python随机数学习笔记

#coding:utf-8
import  random

# random.randint(1,10)产生1，10的随机整数
for i in range(1,5):
    ranint = random.randint(1,10)
    print(ranint, end=" ")
print()

#random.random()产生0，1之间的随机数
for j in range(1,5):
    ran_1 = random.random()
    print(ran_1,end=" ")
print()
#random.uniform(10,20)产生指定区间的随机符点数
for a in range(1,5):
    ran_2 = random.uniform(10,20)
    print(ran_2,end=" ")
print()

#random.randrange(10,20,2)在指定区间上以特定步长产生随机数
for b in range(1,5):
    ran_3 = random.randrange(10,20,2)
    print(ran_3,end=" ")
print()
for c in range(1,5):
    ran_4 = random.choice(range(10,20,2))
    print(ran_4,end=" ")
print()

#random.choice从序列中获取一个随机元素
ran_5 = random.choice(['a','b','c','d'])
print(ran_5)

#random.shuffle(x[, random])，用于将一个列表中的元素打乱
p = ['I','love','you','and','do','you','hate','me']
ran_6 = random.shuffle(p)
print(p)

#random.sample(sequence, k)，从指定序列中随机获取指定长度的片断。sample函数不会修改原有序列。

list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
slice = random.sample(list, 5)  #从list中随机获取5个元素，作为一个片断返回
print(slice)
print(list)  #原有序列并没有改变。

#random.sample(population, k)产生指定序列的指定长度的随机数,可利用此方法产生10位随机密码
#列表转字符串方法：“”.join(list)
#字符串转列表方法：list(str)
ran_7 = random.sample('abcdefghijklmnopqrstuvwxyz1234567890!@#$%^&*():"?><',10)
print("".join(ran_7))

#random.triangular(low, high, mode)
# random.triangular（低，高，模式）
# 返回一个随机浮点数N，以便在这些边界之间使用指定的模式。该低和高界默认的0和1。
# 所述模式参数默认为边界之间的中点，给人一种对称分布。low <= N <= high
ran_8 = random.triangular(10,20)
print(ran_8)

# random.betavariate（alpha，beta ）
# Beta分发。参数的条件是和 。返回值的范围介于0和1之间。alpha > 0beta > 0
ran_9 = random.betavariate(2,9)
print(ran_9)

# random.expovariate（lambd ）
# 指数分布。 lambd是1.0除以所需的平均值。它应该是非零的。
# （该参数将被称为“拉姆达”，但是这是在Python保留字。）
# 返回值的范围从0到正无穷大如果lambd为正，且从负无穷大到0，如果lambd为负。
for i in [0.01,0.2,1,33,-0.02,-0.99,-22,-88]:
    ran_10 = random.expovariate(i)
    print(ran_10,end=" ")

# random.gammavariate（alpha，beta ）
# Gamma分布。（不是伽玛函数！）参数的条件是和。alpha > 0beta > 0
#
# 概率分布函数是：
#
#           x ** (alpha - 1) * math.exp(-x / beta)
# pdf(x) =  --------------------------------------
#             math.gamma(alpha) * beta ** alpha
# random.gauss（mu，sigma ）
# 高斯分布。 mu是平均值，sigma是标准偏差。这比normalvariate()下面定义的函数稍快。
#
# random.lognormvariate（mu，sigma ）
# 记录正态分布。如果你采用这个分布的自然对数，你将获得具有平均μ和标准偏差西格玛的正态分布。 mu可以有任何值，sigma必须大于零。
#
# random.normalvariate（mu，sigma ）
# 正态分布。 mu是平均值，sigma是标准偏差。
#
# random.vonmisesvariate（mu，kappa ）
# mu是平均角度，以弧度表示，介于0和2 * pi之间，kappa 是浓度参数，必须大于或等于零。如果 kappa等于零，则该分布在0到2 * pi的范围内减小到均匀的随机角度。
#
# random.paretovariate（alpha ）
# 帕累托分布。 alpha是形状参数。
#
# random.weibullvariate（alpha，beta ）
# 威布尔分布。 alpha是scale参数，beta是shape参数。

# 基本示例：
#
# >>>
# >>> random()                             # Random float:  0.0 <= x < 1.0
# 0.37444887175646646
#
# >>> uniform(2.5, 10.0)                   # Random float:  2.5 <= x < 10.0
# 3.1800146073117523
#
# >>> expovariate(1 / 5)                   # Interval between arrivals averaging 5 seconds
# 5.148957571865031
#
# >>> randrange(10)                        # Integer from 0 to 9 inclusive
# 7
#
# >>> randrange(0, 101, 2)                 # Even integer from 0 to 100 inclusive
# 26
#
# >>> choice(['win', 'lose', 'draw'])      # Single random element from a sequence
# 'draw'
#
# >>> deck = 'ace two three four'.split()
# >>> shuffle(deck)                        # Shuffle a list
# >>> deck
# ['four', 'two', 'ace', 'three']
#
# >>> sample([10, 20, 30, 40, 50], k=4)    # Four samples without replacement
# [40, 10, 50, 30]
# 模拟：
#
# >>>
# >>> # Six roulette wheel spins (weighted sampling with replacement)
# >>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
# ['red', 'green', 'black', 'black', 'red', 'black']
#
# >>> # Deal 20 cards without replacement from a deck of 52 playing cards
# >>> # and determine the proportion of cards with a ten-value
# >>> # (a ten, jack, queen, or king).
# >>> deck = collections.Counter(tens=16, low_cards=36)
# >>> seen = sample(list(deck.elements()), k=20)
# >>> seen.count('tens') / 20
# 0.15
#
# >>> # Estimate the probability of getting 5 or more heads from 7 spins
# >>> # of a biased coin that settles on heads 60% of the time.
# >>> trial = lambda: choices('HT', cum_weights=(0.60, 1.00), k=7).count('H') >= 5
# >>> sum(trial() for i in range(10000)) / 10000
# 0.4169
#
# >>> # Probability of the median of 5 samples being in middle two quartiles
# >>> trial = lambda : 2500 <= sorted(choices(range(10000), k=5))[2]  < 7500
# >>> sum(trial() for i in range(10000)) / 10000
# 0.7958
# 使用重新取样和替换来估计大小为5的样本的均值的置信区间的统计自举的示例：
#
# # http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm
# from statistics import mean
# from random import choices
#
# data = 1, 2, 4, 4, 10
# means = sorted(mean(choices(data, k=5)) for i in range(20))
# print(f'The sample mean of {mean(data):.1f} has a 90% confidence '
#       f'interval from {means[1]:.1f} to {means[-2]:.1f}')
# 重新采样置换测试的示例， 以确定药物与安慰剂的效果之间观察到的差异的统计显着性或p值：
#
# # Example from "Statistics is Easy" by Dennis Shasha and Manda Wilson
# from statistics import mean
# from random import shuffle
#
# drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
# placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
# observed_diff = mean(drug) - mean(placebo)
#
# n = 10000
# count = 0
# combined = drug + placebo
# for i in range(n):
#     shuffle(combined)
#     new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):])
#     count += (new_diff >= observed_diff)
#
# print(f'{n} label reshufflings produced only {count} instances with a difference')
# print(f'at least as extreme as the observed difference of {observed_diff:.1f}.')
# print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null')
# print(f'hypothesis that there is no difference between the drug and the placebo.')
# 模拟单个服务器队列中的到达时间和服务交付：
#
# from random import expovariate, gauss
# from statistics import mean, median, stdev
#
# average_arrival_interval = 5.6
# average_service_time = 5.0
# stdev_service_time = 0.5
#
# num_waiting = 0
# arrivals = []
# starts = []
# arrival = service_end = 0.0
# for i in range(20000):
#     if arrival <= service_end:
#         num_waiting += 1
#         arrival += expovariate(1.0 / average_arrival_interval)
#         arrivals.append(arrival)
#     else:
#         num_waiting -= 1
#         service_start = service_end if num_waiting else arrival
#         service_time = gauss(average_service_time, stdev_service_time)
#         service_end = service_start + service_time
#         starts.append(service_start)
#
# waits = [start - arrival for arrival, start in zip(arrivals, starts)]
# print(f'Mean wait: {mean(waits):.1f}.  Stdev wait: {stdev(waits):.1f}.')
# print(f'Median wait: {median(waits):.1f}.  Max wait: {max(waits):.1f}.')
# 也可以看看 “黑客统计” 是Jake Vanderplas 关于统计分析的视频教程， 仅使用了一些基本概念，包括模拟，抽样，改组和交叉验证。
# 经济模拟Peter Norvig对 市场的模拟 ，显示了该模块提供的许多工具和分布的有效使用（高斯，均匀，样本，beta变量，选择，三角和randrange）。
#
# 具体的概率介绍（使用Python）Peter Norvig 的教程，涵盖了概率论的基础知识，如何编写模拟，以及如何使用Python进行数据分析。