Description
1. Introduction
Monte Carlo is a statistical method using random sampling that is used to perform an
estimation of something that is difficult or impossible to measure. This process was named
after the city of Monte Carlo in Monaco, which is known for its casinos.
We will use the Monte Carlo sampling process to estimate the area of circle, and thus the
value of π. Furthermore, we will be creating a simulation of a physical process in which
we use Monte Carlo sampling.
Say we have a square field in which there is a perfectly round pond. We know the size of
the field and the radius of the pond. What we would like to do is determine using a small
number of events what the area of the pond is. What we shall do is fire a cannon at the
field a number of times (rather, simulate the firing of a cannon — we’re not actually using
live firearms). Assuming that the cannon balls land in the field in a uniformly distributed
pattern, then we should expect some of them to hit the ground around the pond, and
some of them to splash into the pond. I have represented it graphically like this:
This shows one run of my program in which 100 samples were taken (i.e., 100 shots were
fired). The pond radius is 1, and the field width is 2 (but Matlab shows more empty space
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to the left and right of the plot). The green circles are where the cannon balls hit the
ground, and the blue stars are where they splashed into the water. It turns out that the ratio
of splashes to thuds is proportional to the area of the pond to the area of the field. The
accuracy of the measurement is determined by the number of samples taken, so the
greater the number of samples taken, the greater the accuracy is.
In a simulation like this it is not necessary to call the random events cannon shots. You
might simulate mathematicians throwing darts at a board while they’re arguing in a bar, or
a philosopher dropping grains of rice onto a table, or a nuclear physicist firing particles at
a metal plate: as long as the samples are distributed uniformly, this algorithm works
without modification. If the distribution is not uniform you can still use a Monte Carlo
process, but the math would be different.
Q: Why do we need to use sampling to determine the area of that circle? Can’t you see
that the area is precisely π?
A: Indeed, the area is π. We need to use this information to work backwards to determine
the accuracy of our program that uses Monte Carlo sampling. After we know it works, we
would be able to apply this technique to find areas of other shapes, some which may be
difficult or even impossible to compute analytically.
2. Value
This program is worth a maximum of 20 points. See the grading rubric at the end for a
breakdown of the values of different parts of this project.
3. Due Date
This project is due by 11:59PM on Sunday,
4. Objectives
The purpose of this assignment is to have you improve your skills in these areas:
• Visualizing data by plotting data on graphs.
• Random numbers.
• Conditional statements.
• Iteration (looping) statements.
Keywords: random numbers, conditional, loop, iteration, monte carlo, simulation, error
magnitude, fprintf
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5. Background
For this assignment you will need to be familiar with these topics:
• random numbers
• if statements
• loops / iteration
• fprintf function
• plotting
6. Assignment
Read through all of the subsections in this section before you start to make sure you
understand how you must proceed with the assignment. In this assignment you will create
a program that generates a plot like the one shown in the introduction. You will use the
program to run a series of simulations, collect data from them, and write a report on your
findings.
6.1. Create the simulation
In the first part of the assignment you will write a Matlab program that runs a Monte Carlo
simulation of firing cannon shots into a field that contains a circular pond.
6.1.1. Create a project folder
For this project you will be creating a Matlab script file and several functions. These should
be stored in a new folder made just for this project.
Create a new folder somewhere on your computer for homework project 4. It should be in
your CSE1010 folder. I recommend creating a list of folders like this:
The name does not need to be exactly what I show, but I do recommend naming the
project folders with some kind of number first because otherwise the folders will appear in
alphabetical order instead of chronological order. If you have not yet organized your
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folders like this, you can do it now. You can make new folders (right click on the white
background, select New Folder), you can drag and drop them, and you can rename them
(select a folder and press F2, or hit Enter, depending on your operating system).
If you are using a computer in a lab, place the new folder on your H: drive.
After you create the Monte Carlo folder, double click on it to enter that folder. Now you
are ready to add files to the project.
6.1.2. Script monteCarlo
Create a new script file in your project folder called monteCarlo.m. Copy and paste this
program outline into the file:
% Monte Carlo Area calculation
% CSE1010 Project 4, Fall 2012
% (your name goes here)
% (the current date goes here)
% TA: (your TA’s name goes here)
% Section: (your section number goes here)
% Instructor: Jeffrey A. Meunier
clc % clear command window
clear % clear all variables
clf % clear plot area
rng(‘shuffle’) % shuffle the random number generator
fieldSize = 4;
pondRadius = 1;
numShots = 100;
Be sure to change the comments in the comment block at the top of the file.
At the bottom of this script add one more statement that calls a function named setupField
and supplies one argument: the field size.
6.1.3. Function setupField
Write a function called setupField that has these statements in the body:
title(‘Shots fired into a field with a round pond’)
axis([-fieldSize/2, fieldSize/2, -fieldSize/2, fieldSize/2])
axis equal
hold on
This function does not need to return anything, but it does need a parameter variable.
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From the statements above you will be able to determine what the name of the parameter
variable should be.
Add a call to this function at the bottom of the monteCarlo script file. You need to provide
an argument to the setupField function when you call it. Can you figure out what it should
be?
Run the monteCarlo script. It should display an empty plot window that looks like this:
6.1.4. Function plotPond
Write a function called plotPond that draws a circle in the middle of the field. Refer to lab
exercise 4, in which you plotted circles on a Cartesian plane.
This function will have one parameter, which is the radius of the pond circle.
Note that you need only to draw one revolution in order to have a complete circle (or
maybe a bit more than one revolution if the ends don’t meet fully). In the lab exercise I
was having you plot many revolutions. Don’t do that here.
Add a call to this function at the bottom of the monteCarlo script file. You need to provide
an argument to the plotPond function when you call it. Can you figure out what it should
be?
Run the monteCarlo script. It should display a plot window that looks like this:
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6.1.5. Function hitPond
Write a new function called hitPond that has three parameters:
1. The pond radius.
2. The x location of the shot.
3. The y location of the shot.
Use the Pythagorean theorem to determine if the shot (which fell at location x, y) landed
within the pond (i.e., if the distance from 0, 0 to x, y is less than the pond radius). This
function returns true if the shot fell in the pond or false if it did not.
Test it:
>> hitPond(1, 1, 1)
ans =
0
>> hitPond(1, 0.5, 0.5)
ans =
1
>> hitPond(1, 0.7, 0.7)
ans =
1
>> hitPond(1, 0.7, 0.8)
ans =
0
6.1.6. Function plotShot
This function will place a visual indicator on the plot that shows in color whether a shot
landed in the pond or in the field. You may want to scroll down to see how this will look.
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This function has three parameters:
1. The pond radius.
2. The x location of the shot.
3. The y location of the shot.
The function then plots the point x, y in one of two styles depending on whether it landed
in the pond or not.
The plot function accepts a third argument that indicates the shape of the point to plot and
the point’s color. For instance this plots a blue asterisk:
plot(x, y, ‘b*’)
and this plots a green circle:
plot(x, y, ‘go’)
The statements in the function must do this: If the shot hit the pond, plot a blue star,
otherwise plot a green circle. You will need to use an if/else statement, and you will need
to call the hitPond function and the plot function.
Also, this function must return a result:
• It returns the string ‘splash’ if the shot lands in the pond.
• It returns the string ‘thud’ if the shot lands in the field.
Test it:
>> monteCarlo
>> plotShot(1, 0, 0)
ans =
splash
>> plotShot(1, 0.5, 0.5)
ans =
splash
>> plotShot(1, 1, 1)
ans =
thud
>> plotShot(1, 1.5, 1.5)
ans =
thud
Here is what the plot should look like:
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Note that I have drawn two red arrows to show where two shots fell outside the pond area
and were plotted in green. They might be difficult to see.
6.1.7. Function fireShot
The fireShot function will have two parameters: the size of the field and the radius of the
pond.
The function generates two random numbers that fall within the field (that is from −2 to
+2, but don’t use −2 and +2, use the field size variable). Use these two numbers as the x, y
point location of the shot (one number is x, the other is y). Call the plotShot function to
plot the shot. Return the value that is returned by the plotShot function call.
Test it:
>> monteCarlo
>> fireShot(4, 1)
ans =
thud
Look carefully for the point in the plot window. It may be hard to find.
After calling fireShot(4, 1) a total of 7 times, this is what my plot looks like:
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One shot landed in the pond, 6 others landed outside the pond. Your shots may be in
different locations. Note that it’s still pretty hard to see the green points outside the pond
area, but they’re there.
6.1.8. Function fireShots
This function will fire multiple shots and count how many of them land in the pond. This
function has three parameters:
1. The number of shots to fire.
2. The size of the field.
3. The radius of the pond.
This function is not long, but it incorporates several new things.
• The function must iterate, counting down from some number to 0, and stopping
when it gets to 0.
• The function must count events: every time something happens then a variable
must be incremented.
• Strings must be compared for equality.
Let’s start by iterating. Place a while loop in the function that iterates while the number of
shots to fire is greater than 0. Inside the loop, display the number of shots and then
decrement the number of shots (meaning, subtract 1 from the variable):
___ = ___ – 1;
Test the function:
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>> fireShots(5, 4, 1)
5
4
3
2
1
Yours may look a little different, but as long as it counts from 5 to 1 then it is correct so far.
After you determine that it works, remove the statement that displays the number.
Inside the loop do this: call the fireShot function using the field size and pond radius as
arguments. Examine the result of this function call. If the result is a string that’s equal to
‘splash’, then increment the number of splashes that have been counted so far.
Be careful: you must still decrement the number of shots, but make sure that it happens
whether the shot hit the pond or the field.
In order to compare two strings for equality, you should not use the double equal operator:
that will treat the two strings as vectors, which will work only if the two strings have
exactly the same length. This is what would happen:
>> ‘splash’ == ‘splash’
ans =
1 1 1 1 1 1
A vector of all 1s is acceptable as a logical value, however:
>> ‘splash’ == ‘thud’
Error using ==
Matrix dimensions must agree.
The solution is to use the strcmp function built into Matlab. Use Matlab’s built-in help to
learn how to use the strcmp function.
Test it:
>> monteCarlo
>> fireShots(100, 4, 2)
ans =
19
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Thus, out of 100 shots, 19 have landed in the pond.
Add a statement to the bottom of the monteCarlo script so that the fireShots function is
called automatically. Replace the numbers 100, 4, and 2 with the appropriate variables
that you defined in the monteCarlo script. Now when you type monteCarlo at the
command prompt, the plot is drawn and 100 shots are fired. Also, the number of shots
landing in the pond is returned and displayed. Change that so the return value is stored in
a variable instead, and display two message like this:
Number of shots = 100
Number of splashes = 19
Format each message on one line. To do this you will need to use two fprintf statements
(although it’s possible to do it using one fprintf statement). Use the %g format specifier for
each number, and also use the \n format command. See pages 42 and 43 in the book for
information about using the fprintf statement, or use Matlab’s built-in help.
6.1.9. Function estimatePondArea
Write a function called estimatePondArea that has three parameters:
1. The number of splashes.
2. The total number of shots.
3. The size of the field.
The estimated area of the pond is the ratio of splashes to shots multiplied by the field area.
Note that the field size is not the same as its area. The field is square.
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Return the estimated area from this function.
Test it:
>> estimatePondArea(19, 100, 4)
ans =
3.0400
Call this function from the monteCarlo script. Store the result in a variable and display a
message like this:
Estimated pond area = 3.04
6.1.10. Function estimatePi
Write a function called estimatePi that has two parameters:
1. The estimated pond area.
2. The actual pond radius.
Note that we can estimate the area of a shape just by firing shots into it, but we can’t
calculate the estimated value of pi without knowing the pond’s actual radius. If this were a
real-life experiment with a real pond in a real field, we would have to be sure that the
pond was perfectly round, and we would need to measure the pond’s size accurately.
The estimated value of pi is the estimated pond area divided by the square of the actual
pond radius. Return the estimated value of pi from this function.
Test it:
>> estimatePi(3.04, 1)
ans =
3.0400
Call this function from the monteCarlo script. Store the result in a variable and display a
message like this:
Estimated value of pi = 3.04
6.1.11. Function piErrorPercent
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Write a function called piErrorPercent. This function has one parameter: the estimated
value of pi.
Inside this function calculate the error magnitude by taking the absolute value of the
difference between the estimated value of pi and the actual value of pi. Use the abs
function.
Then calculate the error percentage by dividing the error magnitude by the actual value of
pi and multiplying by 100.
Return the error percentage.
Test it:
>> piErrorPercent(3.04)
ans =
3.2338
Thus, the number 3.04 differs from Matlab’s value of pi by about 3¼ percent.
Call this function from the monteCarlo script. Store the result in a variable and display a
message like this:
Error in estimation of pi = 3.23379%
Make sure the percent sign is shown after the number. To get the percent sign to be
displayed, just place a double percent inside the string that you use in the fprintf
statement: ‘… %g%%\n’
6.1.12. Get user input
Modify the monteCarlo script file so that instead of assigning constant scalars to the
three variables, like this:
fieldSize = 4;
pondRadius = 1;
numShots = 100;
you call the input function once for each variable, like this:
fieldSize = input(‘Enter the size of the field: ‘);
This allows you to change the characteristics of the experiment each time you run the
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program.
Enter the size of the field: 4
Enter the radius of the pond: 1
Enter the number of shots: 100
Number of shots = 100
Number of splashes = 19
Estimated pond area = 3.04
Estimated value of pi = 3.04%
Enter the size of the field: 90
Enter the radius of the pond: 20
Enter the number of shots: 350
Number of shots = 350
Number of splashes = 64
Estimated pond area = 1481.14
Estimated value of pi = 3.70286%
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6.1.13. Comment the functions
Add comment lines inside each function that you wrote. There are a few examples of this
on page 87. Use at least two comment lines:
1. A brief description of what the function does.
2. Show how to call the function (you can just copy & paste the function definition
without the word function).
Here is what the comments look like in my setupField function:
function setupField(fieldSize)
% Sets up the plot window for the simulation.
% Use: setupField(fieldSize)
6.2. Run the simulation
In the second part of the assignment you will perform some experiments by running the
simulation, collecting data, and creating graphical visualizations of the data.
6.2.1. Create spreadsheet data
You have just created a very useful simulation. Now it is time to use the simulation to run
a few experiments (this is the science part of Computer Science and Engineering). What
you will do is run the program a number of times and collect the data into a spreadsheet.
Start Excel or some other spreadsheet program and enter these row and column headings
(make the headings bold):
Run 1 Run 2 Run 3 Run 4 Run 5 mean log(mean)
1
10
100
1000
10000
100000
Run your program 5 times, each time with 1 shot. Use field size 4 and pond radius 1.
Enter only the error percentage in each cell in the row labeled 1. You’ll fill in the mean
and log(mean) columns afterward. My row looks like this, your numbers might be
different:
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Run 1 Run 2 Run 3 Run 4 Run 5
1 409.2958 100 100 100 100
Run the program 5 more times, each time with 10 shots. Enter the error percentages in the
row labeled 10.
Do it 5 times with 100 shots, and 5 times with 1000 shots. For 10000 and 100000 shots
the simulation becomes very slow. The problem is not so much that there are too many
iterations, but that you are asking Matlab to plot those points. The loop would run very
quickly without all the plotting. Since all you need is the number at the end and not the
plot, you can comment out the part of the program that makes it slow. Find the two plot
statements inside the plotShot function and comment them out:
%plot(x, y, ‘b*’)
%plot(x, y, ‘go’)
When you run the program the plot window will still appear, but the shots will not be
drawn. As a result the program will run quickly.
6.2.2. Complete spreadsheet
Now it’s time to enter the formulas for the mean and log(mean) columns.
In cell G2 enter the formula =AVERAGE(B2:F2)
Make sure you type the equal sign in that formula.
In cell G3 enter the formula =AVERAGE(B3:F3)
Do the same for G4, G5, G6, and G7. Make sure you change the range appropriately for
each entry: B4:F4, B5:F5, and so on.
In cell H2 enter the formula =LOG(G2)
In cell H3 enter the formula =LOG(G3)
Do the same for H4, H5, H6, and H7.
6.2.3. Plot log(mean)
Next you will create a plot in Matlab of the log(mean) column. Here’s how you do it using
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copy & paste:
1. In the Excel spreadsheet, highlight the 6 numbers in the log(mean) column and
press Ctrl-C (or select Copy in the ribbon strip under the Home tab) to copy the
numbers to the clipboard in Windows (use Cmd-C on the Mac).
2. Click once in the Matlab workspace window in the upper right and type Ctrl-N
(Cmd-N on Mac) to create a new variable. Call it logMean.
3. Double click on the logMean variable to open the variable editor.
4. Press Ctrl-V (Cmd-V) to paste the numbers copied from Excel.
5. Close the variable editor window.
This creates a column vector in the logMean variable. Now go to the command window
and type plot(logMean). Save this plot (use File → Save as) because later you will insert it
into the report document.
The fact that this plot is largely linear shows that the error in the calculation in pi depends
linearly on the number of shots fired. For instance, doubling the number of shots doubles
the accuracy.
7. Report
Create a Microsoft Word or OpenOffice Word file (or some other format that your TA
agrees on — ask him or her if you are not sure). Save the file with the name Project2 with
a .doc or .docx format.
At the beginning of the document include this information:
Monte Carlo Simulation
CSE1010 Project 4, Fall 2012
Your name goes here
The current date goes here
TA: Your TA’s name goes here
Section: Your section number goes here
Instructor: Jeffrey A. Meunier
Be sure to replace the parts that are underlined above.
Now create the following five sections in your document.
1. Introduction
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In this section copy & paste the text from the introduction section of this assignment. (It’s
not plagiarism if you have permission to copy something. I give you permission.)
2. Test runs
Run the program 3 times. For all three runs use field size 4, pond radius 1, and for the
number of shots use 10, then 100, then 1000. For each test run, copy & paste the output
from the command window into the document, and save the plot and insert it into the
document. Label each test run & plot, something like this:
Test run of field size 4, pond radius 1, 10 shots
Enter the size of the field: 4
Enter the radius of the pond: 1
Enter the number of shots: 10
etc.
3. Plot of log(mean)
Insert the log(mean) plot that you created in section 6.2.3. Write a brief description of
what the plot is and what it means. Why is the plot linear? Why does the plot go in the
direction it goes?
4. Spreadsheet data
Copy and paste the spreadsheet data into this section, or insert a cropped screen shot of
your spreadsheet. Add a sentence or two describing what the values in the spreadsheet
are.
5. Source code
Copy & paste the contents of your monteCarlo.m file, then after it paste each function that
you wrote. You don’t need to write anything with the functions this time because the
functions already contain comments.
8. Submission
Submit the following two things things on HuskyCT:
1. All the .m files for this project stored in a Zip file. Do not upload each separate .m
file on HuskyCT. I have made tutorial videos available on HuskyCT describing how
to make a Zip file. These are the files you must include in the Zip file:
⁃ estimatePi.m
⁃ estimatePondArea.m
⁃ fireShot.m
⁃ fireShots.m
⁃ hitPond.m
⁃ monteCarlo.m
⁃ piErrorPercent.m
⁃ plotPond.m
⁃ plotShot.m
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⁃ randomShot.m
⁃ setupField.m
2. The MS Word document.
If for some reason you are not able to submit your files on HuskyCT, email your TA before
the deadline. Attach your files to the email.
9. Notes
Here are some notes about working on this project:
10. Grading Rubric
Your TA will grade your assignment based on these criteria:
• (1 point) The comment block at the beginning of the monteCarlo.m file is correct.
• (10 points) The program displays the correct answers and calculates them in the
correct way.
• (4 points) The program is formatted neatly. Follow any example in the book, or
see the web site here: https://sites.google.com/site/jeffscourses/cse1010/matlab-programformatting
• (5 points) The document contains all the correct information and is formatted
neatly, and you have used correct grammar and spelling where appropriate (this will be
mostly in your program comments: you don’t need to write full sentences, but make sure
that what you write is otherwise correct). Non-native speakers of English will be granted a
bit of leniency on grammar.
11. Getting help
Start your project early, because you will probably not be able to get help in the last few
hours before the project is due.
• If you need help, send e-mail to your TA immediately.
• Go to the office hours for (preferably) your TA or any other TA. I suggest you seeing
your own TA first because your TA will know you better. However, don’t let that
stop you from seeing any TA for help.
• Send e-mail to Jeff.
• Go to Jeff’s office hours.