NumPy Manual

概要

Random sampling (numpy.random)

Numpy’s random number routines produce pseudo random numbers using combinations of a BitGenerator to create sequences and a Generator to use those sequences to sample from different statistical distributions:
BitGenerators: Objects that generate random numbers. These are typically unsigned integer words filled with sequences of either 32 or 64 random bits.
Generators: Objects that transform sequences of random bits from a BitGenerator into sequences of numbers that follow a specific probability distribution (such as uniform, Normal or Binomial) within a specified interval.
Since Numpy version 1.17.0 the Generator can be initialized with a number of different BitGenerators.
- It exposes many different probability distributions. See NEP 19 for context on the updated random Numpy number routines.
- The legacy RandomState random number routines are still available, but limited to a single BitGenerator.
- See What’s New or Different for a complete list of improvements and differences from the legacy RandomState.
- For convenience and backward compatibility, a single RandomState instance’s methods are imported into the numpy.random namespace, see Legacy Random Generation for the complete list.

Random Generator

rng:random-generator
The Generator provides access to a wide range of distributions, and served as a replacement for RandomState.
The main difference between the two is that Generator relies on an additional BitGenerator to manage state and generate the random bits, which are then transformed into random values from useful distributions.
The default BitGenerator used by Generator is PCG64.
The BitGenerator can be changed by passing an instantized BitGenerator to Generator.

新旧API

Random sampling (numpy.random) — NumPy v1.24 Manual
Generator can be used as a replacement for RandomState. Both class instances hold an internal BitGenerator instance to provide the bit stream, it is accessible as gen.bit_generator.

Some long-overdue API cleanup means that legacy and compatibility methods have been removed from Generator

`RandomState`	`Generator`	Notes
`random_sample`,	`random`	Compatible with `random.random`
`rand`
`randint`,	`integers`	Add an `endpoint` kwarg
`random_integers`
`tomaxint`	removed	Use `integers(0, np.iinfo(np.int_).max,` `endpoint=False)`
`seed`	removed	Use `SeedSequence.spawn`

See What’s New or Different for more information.

随机数模块的基本使用🎈

构造RandomGenerator

 import numpy as np
# 随机数模块
# 实例化一个默认的随机数产生器
rng = np.random.default_rng()
print("@rng:", rng)

```
 @rng: Generator(PCG64)
```

生成指定形状的n维数组

整型数矩阵

 ##
# 生成一个随机整数数组(元素大小范围[0,10),包含3个随机整数
rints = rng.integers(low=0, high=10, size=3)
print( rints,"@rints1")
##
# 生成一个元素大小范围为[5,10),3行4列的随机整数矩阵
rints = rng.integers(low=5, high=10, size=(3,4))
print("%s@rints2"% rints)
##
# 生成一个元素大小范围为[5,10),3行4列的随机整数矩阵
rints = rng.integers(low=5, high=10, size=(3,4,2))
print("%s@rints3"% rints)

 [6 2 2] @rints1
[[5 9 6 8]
 [7 6 5 6]
 [9 6 6 7]]@rints2
[[[5 7]
  [7 7]
  [7 5]
  [7 7]]
 
 [[9 6]
  [5 9]
  [7 5]
  [9 6]]
 
 [[9 6]
  [5 8]
  [6 9]
  [6 9]]]@rints3

老式api:(调用方式类似)

 import numpy as np
 
# 生成3行4列的随机整数矩阵
random_matrix = np.random.randint(0, 10, size=(3, 4))
 
print(random_matrix)

浮点数矩阵

 # 生成一个随机浮点数[0,1)
rfloat = rng.random()
print("@rfloat:", rfloat)
##
# 产生元素在[0,1)随机浮点数矩阵(shape=(3,3))
arr1 = rng.random((3, 3))
print("@arr1:\n", arr1)
##
#生成元素在[10,11)的3行3列随机浮点数矩阵
arr3 = rng.random((3, 3))+10
print("@arr3:\n", arr3)
#生成元素在[10,15)的3行3列随机浮点数矩阵
arr3 = rng.random((3, 3))*(15-10)+10
print("@arr3:\n", arr3)
##
# 生成0~10间的随机数浮点数
arr4 = rng.random((3, 3))*10
print("@arr4:\n", arr4)

 @rfloat: 0.7939732827048979
@arr1:
 [[0.99358211 0.41876978 0.02352003]
 [0.05406612 0.23521216 0.78707249]
 [0.06253873 0.01311899 0.20807799]]
@arr3:
 [[10.04022177 10.46978502 10.18798832]
 [10.22221633 10.95567349 10.35092544]
 [10.51654467 10.77036623 10.03771871]]
@arr3:
 [[14.39844957 14.39288912 14.94155331]
 [13.59679302 14.93300216 11.32998308]
 [11.10215349 12.17755138 14.9722963 ]]
@arr4:
 [[8.05027557 0.93742348 7.96298449]
 [6.06692369 9.4384969  8.14886692]
 [9.55586509 5.97955699 1.40634796]]

老式api:

 import numpy as np
 
# 生成3行4列的随机浮点数矩阵
random_matrix = np.random.rand(3, 4)
 
print(random_matrix)

 [[0.62246687 0.64744595 0.34477091 0.13634874]
 [0.9282927  0.10339838 0.82403918 0.0128686 ]
 [0.83765859 0.93527123 0.93757736 0.78928998]]

数理统计和随机数

 import matplotlib.pyplot as pl
# 概率论&数理统计:符合泊松分布的数据集使用案例
rng = np.random.default_rng()
s = rng.poisson(5, 10000)
count, bins, ignored = plt.hist(s, 14, density=True)
plt.show()

随机矩阵元素精度设置

 import numpy as np
# a = np.arange(16).reshape(4,4)
#rng:random generator
# Construct a new Generator with the default BitGenerator (PCG64).
rng = np.random.default_rng()
 
##
c=rng.random(size=(4,4))
# 保留三位小数(可以确保打印的时候每个元素的小数位数不超过3位)
d=c.round(3)
print(d)
##
# 一种可能的实现(存在精度表示问题,仅作为一种理论上的方法)
p=c%0.001
# p=c%1e*3
d=c-p
print(d)
#两种方式在打印的时候都不打印结尾的0串(如果有的话)
##矩阵转置的一种实现
d=c.round(3)
for i in d:
    # print(i)
    for j in i:
        print(j,end="\t")
    print()
print("translating...","-"*10)
l=len(d)
for i in range(l):
    # print(i)
    for j in range(l):
        print(d[j,i],end="\t")
    print()

 0.067	0.367	0.923	0.795	
0.086	0.041	0.033	0.969	
0.572	0.868	0.353	0.908	
0.196	0.508	0.374	0.743	
translating... ----------
0.067	0.086	0.572	0.196	
0.367	0.041	0.868	0.508	
0.923	0.033	0.353	0.374	
0.795	0.969	0.908	0.743

posted @ 2021-08-30 17:45 xuchaoxin1375 阅读(12) 评论(0) 编辑收藏举报来源

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python随机数模块@numpy@随机数RandomGenerator@生成指定范围内的随机数序列@js随机数

文章目录

python随机数模块@numpy@随机数RandomGenerator@生成指定范围内的随机数序列@js随机数

生成自定范围内不重复的随机数序列

公式

一般的

特殊的

numpy接口@得到指定范围内的浮点数矩阵

使用uniform函数(均匀分布)

使用上一节介绍的公式

numpy得到指定范围内的整数矩阵

js@指定范围内的随机数

reference link

指定范围内的随机数

python@得到不重复的指定范围内的随机数🎈

python自带实现(sample)

简单的实现算法

借助shuffle函数

python随机数模块

输出(某一次)

numpy.Generator.choice方法🎈

numpy&随机数🎈

随机数模块api文档

概要

Random Generator

新旧API

随机数模块的基本使用🎈

构造RandomGenerator

生成指定形状的n维数组

整型数矩阵

浮点数矩阵

数理统计和随机数

随机矩阵元素精度设置

公告

搜索

常用链接

随笔档案

阅读排行榜

推荐排行榜

	import numpy.random as npr
	rng=npr.default_rng()
	size=(3,4)
	C=rng.uniform(4,7,size)
	print(f"{C=}")

	C=array([[6.0436931 , 5.63331156, 6.11905388, 5.77916688],
	[5.6442441 , 5.61249485, 6.79054321, 6.7742957 ],
	[4.80639433, 6.3189182 , 6.72264963, 4.94990386]])

	##
	import numpy.random as npr
	#旧api
	size=(3,4)# size 规格
	A=npr.random(size)*(7-4)+4
	#新api
	rng=npr.default_rng()
	B=rng.random(size)*(7-4)+4
	print(f"{A=}\n{B=}")

	A=array([[0.73924313, 0.86760037, 0.18800622, 0.8370736 ],
	[0.10841024, 0.0564878 , 0.83902755, 0.64796633],
	[0.32000126, 0.21304282, 0.0333497 , 0.33100477]])
	B=array([[0.93182649, 0.40417216, 0.06125742, 0.36432193],
	[0.48754533, 0.69363528, 0.34998984, 0.70583201],
	[0.41260264, 0.11074999, 0.83018146, 0.24863994]])

	A=array([[6, 5, 7, 4],
	[4, 6, 4, 5],
	[5, 5, 6, 6]])
	B=array([[6, 6, 6, 7],
	[6, 6, 7, 5],
	[6, 6, 6, 6]], dtype=int64)

	population=('a', 'b', 'c', 'd')
	rand_sample_immutable2 = random.sample( population,k=len(population))
	print("@rand_sample_immutable2:", rand_sample_immutable2)
	#output
	#@rand_sample_immutable2: ['d', 'c', 'a', 'b']

	import random as rand
	def get_range_randoms(low=20, high=100, size=10, is_contain_high=0, is_sorted=1):
	'''

	:param low: 随机数下界
	:type low:
	:param high: 随机数上界
	:type high:
	:param size: 需要取出多少个随机数
	:type size:
	:param is_contain_high:默认开区间; 0表示开区间;1表示闭区间
	:type is_contain_high:
	:param is_sorted: 默认排序;0表示排序;1表示排序;
	:type is_sorted:
	:return:
	:rtype:
	'''
	if is_contain_high:
	high += 1
	range_list = list(range(low, high)) # 如果需要闭区间,可以为upper_bound+1
	rand.shuffle(range_list)
	shuffled_list = range_list
	sized_list = shuffled_list[:size]
	# print(randon_list)
	##
	# 可选(排序这些随机数)
	if (is_sorted):
	sized_list.sort()

	# 查看结果
	return sized_list


	res=get_range_randoms(55, 177, 10, is_contain_high=0,is_sorted=1)
	print(res)

	# from random import random
	import random

	# 获取闭区间内的随机数
	rand_int = random.randint(1, 10)
	print("@rand_int:", rand_int)
	rand_int = random.randrange(1, 10 + 1)
	print("@rand_int:", rand_int)
	# 获取0-2^k次幂内的整数(左闭右开)0...(111..1)
	rand_bits = random.getrandbits(2)
	print("@rand_bits:", rand_bits, type(rand_bits))
	# 从给定的序列(集合)中随机选中一个元素
	rand_choice = random.choice(['a', 'b', 'c', 'd'])
	print("@rand_choice:", rand_choice)
	# 从给定的序列中返回子集
	rand_choices = random.choices(population=['a', 'b', 'c', 'd'], k=3)
	print("@rand_choices:", rand_choices)
	# rand_shuffle = random.shuffle(['a', 'b', 'c', 'd'])# 不恰当当用法,返回None;被随机排序的对象会发生改变!
	seq_mutable= ['a', 'b', 'c', 'd']
	seq_mutable_bak=seq_mutable.copy()
	random.shuffle(seq_mutable)

	print("@rand_shuffle:", seq_mutable,'<-',seq_mutable_bak)
	rand_sample_immutable = random.sample(population=('a', 'b', 'c', 'd'), k=3)
	print("@rand_sample_immutable:", rand_sample_immutable,'<-',('a', 'b', 'c', 'd'))
	population=('a', 'b', 'c', 'd')
	seq_sample = random.sample(population, k=len(population))
	print("@rand_sample_immutable2:", seq_sample, '<-', population)

	@rand_int: 3
	@rand_int: 9
	@rand_bits: 3 <class 'int'>
	@rand_choice: d
	@rand_choices: ['a', 'a', 'c']
	@rand_shuffle: ['d', 'b', 'a', 'c'] <- ['a', 'b', 'c', 'd']
	@rand_sample_immutable: ['a', 'b', 'd'] <- ('a', 'b', 'c', 'd')
	@rand_sample_immutable2: ['c', 'd', 'b', 'a'] <- ('a', 'b', 'c', 'd')

	import numpy.random as npr
	rng=npr.default_rng()
	RF=rng.choice(15,size=(3,4),replace=False)
	print(RF)

	array([[ 2, 5, 9, 14],
	[13, 10, 4, 3],
	[ 6, 7, 1, 0]], dtype=int64))

	D=rng.integers(0,10,size=15)
	RTD=rng.choice(D,size=(3,4))
	RFD=rng.choice(D,size=(3,4),replace=False)
	print(f'{D=}\n{RTD=}\n{RFD=}')

	D=array([8, 2, 8, 6, 1, 8, 3, 9, 7, 8, 6, 6, 1, 4, 7], dtype=int64)
	RTD=array([[1, 1, 8, 1],
	[9, 7, 8, 8],
	[6, 8, 7, 6]], dtype=int64)
	RFD=array([[4, 8, 2, 6],
	[6, 8, 8, 1],
	[9, 6, 7, 7]], dtype=int64)

	import numpy as np
	# 随机数模块
	# 实例化一个默认的随机数产生器
	rng = np.random.default_rng()
	print("@rng:", rng)

	##
	# 生成一个随机整数数组(元素大小范围[0,10),包含3个随机整数
	rints = rng.integers(low=0, high=10, size=3)
	print( rints,"@rints1")
	##
	# 生成一个元素大小范围为[5,10),3行4列的随机整数矩阵
	rints = rng.integers(low=5, high=10, size=(3,4))
	print("%s@rints2"% rints)
	##
	# 生成一个元素大小范围为[5,10),3行4列的随机整数矩阵
	rints = rng.integers(low=5, high=10, size=(3,4,2))
	print("%s@rints3"% rints)

	[6 2 2] @rints1
	[[5 9 6 8]
	[7 6 5 6]
	[9 6 6 7]]@rints2
	[[[5 7]
	[7 7]
	[7 5]
	[7 7]]

	[[9 6]
	[5 9]
	[7 5]
	[9 6]]

	[[9 6]
	[5 8]
	[6 9]
	[6 9]]]@rints3

	import numpy as np

	# 生成3行4列的随机整数矩阵
	random_matrix = np.random.randint(0, 10, size=(3, 4))

	print(random_matrix)

	# 生成一个随机浮点数[0,1)
	rfloat = rng.random()
	print("@rfloat:", rfloat)
	##
	# 产生元素在[0,1)随机浮点数矩阵(shape=(3,3))
	arr1 = rng.random((3, 3))
	print("@arr1:\n", arr1)
	##
	#生成元素在[10,11)的3行3列随机浮点数矩阵
	arr3 = rng.random((3, 3))+10
	print("@arr3:\n", arr3)
	#生成元素在[10,15)的3行3列随机浮点数矩阵
	arr3 = rng.random((3, 3))*(15-10)+10
	print("@arr3:\n", arr3)
	##
	# 生成0~10间的随机数浮点数
	arr4 = rng.random((3, 3))*10
	print("@arr4:\n", arr4)

	@rfloat: 0.7939732827048979
	@arr1:
	[[0.99358211 0.41876978 0.02352003]
	[0.05406612 0.23521216 0.78707249]
	[0.06253873 0.01311899 0.20807799]]
	@arr3:
	[[10.04022177 10.46978502 10.18798832]
	[10.22221633 10.95567349 10.35092544]
	[10.51654467 10.77036623 10.03771871]]
	@arr3:
	[[14.39844957 14.39288912 14.94155331]
	[13.59679302 14.93300216 11.32998308]
	[11.10215349 12.17755138 14.9722963 ]]
	@arr4:
	[[8.05027557 0.93742348 7.96298449]
	[6.06692369 9.4384969 8.14886692]
	[9.55586509 5.97955699 1.40634796]]

	import numpy as np

	# 生成3行4列的随机浮点数矩阵
	random_matrix = np.random.rand(3, 4)

	print(random_matrix)

	[[0.62246687 0.64744595 0.34477091 0.13634874]
	[0.9282927 0.10339838 0.82403918 0.0128686 ]
	[0.83765859 0.93527123 0.93757736 0.78928998]]

	import matplotlib.pyplot as pl
	# 概率论&数理统计:符合泊松分布的数据集使用案例
	rng = np.random.default_rng()
	s = rng.poisson(5, 10000)
	count, bins, ignored = plt.hist(s, 14, density=True)
	plt.show()

	import numpy as np
	# a = np.arange(16).reshape(4,4)
	#rng:random generator
	# Construct a new Generator with the default BitGenerator (PCG64).
	rng = np.random.default_rng()

	##
	c=rng.random(size=(4,4))
	# 保留三位小数(可以确保打印的时候每个元素的小数位数不超过3位)
	d=c.round(3)
	print(d)
	##
	# 一种可能的实现(存在精度表示问题,仅作为一种理论上的方法)
	p=c%0.001
	# p=c%1e*3
	d=c-p
	print(d)
	#两种方式在打印的时候都不打印结尾的0串(如果有的话)
	##矩阵转置的一种实现
	d=c.round(3)
	for i in d:
	# print(i)
	for j in i:
	print(j,end="\t")
	print()
	print("translating...","-"*10)
	l=len(d)
	for i in range(l):
	# print(i)
	for j in range(l):
	print(d[j,i],end="\t")
	print()

	0.067 0.367 0.923 0.795
	0.086 0.041 0.033 0.969
	0.572 0.868 0.353 0.908
	0.196 0.508 0.374 0.743
	translating... ----------
	0.067 0.086 0.572 0.196
	0.367 0.041 0.868 0.508
	0.923 0.033 0.353 0.374
	0.795 0.969 0.908 0.743