笔记-python-standard library-9.6 random
笔记-python-standard library-9.6 random
1. random
source code:Lib/random.py
1.1. functions for integers
random.randrange(stop)
random.randrange(start, stop[, step])
从range(start, stop, step)中返回一个随机选择的元素。注意并不会生成一个range对象。
random.randint(a,b)
return a random integer N such that a<=N <=b.alias for randrange(a, b+1)
1.2. functions for sequences
random.choice(seq)
return a random element from the non-empty sequence seq. if seq is empty, raises IndexError.
random.shuffle(x[, random]) shuffle the sequence x in place.
the optional argument random is a 0-argument function returning a random float in[0.0,1.0];by default, this is the function random().
random.sample(population,k)
return a k length list of unique elements chosen from the population sequence or set.
注意,已选择过的元素不会再次选中。
1.3. real-valued distriutions
random.random()
return the next random floating point number in the range [0.0,1.0].
random.uniform(a, b)
return a random floating point number N such that a<=N<=b for a<=b and b<=N<=a for b<a.
1.4. examples
下面是一些例子,基本可以满足常用场景。
Basic examples:
>>> 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]
Simulations:
>>> # 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
Example of statistical bootstrapping using resampling with replacement to estimate a confidence interval for the mean of a sample of size five:
# 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}')
Example of a resampling permutation test to determine the statistical significance or p-value of an observed difference between the effects of a drug versus a placebo:
# 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.')
Simulation of arrival times and service deliveries in a single server queue:
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}.')
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