-
Notifications
You must be signed in to change notification settings - Fork 0
/
CLT.py
44 lines (34 loc) · 1.09 KB
/
CLT.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
# LINK TO DOC: https://docs.google.com/document/d/1f4ljPEMx-4P9lj5icFPetLsAn30AIrGosCVnljBIbTQ/edit?usp=sharing
import random
from matplotlib import pyplot as plt
import statistics
import math
N_SAMPLES = 300 # N in the problem
N_MEANS = 10000
def getUniformRandomNumber():
return random.random()
def getExponentialRandomNumber():
return random.expovariate(2)
def getBernoulliRandomNumber():
return random.randint(0,1)
def getBetaRandomNumber():
return random.betavariate(0.5,0.5)
def getSampleMean(N):
sample = []
for i in range(N):
#sample.append(getUniformRandomNumber())
#sample.append(getExponentialRandomNumber())
#sample.append(getBernoulliRandomNumber())
sample.append(getBetaRandomNumber())
return sum(sample)/N
def printStats(l):
print("Standard Deviation: " + str(statistics.stdev(l)))
print("Variance: " + str(statistics.variance(l)))
def plotMeans(n):
means = []
for i in range(n):
means.append(getSampleMean(N_SAMPLES))
printStats(means)
plt.hist(means, bins=100)
plt.show()
plotMeans(N_MEANS)