2018 Winter Data Analytics Competition – MCM

I took a brief break from working on side projects to participate in the MCM (Mathematical Contest in Modeling) competition. I decided to compete in this competition mainly after enjoying and seeing success in the previous semester’s GDMS data analytics competition (post linked here) in which my team received an honorable mention in the beginner’s division. Again, my team consisted of friends Kali Liang (CMDA major) and Eric Fu (BIT major). Looking back on both of these competitions, I would definitely say the diversity in majors really helped in our ability to come together and perform, mainly due to our differences in perspective when it came to looking at the problem as a whole. Similar to the previous competition, we were given a .csv file with about 600 categories involving energy consumption and production rates per year in Arizona, California, New Mexico, and Texas, and were asked to come up with energy profiles for each state and to predict energy consumption and production in 2025 and 2050, all in the context of using cleaner energy sources. For reference, we tackled problem C (all problems linked here).

We used a mixture of VBA (Excel), Python, and R to analyze the given data. My main job was to determine which subsets of data were interesting and useful and to plot them using Python/matplotlib.

Moving on to the actual data analysis, we started by creating energy profiles for each of the four given states. We split our profiles into 3 graphs we deemed relevant, including overall energy consumption, overall energy production, and energy usage by sector in each state. The main hurdle for this section was deciding how to cut down the data to where each graph wouldn’t be too cluttered but the data would still be accurate enough to use. For instance, for energy consumption alone, each state’s initial graph had over 20 sub-plots, making the graph hard to read at first glance. This was due to the inclusion of ultimately insignificant values (for instance wood and waste energy consumption). As a result, we cut these insignificant sub-sets, facilitating visual analysis on each graph. The downside to this was not having a fully accurate representation of total consumption and production in each state, but we decided the sacrifice was worth it for the visual clarity provided. az_profile_cons.png
Energy consumption graph for Arizona. You can see that as the Y values approach 0, the higher the concentration of plot points (and this is after cutting more than 10 sub-sets).

Next, we moved onto attempting to define a more accurate illustration of the data set in regards to the context of the problem, renewable energy usage. We decided the best way to do this was to put everything in terms of the estimated amount of pollution each state was producing per year. We determined this value for each state per year by calculating the summation of each states energy consumption value multiplied by the transfer rate between that specific energy type and CO2 emissions. Essentially, we arranged Jet Fuel, Natural Gas, Coal, Petroleum, Motor Gasoline, and LPG consumptions per year in a single-column matrix and found its dot product with a constant single-column matrix that consisted of the conversion coefficients for that specific energy type and CO2 emissions, labeled C in the graphic below. We determined the conversion coefficients by pulling data from the EIA (here).

Screen Shot 2018-02-19 at 2.08.00 PM.png

Screen Shot 2018-02-19 at 2.09.34 PM.pngExcerpt from our final paper detailing the values of the C conversion constant and giving Arizona’s numbers for 2009.

Using the information above (Note: the structure of X_AZ follows that of C (in order of energy type from top to bottom, given in billion BTU. A conversion factor of 1000 Mill BTU / 1 Bill BTU was added) we are able to calculate an estimation of the CO2 emissions in Arizona in 2009 (X_AZ_2009 * C = 93307874.83 metric tons of CO2. Given the population of Arizona in 2009, we can then determine an estimation of CO2 emissions per capita of Arizona in 2009 (abt. 14 metric tons of C02 per capita). We then generalized this process in order to create a graph of CO2 emissions in each state per year.

Graph detailing CO2 emissions per capita per year in each of the states. We used this information to generally rank each state in terms of pollution production, in order to circle back and create a discussion surrounding using cleaner energy sources.

Next came predicting energy consumption/production in 2025 in the context of cleaner energy usage. For this, as we began to run out of time (we only had four days for this competition) we resorted to using a basic linear regression to predict the values for 2025 and 2050. The main notable conclusion from this experiment was seeing how high energy consumption values were predicted to become, especially in regards to the very rapidly increasing CO2 emissions per capita for each state. As an example, total energy consumption in Arizona, using the linear regression model, was projected to increase by over 800,000 BTU. This, paired with the estimated increase in CO2 emissions per capita of abt. 5 metric tons per capita shows just how quickly pollution is projected to get out of control. Of the four states, Texas had the worst CO2 emission rates per capita estimated by 2050, seeing an increase of 37 metric tons per capita from 2009 (a projected value of abt 76 metric tons per capita). Again, we used these values to stress the importance of switching to more renewable energy sources in the coming years, before damage becomes irreversible.

Using all of the above data, we discussed in our conclusion the importance of moving away from commonly-used, bad for the environment energy sources. With the exponential increase in population in all of these states and really throughout the world, energy consumption and production has understandably seen a drastic increase in recent years. However, with the threat of global warming, each state needs to do whatever it can in order to slow or even reverse the effects brought on by these increased consumption and production rates.

I’m incredibly thankful to have competed in the GDMS competition prior to this competition, because without prior experience, we definitely would have had no idea where to start and probably would not have finished on time (of which we only barely did given the work detailed above, after making a numerous amount of assumptions ultimately weakening our argument). All in all, this competition was equally taxing on our mental health as it was fun to compete in. We look forward to participating in future data analysis competitions in the future. Unfortunately, we don’t hear back about the results until some time in April. If you want to see our final paper (written in LaTeX) in detail, you can find it here.


2018 Winter AI Project – Connect 5 Minimax

After finishing my previous project, I still found myself with plenty of time in-between classes (thankfully I’m only taking 4 courses this semester as opposed to my usual 6, so hopefully this trend continues throughout the semester). Also, as I recently took up a UTA position for CS2505 at VT (Intro to Comp Org), I found myself with plenty of down time on-shift as the early assignments are easy and most students don’t need help as of yet. Thankfully, some friends come and spend time with me to make the time go by faster. One of these friends typically brings a Go board along with her and loves to play connect 5 with us (think tic-tac-toe, but on a Go board, and you have to connect 5 instead of 3). After countless games, I thought back to my minimax algorithm for tic-tac-toe (here), and wondered if it applied smoothly to connect 5. And so, I decided to start working on this project.

First was to create the game. I was drawn to Java because I’m most comfortable working with object-heavy projects in Java (as opposed to something like Python). Coding the basic game was simple. The board itself is basically an 18×18 tic-tac-toe board. As such, I just used a 2D integer array size 18×18 and used constants for black, white, and empty spaces. I used swing for rendering, and KeyListeners for user input. Arrow keys navigate and enter places a piece. Upon placing a piece, control is handed over to the AI to decide the move they are going to take.

Screen Shot 2018-02-05 at 2.24.53 PM.png
Empty gameboard. The cursor is indicated with the red square (moves with arrow key input). As you’ll see later, the blue cursor (not shown here) indicates the previous move.

Next came calculating the heuristics for scoring any given board. I used this post by Ofek Gila as a foundation and built on that. Essentially, the board is traversed in every direction (horizontal, vertical, topleft->bottomright diagonal, topright->bottomleft diagonal) and consecutive pieces are counted and scored, with the sum total being the overall score of the current board state. The scores for consecutive pieces are based on the perceived value of that shape (i.e. a 5 in a row is worth much more than a single piece). I wanted this version to play very defensively (an annoying strategy for veterans of the game), so there are noticeable score differences based on what the color of the consecutive pieces is. Afterward, I began writing the minimax algorithm. The basic jist of minimax is to look a set number of moves into the future, score the current board state, and play off of the assumption that your opponent is going to make the best move for them at their given board state (hence the name minimax — you’re trying to maximize your gains and the opponent is trying to minimize them). I settled on looking 2 moves into the future, as it fit the balance of running quickly and decent decision making. If you want to learn more about minimax, feel free to look at the code for this project (link at the bottom).

Screen Shot 2018-02-05 at 2.23.14 PM.png
Using minimax, the AI (white) was able to defeat the user (black) here, while also stopping a win for black (if the white piece at the upper-right was not placed, black would have won)

Using this method, I ran into a couple of speed bumps. The major issue was performance. Especially at the beginning of the game, even after the user’s move, there are still 323 possible moves (18^2=324, -1 for the placed piece). That in addition to looking n moves into the future, meant that there are about 323^n checks that the algorithm needs to make (the number is actually slightly less than 323^n, as it doesn’t factor in placing new pieces and repeating the process, but it is essentially that value for small values of n, so we will ignore it for this basic analysis). To solve this, I cropped the board down to only ‘relevant’ spaces (I defined relevant here as any space around all of the current pieces in play with a buffer of 2 spaces on every side). Using this method, I was able to reduce the number of checks for 2 moves from 323^2=104,329 to 25^2=625 (placing one piece and creating a buffer of size 2 on every side creates a 5×5 grid, resulting in the 25 seen there (5^2 = 25)). Other than that, there is a bug in which the AI would let the player win if it won later down the decision-tree (in other words, it detected a win but didn’t take into account the opponent winning sooner). I expect this to be an error with scoring, but as I want to continue working on other projects, I will leave this bug in for now.

Screen Shot 2018-02-05 at 2.21.13 PM.png
You can see that the AI (white) detected a win, so it ignored black’s 3 in a row, ensuring a win for black.

Testing this out on some of my friends, I found that it played fairly decently. However, due to some minor bugs in the score evaluation (detailed at the end of the previous paragraph), veterans could consistently beat the bot (albeit after hard, long-fought games). This project was a fun one that I could share with friends, so I definitely enjoyed working on it and seeing it get better along the way. I encourage you to download the project and try playing against it yourself. You can find the github for this project here.

2018 Winter Web Project – Pyreddit

During the start of my spring semester, I found myself with a lot of free time while waiting for classes to pick up in difficulty. I thought of side projects to do to fill up this time, and decided to focus on something web-based, as the last time I did anything web related was in highschool. Eventually, I ended up finding projects like rtv, of which inspired me to create something of a reddit browser from terminal. Upon doing a bit of research, I found that there exists a nice, easy to use library that handles connecting to reddit in python called praw. I mainly focused on browsing, not submitting jobs (like upvoting or writing a post), simply because I focused on my own reddit-using habits. I mainly read posts, and rarely (if ever) make them, so I decided to cut that feature from my program. Perhaps if I come back to this in the future, I will implement those features.

Thanks to praw, connecting to reddit was very easy. Given some credentials (along with authorization codes for a script to connect to reddit), I was able to connect in essentially just one line. Praw allows easy movement within reddit as well, whether it be connecting to different subreddits or posts within a subreddit, its all very easy and straightforward. If you’re considering making any script or application that will connect to reddit, I strongly recommend praw. View connection_manager.py (github linked below) to see for yourself how easy everything really was.

Screen Shot 2018-01-23 at 3.00.49 PM.png
All of the setup for pyreddit occurs within less than a second, and is easy to implement from a developer’s point of view.

Other than establishing a connection, all that was left was figuring out how I wanted to do the user interface. I used the colorama library to color text, and used reddit’s basic layout as inspiration for the various aspects that needed a ui. First off was subreddits. Praw loads in an iterator for a large number of posts from a given subreddit and tab (hot, top, new, etc.) so all I needed to do was separate those into pages.

Screen Shot 2018-01-23 at 3.03.40 PM.png
Cutout of a page in pyreddit. I tried to make each bit of information color-coded to ease user understanding. If the user wants to open one of these posts, they simply have to type ‘open i‘, where i is the index of the post (shown in red in the screen).

Next was creating an interface for posts. I created a simple method for breaking text bodies into chunks that were easy to read (split the post body at the space after 100 characters from the previous split) and created comment trees similar to that of python using depth-first search. All of which resulted in a simple, clean style that doesn’t look too bad. I used the webbrowser library (standard in python) to open the current post’s url if so desired as well.

Screen Shot 2018-01-23 at 3.10.37 PM.png
Example of a post shown within pyreddit. If the post itself contains a body, it is printed in the same fashion (split every 100 characters at a space, etc.)

This was a quick and easy project that helped me further my understanding in python scripting and establishing web connections (esp. to reddit). If you want to download this program or look at the code, you can find the github for this project here.

2017 Fall Cluster Project – Raspberry Pi 3 Cluster

After using VT’s cluster for so long, I was interested to learn how the computing behind it worked, and decided to make my own cluster of Raspberry Pis. I contemplated the benefits of embarking on such a project and came up with a multitude of experiments I was interested in exploring that would benefit a cluster-like structure, of which include password decryption, simulation for genetic algorithms, and more. What really tipped me over, however, was the already written and free to use libraries MPI and MPI4PY, of which allowed me to use python to write easy to understand and easy to implement code that utilized cluster computing. Thankfully I had a lot of the components on hand already, but I estimate the overall cost to be around $270. This includes the cost of 4 Raspberry Pis, 4 8gb micro SD cards, a stand for the pis, an ethernet switch, 5 ethernet cords, a USB hub, and 4 micro USB – USB cords. There’s a great video series by Tinkernut on Youtube that I used for basic reference when configuring my Pis that I’d certainly recommend you follow along as well if you have any difficulties putting one of these together. If you have any questions about any of the components I used to build my cluster, feel free to email me at brendan.mccloskey@gmail.com.

For my cluster, I used 4 Raspberry Pi 3 B boards, each running a distribution of CentOS 7 Linux. I decided to use CentOS 7 over a friendlier version of linux (i.e. Raspbian, a distribution written specifically for the Pi), simply because VT’s cluster also uses CentOS. Each Pi is given power and connected to an Ethernet switch that is also connected to my router so I can ssh into each of the nodes.

Picture of my cluster

I have the Pis set up in a master-slave system, in which the head node takes one big problem and divides it into smaller problems for the slave nodes to compute. This allows me to, say, run genetic algorithm/ANN simulations on the slave nodes while performing breeding and fitness evaluation on the master node. I definitely plan on experimenting with speedups in regards to AI in the future, so be on the lookout.

After building my cluster, I found it fitting to at least write a short piece of test code to see how much the cluster structure sped things up. I decided to time Leibniz’s Pi approximation to the 100,000th term on a single worker vs. all three workers. You can see the results of that experiment below.

Screen Shot 2017-11-22 at 9.04.51 PM
Testing 1 vs. 3 workers

You can see that the test in which I only used one worker (nodelist_small), the runtime was approximately 3 times as large as the runtime when I used 3 workers (nodelist), which is to be expected. You can also see that the estimates are slightly different, though I believe this error to be a result of integer division losing some information when divvying up the work for slave nodes (As this is just a small test, I’m leaving this error for now). You can get the code for this test here.

This was an incredibly fun and practical project to work on during my Thanksgiving break. Definitely be looking for more updates from me as I begin to experiment more with the cluster and what it can do. Hopefully I can run some experiments before my next break, but as exam season is coming up, I may need to focus more on my studies. If you have any questions or comments, feel free to email me or leave a comment.

When idle, my cluster is aiding the BOINC sponsored SETI@Home project. I chose this over other BOINC projects mainly due to it’s friendliness towards ARM processors (of which the Raspberry Pi uses).

2017 Fall Data Analytics Competition – VT/GDMS Health in the US

Moving into the fall semester of my Sophomore year at Virginia Tech, I was a little starved of side projects I could do, simply due to the amount of schoolwork I had to finish each week. However, luckily I was able to make time for a couple projects I could focus on in the side. One of them being is data analytics competition, of which was sponsored by the CMDA club here at VT and GDMS. I participated in the beginners competition in a group of 3, the other members being good friends of mine Eric Fu and Kali Liang. In the competition, we were given a large data set (.csv file) involving health issues across various cities throughout the United States (500 entries). Some examples of the categories, just to get a feel of the type of data we received, included ailments such as asthma, obesity, heart disease, kidney disease, etc. We were also given general data involving disease prevention (i.e. access to health care, going to regular checkups, etc.) and unhealthy behaviors (i.e. binge drinking, drug use, lack of leisure time and sleep, etc.).

I believe that my group’s approach to the data was a rather unique one, given our different backgrounds. Eric is a BIT major, Kali is a CMDA (Computational Modeling and Data Analytics) and Statistics major, and I am, of course, a Computer Science major. As a result of our different backgrounds, we each had different ideas as to how to approach the information to find correlations between the categories. For instance, my approach was to code basic programs in Python using numpy and matplotlib to generate scatterplots and other useful graphs, while Kali was comfortable using R to find more statistics-heavy information (like GLM/LS Means) and Eric was comfortable working directly in the given Excel file to look for correlations.

First, we decided as a group to look for interesting differences in the data by splitting the it up into regions. We explored a number of splits, and we ended up using data from a regional split (NE, S, MW, W), a coastal split (west/east coast, with everything else being lumped into a “main land” group), and a split based on population size to explore the uniqueness of big cities (we defined a big city as any city above 2 standard deviations above the mean population for the entire data set). We also explored a split based on State, but found the data to be too volatile, and with 51 groups (incl. Washington DC), there were still too many points to investigate. My python scripts came into good use here, as I was able to make a quick program that found and graphed the scatterplots of each region for any given category, along with a line that connected the means for each sub group together (with and without outliers) for visual investigation. I also coded a script that plotted scatter plots for any 2 given categories to see if they were related.


The first graph details the difference in Obesity in the east vs. west coast, while the second illustrates the difference in Obesity in the US Regions.

From this initial stab at the data, we found what we believed to be significant differences between the general health in the east coast and the west coast (meaning the west coast was significantly lower than the east in almost all categories). Also worth noting, we also determined that health in the west region was generally better than the other regions. From this lead, we explored the differences in the means to attempt to understand why the west is generally healthier. We observed that both the west coast and the west region have significantly lower Obesity rates when compared to other regions, and decided to explore that as a possibility (see the mean comparison charts above). We confirmed our assumption by looking at the general trends for obesity and various other ailments and observed that, in general, there is a strong link between obesity and other serious health problems.


Some of the scatterplots we used to illustrate the link between Obesity and other serious illnesses like heart disease (CHD) and asthma (CASTHMA). 

In order to further provide evidence for our assumptions, Kali used regression analysis, specifically generalized linear models with least square means to further look for significance between these groups. Using these tests was helpful because it not only aided at eliminating some of the confounding variables that may be lurking within the data, but made it extremely clear as to whether the data was significant or not through looking at the p-value numerically and with a visual aide. Essentially, when graphed, intervals of the mean are plotted for each region (each interval determined using a 95% CI). If the regions don’t have any overlapping points, then the difference between the two can be considered significant and is definitely worth looking into. As you can see below, we were able to confirm our assumption that the difference between obesity rates in the east and west is significant.


LSM Chart confirming our assumption that the difference in Obesity rates in the east and west coasts is significant

However, we were not satisfied simply stating that we recommend that Obesity rates be lowered, so we looked into possible variables that could affect Obesity that weren’t already included in our data set. Ideas for this included finding density of fast food restaurants within each city, and measuring the nutritional differences between each city. Unfortunately, we were unable to find any reliable data on density of fast food restaurants, but we were able to pull data from the CDC website pertaining to amount of people that ate less than one fruit and one vegetable per state in the US. Using this data, we first confirmed the link between not eating nutritional food and obesity, then went on to make our recommendation. Eric’s background in Business came in handy, as we gave various recommendations that made sense from an economics point of view (i.e. decrease the tax or cost of fruits and vegetables in obese regions, subsidize farmers to increase fruit and vegetable output, or push advertisements encouraging people to eat healthier).

As a slight aside to our main focus on Obesity across the US, we also found that Stress (we defined stress as a lack of leisure time (LPA) and a lack of sleep) was high in big cities. We noticed that not only were kidney problems slightly higher in bigger cities, but so were other ailments such as strokes and diabetes. We were able to link the connection between stress and diabetes with kidney problems (seen below), and further connect kidney problems with strokes. Unfortunately, due to the time constraints for the competition, we were not able to explore these connections much deeper. However, given more time, we definitely would have done so.


Mean comparison showing increase in lack of sleep in big cities and scatterplot showing the relationship between sleep deprivation and kidney problems.

This competition was extremely fun to participate in. Unfortunately, we did not place in the top 3 teams for our section, although we did get an honorable mention. I want to thank GDMS and the CMDA club at VT for making this a reality. I’ve always been interested in data analytics due to my background in neural networks, and it was incredibly fun to work on something data analytics related even though I haven’t been able to take any statistics courses as of yet.

If you’d like to see any more plots, or would like to see the scripts I made for the purpose of this competition, feel free to email me at brendan.mccloskey@gmail.com.

2017 Summer App Project – The Stalk Market

As my previous post (linked here) eluded, this is the second and main project I worked on during the latter half of my summer break from Virginia Tech. Described shortly, this project is a stock market tracker of sorts for the game Animal Crossing: New Leaf (ACNL) that is able to run from any handheld Android phone. My main focus for this project was introducing myself to the world of app development, mainly following various tutorials online and playing around with the Android Studio IDE. As such, the actual logic behind this project isn’t too difficult to grasp. As I’ll get into, the stock market in ACNL is rather simple and follows a strict set of rules. Thus, I mainly focused on making the user interface as fluid as possible, to ensure ease of use and practicality while keeping the function of the app in mind.


What the app looks like when you open it for the first time. I put a decent amount of effort into ensuring the app looks somewhat decent and is easy to use. Note, all pictures shown here are screenshots taken on my Google Pixel.

First, onto the basics of the stock market in ACNL. Every Sunday, the player can buy Turnips from a particular NPC that visits the town. The price for that particular set of turnips can range anywhere from 90-110 bells (the currency of ACNL). Then, throughout the week, the selling price of those turnips changes depending on the particular pattern for that week. The prices change twice per day (at store-open and 12pm), with the turnips rotting at exactly 6am on Sunday. Thus, the player must guess when he or she should sell all of the turnips they purchased for that week to ensure maximum profits. This is where my app comes in. The user is to enter every price update in-game into the app. Then, when prices are optimal, the app recommends the user to sell all of their turnips.

There are 4 patterns the stalk market can have for any given week, each with its own set of rules that makes it easily distinguishable from the others. Of the patterns that produce positive returns, the player is first tempted multiple times with above average selling prices before the maximum price is observed. The biggest example of this is the big spike pattern, where there are 2 above average prices before the best price, often double that of the previous prices, is observed. My app guesses which pattern is most likely for the given week/set of values, and warns against selling your turnips until it thinks the highest value for the week has been achieved. There is only one pattern in which the player is guaranteed a loss. If you’d like to read more on the various patterns and their strategies, I would recommend visiting this page.

2    3

Though the turnip price in the first picture is much higher than the purchase price (labeled in blue), the app recommends waiting, which pays off with a week-high price 3 times that of the previously shown price, as shown in the second picture.

Once the logic was finished and patted down, I moved onto the user interface portion of the app. The only non-native library I used was GraphView for the plot of prices seen on the upper-half of the screen. The main challenge when coming up with the UI for this project was where to place the buttons and text box in relation to each other and the graph. I ended up with the current design, as I think it looks and feels simple and easy to use. One of the previous designs had all of the buttons underneath the graph in a 2×2 grid pattern, which failed due to the strangeness of fingers as a cursor. Sometimes, I found myself pressing the bottom right button when I meant to press the upper left button, among other similar problems. I’ve found that the current vertical design is a clear and easy solution that wasn’t difficult to implement. Other than button placement, I used in-game and fan art (the background, turnip, and animal figure) for decorating the app. If I were to publish this on the Play Store, I would first want to draw myself or enlist the help of a friend to redraw all of these assets to ensure I didn’t steal content from their creators. As such, you will not be able to find this on the Play Store as of yet. However, if you email me saying you want to put this app to use, feel free to and I will post an app that falls within these guidelines.

This project was fun to explore. I learned a whole lot about Android app development, and created something I can put to use in my future ACNL endeavors. If you’d like to look at the source code, you can see it here.


2017 Summer Cryptography Project – Caesar Ciphers and ASCII Tables

Admittedly, the time before my summer semester in Japan was ill-spent in regards to furthering my understanding in the Computer Science world. However, there were still a couple of projects that I did for fun before I left. This first project, a rather simple caesar cipher, was the first of two projects I did. I did this project in particular as a way to brush up on some of the knowledge of the C language I gained during my spring semester CS 2505 class at Virginia Tech. For those unfamiliar with the concept of a caesar cipher, it’s a crude way to encode a chunk of information — simply taking every letter and shifting it up or down a predetermined number of spaces in the alphabet. For instance, the string “abcd” would be encrypted to “bcde” with a caesar key of positive one. Of course, traditionally, wrapping occurs at the ends of the alphabet; meaning z -> a with a key of +1, and a -> z with a key of -1.

There are a couple key parts when it comes to translating this into code. First and foremost, I made it runnable via. the command line. The program expects the file path to the text file the user wants scrambled or unscrambled, along with either ‘e’ or ‘d’ for ‘encode’ and ‘decode’ respectively. Next, instead of letting the letters wrap once at the end/beginning of the alphabet, I allowed the characters to go above/below this limit, possible thanks to the format of the ASCII table.


ASCII table

In C, along with the vast majority of programming languages used today, ‘char’ (character) variables are actually stored as integers. Each character is 1 byte, allowing for up to 256 different characters to exist. This not only includes your standard upper/lower case alphabet and numbers, but also special characters, along with some special, machine only characters, like ‘\n’ (new line). So, instead of wrapping at the ends of the alphabet, I wrapped at the ends of the ASCII table (see methods encode and decode in the block below to see how). In regards to my program, this is relevant because it allows for characters to spill over into generally weird or unexpected characters, making it easier on us to program because we don’t need to look for specific ASCII values to wrap around, as well as adding a bit more effectiveness to the final product. Of course, this method won’t fool someone who knows what they’re doing when it comes to decryption, but it should generally prevent friends or co-workers from snooping into information you don’t want shared.

There are still plenty of improvements that can be made to this system to improve it even further and lessen the chance of someone cracking a particular files key. As you can see in this iteration of the program below, the key is static between all files, meaning a correct guess on one file means access to your entire library of secret text files. This could be improved by simply making the key random between files and stored somewhere inconspicuous. For instance, the key could be between 0 and 255, and simply written as the first character of the encoded file. While again, this method would fail almost instantly in the case of a professional crack, it should stump the average Joe for a couple of hours. Or, perhaps this method is repeated for every character in any one file; the key for any given character placed just before or after it. I’m sure you can see how this method can become increasingly complex, and increasingly difficult to break, the longer you think about it.

FILE *f;

int caesar = 5;

int main(int argc, char **argv) {
	if (argc != 3)
	FILE* f = getFile(argv[1]);
	if (strcmp(argv[2], "e") == 0)
	else if (strcmp(argv[2], "d") == 0)
	return 0;

void badInput() {
	printf("Format is '.../caeser <.../fileToEdit.txt> <e/d>'\n");

FILE* getFile(char *url) {
	FILE *f = fopen(url, "r+");
	if (f == NULL) {
		printf("Can't locate file '%s'", url);
	return f;

void encode(FILE *f) {
	int c;
	while((c = fgetc(f)) != EOF) {
		fseek(f, -1, SEEK_CUR);
		fputc((c + caesar) % 255, f);
		fseek(f, 0, SEEK_CUR);

void decode(FILE *f) {
	int c;
	while((c = fgetc(f)) != EOF) {
		fseek(f, -1, SEEK_CUR);
		fputc((c - caesar) % 255, f);
		fseek(f, 0, SEEK_CUR);

The C code behind the Caesar Cipher. If you have any questions about the code feel free to email me: brendan.mccloskey@gmail.com.

This project was an easy, quick, and fun way to stay familiar with the ins and outs of ASCII values and C I/O alike. Feel free to take this idea further and create ciphers of your own!

2017 Spring AI Project – PoNNg

Towards the end of my first year at Virginia Tech, I oddly found myself with more free time than usual. Throughout the year I tried to focus on my neural network study when I could, but sadly, little time arose for me to pursue what I love. However, I always worked on small projects when I could. The majority of this time was spent geared towards learning Linux and Python, both of which I have become somewhat proficient in. Using these newfound skills, I looked back to my roots to see what improvements I could make on my original code from high school and to get back into the field of artificial intelligence in general. The main goal for this project was to create a short, dependable neural network algorithm for personal use in Python. I also wanted to create a system in which 2 neural networks were pitted against each other in some form of competition. I thought pong was a suitable game for this purpose because of its simplicity and competitive nature. My version of it stays, for the most part, true to the original game. 2 paddles, 1 ball, reset and score increase upon the ball exiting the left or right screen. After each bounce, the ball’s y velocity is randomly chosen between [2, 5] to add some randomness to where the ball will be when it reaches the left or right side of the screen. Pygame was used to render the components onto a window.

The general frame of mind I had while creating the neural network algorithm for this project was brevity and simplicity. One of the main issues with my Java implementation from a year ago was the fact that all of the weights were stored in a single 1D array, which isn’t ideal for many reasons. Mainly, it’s confusing to look at and understand the math behind extracting the correct weight at the correct time. To correct this, I switched it from a 1D array to a 3D array, in the format of [layer][input][output], layer being the current index in regards to the hidden layer, input being the “left” set, and output being the “right” set (i.e. input -> hidden, input is the “left” set and hidden is the “right” set). Although confusing at first due to my lack of experience with 3D arrays, I eventually came up with a good system that works just as fine as the original and is slightly easier to understand from an outsiders point of view. e

Screenshot from 2017-04-09 19-13-31

Example of a 3D weight storage system for a neural network with 2 input nodes, 1 hidden layer of 3 nodes, and 1 output node. The number of rows corresponds to the “input” layer (input -> hidden; input is the input layer, hidden -> output; hidden is the input layer) and the number of columns corresponds to the “output” layer (hidden and output in the above example).

In this project, I experimented around with neural networks of varying sizes before settling on a simple 2 node input, 3 node hidden layer, and 1 node output network. The input was the vertical and horizontal distance between the paddle and the ball over the total height/width of the game window. When given more input nodes like the x and y velocity of the ball, it was observed that the neural networks were able to accurately predict where the ball would hit the paddle when it reached the side of the window (as opposed to just following the ball’s y component), but the time it took to reach this point was incredibly long and results varied over different situations (i.e. the angle the ball was approaching the paddle, etc.). Fitness was given based on how many matches the neural network won, but was punished twice as hard for every match (first to 3 points) lost (+1 for win, -2 for loss). A standard breeding process was performed after each match in which the parents were the 2 best performing networks in a population and the child was the worst, with the child weights having 2/3 chance of being from either of the parents and 1/3 chance of being the average of the parent weights. There was a 6% chance of any weight being erased and assigned a random value between (-1, 1). After each match, the loser was replaced by a randomly selected network from the population (it was ensured that the two networks playing each other were not the same one). Each paddle’s neural network can be seen displayed on its side of the board — each circle being a node and each line being a weight. A blue weight signifies a negative value and a red weight signifies a positive value.

Getting on to the actual project, the rate at which the neural networks became “good” at the game varied on each run. In my experience, it could be as short as 100 breeds, and as long as around 2000. However, for each run, the neural networks followed the same general pattern every time — make no attempt to move for the ball, show some attempt at chasing the ball or “reacting” the ball when it came close, and successfully chasing the ball.


Right when the program started. You can see the paddles move to either the top or the bottom of the screen, paying no attention to the ball.


Networks show some learning — they now react to the ball when it approaches them, though they do not move to the correct y value as of yet.


The end of the pattern. Networks demonstrate ability to chase after the ball and hit it every time. At this point, the match time is indefinite.

Although this project seemed simple at first, there were many different stages it went through while in development. At first, the input size for the neural networks was complex (including paddle location, ball location, ball velocity). However, after a lot of playing around, consistent improvement was shown when this list was refined to just the distance between the paddle and the ball. In retrospect, this makes sense, as decreasing the complexity of any problem makes it easier to solve. Although, maybe with more patience, a more unique strategy could have been developed with more inputs. I encourage you to download my source code and play around with yourself if you are at all interested in doing so (here).

2016 Spring AI Project – Floppo Bordo

As my senior year of high school began to slow down and the college hype began to increase, I looked for simple projects to work on while in the midst of the craziness. I spent a while sitting back and looking at the projects I’ve finished this past school year and remembered a couple of other projects from other people that served as inspiration for my personal projects. Since I learned everything I know about AI from watching lectures and demonstrations online, it was a trip down memory lane to go back and watch those videos in particular. One of the projects I looked back at was a machine learning algorithm that used reinforcement learning for Flappy Bird (A popular mobile game), and I remember being amazed at the actual process of learning that the algorithm went through (see that video here). I decided to challenge myself by making my own Flappy Bird AI to benchmark my current progress, especially in comparison to where I was 6 months ago.

First, however, I had to make a Flappy Bird clone. Making it was surprisingly easy, as it has simple rules and doesn’t require a lot of complex algorithms. Flappy Bird is essentially an endless runner, but the player must tap the bird to fly precariously in between pipes (if the player touches one of these pipes they die and have to start over). Since this was just a quick project, I didn’t bother with making images to go over each of the objects, instead using colored rectangles. The blue rectangle is the player, the black rectangles are pipes, and the green rectangle is the ground.

I used my standard Neural Network algorithm with a genetic algorithm for the machine learning AI (read more about Neural Networks here). It uses 4 inputs – the player’s y position (height from the ground), the y positions of the opening between the closest pipes (the lowest point on the top pipe and the highest point on the bottom pipe), and the horizontal distance between the player and the closest pipe. There are 2 hidden layers of 5 nodes each, and 1 output node (if the output node > 0.5, the bird jumps). I used a sigmoid function (1 / (1 + e^-x)) as my activation function. Reward was based on the score at the end of the run and how far the AI moved the bird to the right.

Now onto the results. It actually learned to jump between pipes fairly quickly, but difficulty came when it needed to jump with precision between the pipes. However, after a little bit of time, it mastered the game perfectly.


Start of the simulation. You can see the AI has trouble handling its own jump height.


After about 10/15 minutes of runtime, the AI has mastered the art of jumping in between the pipes.

I enjoyed making this project and seeing how far I’ve come. A while ago, I never would have thought I would have been able to make something like this. If you want to see the code for this project, you can get it here.

2016 Spring AI Project – TicTacToe & Brute Search

As my senior year of high school began to slow down and the college hype began to increase, I looked for simple projects to work on while in the midst of the craziness. Looking at the history of Artificial Intelligence, I began to read up on the different methods AI developers used before modern AI existed (Neural Networks would be an example of a modern day technique). I also decided to code this project in Python in order to broaden my skills with other languages (and because Python is pretty fun to use).

Artificial Intelligence for games like TicTacToe and Minimax are prime examples of early stage AI – programs that brute search their way through an entire list of possible situations in order to find the best action to take. However, brute searching only works on games with a small amount of game boards. For instance, Minimax is a really simple game because the amount of choices and paths players can take is very limited. TicTacToe ramps it up a little (about 9! or 362,880 different ways to play the game), but is still feasible with a little bit of load time. Games like chess, however, are almost impossible to brute search just from the sheer amount of games possible (about 10^10^50 games, in fact, there are more games of chess than grains of sand on the Earth). This means that although brute searching is an alright method to use when creating game AI, it isn’t always the most efficient, and other options should be considered the more complex the game is.

One of the main roadblocks I hit along the way on this project was dealing with recursion. I am familiar with recursion and its usefulness, but for more complicated tasks like this, it can become confusing at times (especially writing in an unfamiliar language, might I add). For those who don’t know much about recursion, it’s essentially a function that calls itself in order to simplify a problem. It’s complicated in that its formation is very abstract and you have to keep some key issues in mind while creating recursive functions (i.e., making sure you don’t accidentally create a function that loops indefinitely). After some frustration / hair pulling out, I was able to overcome the challenge and write a program that performs pretty well. Basically, the AI looks at each possible choice it has and finds the probability it will win if it chooses that path taking into consideration all possible game boards extending from that board. After probabilities for each possible spot to play are calculated, the AI chooses the option with the highest probability of success.

Screen Shot 2016-03-31 at 10.24.41 PM

Here the AI is controlling Xs and I am controlling Os. You can see the AI was successfully able to perform a double attack, forcing a win for Xs.

This project was fun to make. Comparing it with the Neural Networks I’ve made this year, It’s amazing to see how far Computer Scientists have come in terms of Artificial Intelligence and the ability for computers to recognize patterns and solve problems. If you want to see the code for this project, you can get it here: https://github.com/mccloskeybr/tictactoe