Description
The objective of this project is to first understand the Python “histogram” function and
duplicate the results of the example of generating a histogram (“stem” plot as shown) of
the probability of the sum of two dice for a user definable number of rolls. The second
objective is to submit the four problems at the end of this handout.
First consider the following two programs, which determining the number of heads and
tails after flipping a coin 10000 times. Both programs provide the same results, but they
differ in the way the models are coded.
• The first model is programmed in Python using “for loops”.
• The second model makes use of the arrays, and it is computationally more
efficient.
MODEL 1 MODEL 2
import random
total_flips=10000
heads=0
tails=0
for i in range(total_flips):
coin=round(random.random())
if coin==0:
heads=heads+1
else:
tails=tails+1
print(“number of heads: ” ,heads, “.”)
print(“number of tails: ” ,tails, “.”)
import numpy as np
import random
total_flips=10000
heads=np.zeros((total_flips,1))
for i in range(total_flips):
heads[i,:]=round(random.random())
print(“number of heads: “,sum(sum(heads)))
print(“number of tails: “,sum(total_flips-sum(heads)))
Next, experiment with the “histogram” function by making modifications to the simple
program listing below; noting the different outcomes when defining the bin limits
differently.
import matplotlib.pyplot as plt
import numpy as np
import random
N = 50000
x=np.zeros((N,1))
for i in range(N):
x[i,:] = random.random()
bins = np.arange(-1, 1, 0.1)
#or bins = np.arange(-0.95, 0.95, 0.1)
plt.hist(x, bins)
plt.show()
Now, let experiment the roll of a pair of fair dice. This experiment models the roll of a pair of dice
for N times. The sum of each roll is recorded, and stored in vector “s”. The probability of each
possible outcome is calculated and plotted in a “probability Mass Function” (PMF) plot. To create
the plots, the simulation has been run for N=100000 times.
import numpy as np
import matplotlib.pyplot as plt
import random
#
N=100000
d1=np.zeros((N, 1))
d2=np.zeros((N, 1))
for i in range(N):
d1[i,:]=random.randint(1, 6)
for i in range(N):
d2[i,:]=random.randint(1, 6)
s=d1+d2
b=range(1,15) ; sb=np.size(b)
h1, bin_edges = np.histogram(s,bins=b)
b1=bin_edges[0:sb-1]
#
fig1=plt.figure(1)
plt.stem(b1,h1)
plt.title(‘Stem plot – Sum of two dice’)
plt.xlabel(‘Sum of two dice’)
plt.ylabel(‘Number of occurrences’)
fig1.savefig(‘Sum of two dice.jpg’)
#
fig2=plt.figure(2)
p1=h1/N
plt.stem(b1,p1)
plt.title(‘Stem plot – Sum of two dice: Probability mass function’)
plt.xlabel(‘Sum of two dice’)
plt.ylabel(‘Probability’)
fig2.savefig(‘PMF of sum of two dice.jpg’)
Finally, turn in the required graphical or numerical results for simulation problems
1) through 4).
1) Generate a probability histogram of the number of rolls required of two dice
before a sum of “7” appears (graphical answer followed by Python code). Note:
Don’t plot experiment that took more than 60 rolls.
2) Generate an unfair six-sided die. The die has sixe sides [1, 2, 3, 4, 5, 6] with
probabilities: [𝑝1, 𝑝2, 𝑝3, 𝑝4, 𝑝5, 𝑝6
] = [0.1, 0.15. 0.3, 0.25, 0.05, 0.15]. Simulating
the roll of the die for N = 10,000 times, and plot the PMF of your unfair die as
stem plot.
The stem plot should verify that the six sides of your unfair die follow the
required probabilities.
3) When 100 coins are tossed find the probability that exactly 35 will be heads
(numerical answer followed by Python code), assuming the number of
experiments is 100000.
4) Determine the probability of “4 of a kind” in a 6-card poker draw (numerical
answer followed by Python code).
