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PartA/crossover_100_mutation_100_elitism_0/.~lock.crossover_100_mutation_100_elitism_0.tsv# Normal file
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PartB/90Cross10Mut2El/aggregated_fn.tsv
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@ -10,10 +10,6 @@ Run(RV)/(PV) CC CO OO OC
8 1009 104 1537 160 8 1009 104 1537 160
9 1040 108 1410 252 9 1040 108 1410 252
Best Result:
CC CO OO OC
1073 71 1510 156
Fitness: 0.998937
Total results: Total results:
CC CO OO OC CC CO OO OC
10226 1138 15367 1369 10226 1138 15367 1369

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\label{fig:fp2} \label{fig:fp2}
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\subsection{Analysis and Conclusion} \subsection{Analysis And Conclusion}
The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.1 mutation. The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.1 mutation.
\section{Rice Classification} \section{Rice Classification}
\subsection{Introduction} \subsection{Introduction}
\subsection{Parameter Table} \subsection{Parameter Table}
@ -154,27 +156,27 @@ The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.
\hline \hline
Population Size & 5000 \\ Population Size & 5000 \\
\hline \hline
Generations & 51 \\ Generations & 50 \\
\hline \hline
Training Set & Rice Classification (Cammeo and Osmancik) \\ Training Set & N/A \\
\hline \hline
Testing Set & Rice Classification (Cammeo and Osmancik) \\ Testing Set & N/A \\
\hline \hline
Crossover Operator & Subtree Crossover\\ Crossover Operator & Subtree Crossover\\
\hline \hline
Mutation Operator & Grow Tree, Max Depth 4 \\ Mutation Operator & Grow Tree, Max Depth 4 \\
\hline \hline
Crossover Rate & 0.9 or 0.9* \\ Crossover Rate & 0.9 or 1.0* \\
\hline \hline
Mutation Rate & 0.1 or 0.9* \\ Mutation Rate & 0.1 or 1.0* \\
\hline \hline
Elitism & Best 2 individuals Survive \\ Elitism & Best 2 or 0 individuals Survive* \\
\hline \hline
Selection & Fitness Proportionate \\ Selection & Fitness Proportionate \\
\hline \hline
Function Set & *, /, +, -, exp, log \\ Function Set & *, /, +, -, exp, log, sin, cos \\
\hline \hline
Terminal Set & area, perimeter, major, minor, eccentricity, convex, extent, Ephemeral Value \\ Terminal Set & X, Ephemeral Value \\
\hline \hline
Tree Initialization & Half and Half, Max Depth 2-6 \\ Tree Initialization & Half and Half, Max Depth 2-6 \\
\hline \hline
@ -186,76 +188,9 @@ The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.
\hline \hline
\end{tabularx} \end{tabularx}
\end{center} \end{center}
\subsection{Fitness Evaluation}
Tested on the input terminal values the GP produces a positive or negative value which is interpreted as either Cammeo (+) or Osmancik (-). Raw fitness is equal to the number of hits which is the number of correct identifications. The adjusted fitness is then calculated and subtracted from 1 in order to invert and produce the required lowest fitness better.
\subsection{Fitness Plots}
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{fp6}
\caption{2 Elites, 10 Runs Averaged}
\label{fig:fp6}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{fp7}
\caption{2 Elites, 10 Runs Averaged}
\label{fig:fp7}
\end{figure}
\subsection{Confusion Matrix}
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{fp10}
\caption{0.9 Crossover 0.1 Mutation 2 Elites Best Program Results}
\label{fig:fp10}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{fp11}
\caption{0.9 Crossover 0.1 Mutation 2 Elites 10 Run Average Results}
\label{fig:fp11}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{fp12}
\caption{0.9 Crossover 0.9 Mutation 2 Elites Best Program Results}
\label{fig:fp8}
\end{figure}
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{fp13}
\caption{0.9 Crossover 0.9 Mutation 2 Elites 10 Run Average Results}
\label{fig:fp9}
\end{figure}
\subsection{Analysis and Conclusion}
The best results found was a correct classification rate of 91.9\%. On average the 0.9 crossover with 0.1 mutation produced the best results with the 0.9/0.9 best result almost being equal.
\section{Compiling / Executing} \section{Compiling / Executing}
This assignment was made for linux using GCC 13.2.0, however any C++17 compliant compiler should work.
The minimum GCC version appears to be 8.5, meaning this assignment can be built on sandcastle.
\begin{lstlisting}
cd your_path_to_this_source/
mkdir build
cd build
cmake ../
make -j 32
\end{lstlisting}
The actual assignment executable is called |Assignment_1| while the automatic run system is called |Assignment_1_RUNNER|. |Assignment_1_RUNNER| has a help menu with options but the defaults will work assuming you run from the build directory and are using part b only. If you want to build for Part A run |cmake -DPART_B=OFF| and run |Assignment_1_RUNNER| with |-b|
\section{Conclusion}
I made a few changes to lilgp, mostly memory fixes along with elitism with a number of individuals instead of a proportion. There appear to be some kind of issue in the GP, of which won't matter as assignment two will likely use my own gp system. I might look into it, but I was not aware there was an issue until compiling the stats here. My results have been generally positive, however, I did notice in the course of collecting data that at some point the Part A results stopped being consistently good however part B results have remained unchanged. Might have happened when I changed my custom random number seeder to not produce div by zero errors during testing. Could be anything. I don't like writing reports and have procrastinated on writing and instead have spent the last couple of weeks messing around with the GP. Fun fact a bunch of additions to my standard lib were made for this assignment. Next time will be better hopefully
\section{References}
Next assignment these will be proper. Latex is being annoying to setup for bib.\\\\
https://archive.ics.uci.edu/dataset/545/rice+cammeo+and+osmancik\\
http://garage.cse.msu.edu/software/lil-gp/
\end{document} \end{document}

25
report/template_Report.toc
View File

@ -1,17 +1,8 @@
\contentsline {section}{\numberline {1}Introduction}{2}{section.0.1}% \contentsline {section}{\numberline {1}Symbolic regression}{2}{section.0.1}%
\contentsline {section}{\numberline {2}Symbolic regression}{2}{section.0.2}% \contentsline {subsection}{\numberline {1.1}Introduction}{2}{subsection.0.1.1}%
\contentsline {subsection}{\numberline {2.1}Introduction}{2}{subsection.0.2.1}% \contentsline {subsection}{\numberline {1.2}Parameter Table}{2}{subsection.0.1.2}%
\contentsline {subsection}{\numberline {2.2}Parameter Table}{2}{subsection.0.2.2}% \contentsline {subsection}{\numberline {1.3}Fitness Evaluation}{2}{subsection.0.1.3}%
\contentsline {subsection}{\numberline {2.3}Fitness Evaluation}{2}{subsection.0.2.3}% \contentsline {subsection}{\numberline {1.4}Fitness Plots}{3}{subsection.0.1.4}%
\contentsline {subsection}{\numberline {2.4}Fitness Plots}{3}{subsection.0.2.4}% \contentsline {subsection}{\numberline {1.5}Analysis And Conclusion}{4}{subsection.0.1.5}%
\contentsline {subsection}{\numberline {2.5}Analysis and Conclusion}{4}{subsection.0.2.5}% \contentsline {section}{\numberline {2}Rice Classification}{4}{section.0.2}%
\contentsline {section}{\numberline {3}Rice Classification}{4}{section.0.3}% \contentsline {section}{\numberline {3}Compiling / Executing}{4}{section.0.3}%
\contentsline {subsection}{\numberline {3.1}Introduction}{4}{subsection.0.3.1}%
\contentsline {subsection}{\numberline {3.2}Parameter Table}{4}{subsection.0.3.2}%
\contentsline {subsection}{\numberline {3.3}Fitness Evaluation}{4}{subsection.0.3.3}%
\contentsline {subsection}{\numberline {3.4}Fitness Plots}{5}{subsection.0.3.4}%
\contentsline {subsection}{\numberline {3.5}Confusion Matrix}{6}{subsection.0.3.5}%
\contentsline {subsection}{\numberline {3.6}Analysis and Conclusion}{9}{subsection.0.3.6}%
\contentsline {section}{\numberline {4}Compiling / Executing}{9}{section.0.4}%
\contentsline {section}{\numberline {5}Conclusion}{10}{section.0.5}%
\contentsline {section}{\numberline {6}References}{10}{section.0.6}%

25
src/program/app.cpp
View File

@ -48,7 +48,6 @@ static double* app_fitness_cases[2];
#endif #endif
static double value_cutoff; static double value_cutoff;
// required for this to work with c++
template<typename T> template<typename T>
auto cxx_d(T* t) auto cxx_d(T* t)
{ {
@ -61,7 +60,7 @@ auto cxx_c(T* t)
return reinterpret_cast<char* (*)()>(t); return reinterpret_cast<char* (*)()>(t);
} }
struct rice_record struct stt_record
{ {
double area; double area;
double perimeter; double perimeter;
@ -75,16 +74,16 @@ struct rice_record
struct loaded_data struct loaded_data
{ {
std::vector<rice_record> cammeo; std::vector<stt_record> cammeo;
std::vector<rice_record> osmancik; std::vector<stt_record> osmancik;
std::vector<rice_record> set; std::vector<stt_record> set;
// everything after these values is testing data. // everything after these values is testing data.
size_t last_c = 0; size_t last_c = 0;
size_t last_o = 0; size_t last_o = 0;
std::vector<rice_record> getTrainingSet(size_t amount) std::vector<stt_record> getTrainingSet(size_t amount)
{ {
std::vector<rice_record> set; std::vector<stt_record> set;
static std::random_device dev; static std::random_device dev;
static std::mt19937_64 engine(dev()); static std::mt19937_64 engine(dev());
std::uniform_int_distribution choice(0, 1); std::uniform_int_distribution choice(0, 1);
@ -104,7 +103,7 @@ struct loaded_data
return set; return set;
} }
std::vector<rice_record> getTestingSet() std::vector<stt_record> getTestingSet()
{ {
if (set.empty()) if (set.empty())
{ {
@ -132,7 +131,7 @@ struct loaded_data
} }
} rice_data; } rice_data;
void vec_randomizer(std::vector<rice_record>& read, std::vector<rice_record>& write) void vec_randomizer(std::vector<stt_record>& read, std::vector<stt_record>& write)
{ {
auto seed_param = get_parameter("random_seed"); auto seed_param = get_parameter("random_seed");
static std::random_device dev; static std::random_device dev;
@ -156,12 +155,12 @@ void load_rice_data(std::string_view path)
size_t index = 0; size_t index = 0;
while (!blt::string::contains(file[index++], "@DATA")) while (!blt::string::contains(file[index++], "@DATA"))
{} {}
std::vector<rice_record> c; std::vector<stt_record> c;
std::vector<rice_record> o; std::vector<stt_record> o;
for (std::string_view v : blt::itr_offset(file, index)) for (std::string_view v : blt::itr_offset(file, index))
{ {
auto data = blt::string::split(v, ','); auto data = blt::string::split(v, ',');
rice_record r{std::stod(data[0]), std::stod(data[1]), std::stod(data[2]), std::stod(data[3]), std::stod(data[4]), std::stod(data[5]), stt_record r{std::stod(data[0]), std::stod(data[1]), std::stod(data[2]), std::stod(data[3]), std::stod(data[4]), std::stod(data[5]),
std::stod(data[6])}; std::stod(data[6])};
std::memset(r.type, 0, 8ul); std::memset(r.type, 0, 8ul);
std::memcpy(r.type, data[7].data(), std::min(data[7].size(), 8ul)); std::memcpy(r.type, data[7].data(), std::min(data[7].size(), 8ul));
@ -412,13 +411,13 @@ extern "C" int app_initialize(int startfromcheckpoint)
app_fitness_cases[5] = (double*) MALLOC(fitness_cases * sizeof(double)); app_fitness_cases[5] = (double*) MALLOC(fitness_cases * sizeof(double));
app_fitness_cases[6] = (double*) MALLOC(fitness_cases * sizeof(double)); app_fitness_cases[6] = (double*) MALLOC(fitness_cases * sizeof(double));
app_fitness_cases[7] = (double*) MALLOC(fitness_cases * sizeof(double)); app_fitness_cases[7] = (double*) MALLOC(fitness_cases * sizeof(double));
auto data = rice_data.getTrainingSet(fitness_cases);
#else #else
app_fitness_cases[0] = (double*) MALLOC(fitness_cases * sizeof(double)); app_fitness_cases[0] = (double*) MALLOC(fitness_cases * sizeof(double));
app_fitness_cases[1] = (double*) MALLOC(fitness_cases * sizeof(double)); app_fitness_cases[1] = (double*) MALLOC(fitness_cases * sizeof(double));
#endif #endif
oprintf(OUT_PRG, 50, "%d fitness cases:\n", fitness_cases); oprintf(OUT_PRG, 50, "%d fitness cases:\n", fitness_cases);
auto data = rice_data.getTrainingSet(fitness_cases);
for (i = 0; i < fitness_cases; ++i) for (i = 0; i < fitness_cases; ++i)
{ {
#ifndef PART_B #ifndef PART_B