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,brett,laforge,15.02.2024 22:10,file:///home/brett/.config/libreoffice/4;
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,brett,laforge,15.02.2024 22:10,file:///home/brett/.config/libreoffice/4;
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,brett,laforge,15.02.2024 22:09,file:///home/brett/.config/libreoffice/4;
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,brett,laforge,15.02.2024 22:09,file:///home/brett/.config/libreoffice/4;
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@ -10,6 +10,10 @@ Run(RV)/(PV) CC CO OO OC
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8 1009 104 1537 160
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9 1040 108 1410 252
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Best Result:
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CC CO OO OC
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1073 71 1510 156
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Fitness: 0.998937
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Total results:
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CC CO OO OC
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10226 1138 15367 1369
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\caption[]{0 Elites, 10 Runs Averaged}
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\label{fig:fp2}
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\end{figure}
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\subsection{Analysis And Conclusion}
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\subsection{Analysis and Conclusion}
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The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.1 mutation.
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\section{Rice Classification}
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\subsection{Introduction}
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\subsection{Parameter Table}
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@ -156,27 +154,27 @@ The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.
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\hline
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Population Size & 5000 \\
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\hline
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Generations & 50 \\
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Generations & 51 \\
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\hline
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Training Set & N/A \\
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Training Set & Rice Classification (Cammeo and Osmancik) \\
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\hline
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Testing Set & N/A \\
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Testing Set & Rice Classification (Cammeo and Osmancik) \\
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\hline
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Crossover Operator & Subtree Crossover\\
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\hline
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Mutation Operator & Grow Tree, Max Depth 4 \\
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\hline
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Crossover Rate & 0.9 or 1.0* \\
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Crossover Rate & 0.9 or 0.9* \\
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\hline
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Mutation Rate & 0.1 or 1.0* \\
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Mutation Rate & 0.1 or 0.9* \\
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\hline
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Elitism & Best 2 or 0 individuals Survive* \\
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Elitism & Best 2 individuals Survive \\
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\hline
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Selection & Fitness Proportionate \\
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\hline
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Function Set & *, /, +, -, exp, log, sin, cos \\
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Function Set & *, /, +, -, exp, log \\
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\hline
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Terminal Set & X, Ephemeral Value \\
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Terminal Set & area, perimeter, major, minor, eccentricity, convex, extent, Ephemeral Value \\
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\hline
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Tree Initialization & Half and Half, Max Depth 2-6 \\
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\hline
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\hline
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\end{tabularx}
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\end{center}
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\subsection{Fitness Evaluation}
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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.
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\subsection{Fitness Plots}
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\begin{figure}[H]
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\centering
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\includegraphics[width=1.0\linewidth]{fp6}
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\caption{2 Elites, 10 Runs Averaged}
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\label{fig:fp6}
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\end{figure}
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\begin{figure}[H]
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\centering
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\includegraphics[width=1.0\linewidth]{fp7}
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\caption{2 Elites, 10 Runs Averaged}
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\label{fig:fp7}
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\end{figure}
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\subsection{Confusion Matrix}
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\begin{figure}[H]
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\centering
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\includegraphics[width=1.0\linewidth]{fp10}
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\caption{0.9 Crossover 0.1 Mutation 2 Elites Best Program Results}
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\label{fig:fp10}
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\end{figure}
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\begin{figure}[H]
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\centering
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\includegraphics[width=1.0\linewidth]{fp11}
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\caption{0.9 Crossover 0.1 Mutation 2 Elites 10 Run Average Results}
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\label{fig:fp11}
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\end{figure}
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\begin{figure}[H]
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\centering
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\includegraphics[width=1.0\linewidth]{fp12}
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\caption{0.9 Crossover 0.9 Mutation 2 Elites Best Program Results}
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\label{fig:fp8}
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\end{figure}
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\begin{figure}[H]
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\centering
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\includegraphics[width=1.0\linewidth]{fp13}
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\caption{0.9 Crossover 0.9 Mutation 2 Elites 10 Run Average Results}
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\label{fig:fp9}
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\end{figure}
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\subsection{Analysis and Conclusion}
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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.
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\section{Compiling / Executing}
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This assignment was made for linux using GCC 13.2.0, however any C++17 compliant compiler should work.
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@ -201,5 +250,12 @@ The minimum GCC version appears to be 8.5, meaning this assignment can be built
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\end{lstlisting}
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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|
|
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|
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\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}
|
||||
|
|
|
|||
|
|
@ -4,8 +4,14 @@
|
|||
\contentsline {subsection}{\numberline {2.2}Parameter Table}{2}{subsection.0.2.2}%
|
||||
\contentsline {subsection}{\numberline {2.3}Fitness Evaluation}{2}{subsection.0.2.3}%
|
||||
\contentsline {subsection}{\numberline {2.4}Fitness Plots}{3}{subsection.0.2.4}%
|
||||
\contentsline {subsection}{\numberline {2.5}Analysis And Conclusion}{4}{subsection.0.2.5}%
|
||||
\contentsline {subsection}{\numberline {2.5}Analysis and Conclusion}{4}{subsection.0.2.5}%
|
||||
\contentsline {section}{\numberline {3}Rice Classification}{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 {section}{\numberline {4}Compiling / Executing}{4}{section.0.4}%
|
||||
\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}%
|
||||
|
|
|
|||
|
|
@ -48,6 +48,7 @@ static double* app_fitness_cases[2];
|
|||
#endif
|
||||
static double value_cutoff;
|
||||
|
||||
// required for this to work with c++
|
||||
template<typename T>
|
||||
auto cxx_d(T* t)
|
||||
{
|
||||
|
|
@ -60,7 +61,7 @@ auto cxx_c(T* t)
|
|||
return reinterpret_cast<char* (*)()>(t);
|
||||
}
|
||||
|
||||
struct stt_record
|
||||
struct rice_record
|
||||
{
|
||||
double area;
|
||||
double perimeter;
|
||||
|
|
@ -74,16 +75,16 @@ struct stt_record
|
|||
|
||||
struct loaded_data
|
||||
{
|
||||
std::vector<stt_record> cammeo;
|
||||
std::vector<stt_record> osmancik;
|
||||
std::vector<stt_record> set;
|
||||
std::vector<rice_record> cammeo;
|
||||
std::vector<rice_record> osmancik;
|
||||
std::vector<rice_record> set;
|
||||
// everything after these values is testing data.
|
||||
size_t last_c = 0;
|
||||
size_t last_o = 0;
|
||||
|
||||
std::vector<stt_record> getTrainingSet(size_t amount)
|
||||
std::vector<rice_record> getTrainingSet(size_t amount)
|
||||
{
|
||||
std::vector<stt_record> set;
|
||||
std::vector<rice_record> set;
|
||||
static std::random_device dev;
|
||||
static std::mt19937_64 engine(dev());
|
||||
std::uniform_int_distribution choice(0, 1);
|
||||
|
|
@ -103,7 +104,7 @@ struct loaded_data
|
|||
return set;
|
||||
}
|
||||
|
||||
std::vector<stt_record> getTestingSet()
|
||||
std::vector<rice_record> getTestingSet()
|
||||
{
|
||||
if (set.empty())
|
||||
{
|
||||
|
|
@ -131,7 +132,7 @@ struct loaded_data
|
|||
}
|
||||
} rice_data;
|
||||
|
||||
void vec_randomizer(std::vector<stt_record>& read, std::vector<stt_record>& write)
|
||||
void vec_randomizer(std::vector<rice_record>& read, std::vector<rice_record>& write)
|
||||
{
|
||||
auto seed_param = get_parameter("random_seed");
|
||||
static std::random_device dev;
|
||||
|
|
@ -155,12 +156,12 @@ void load_rice_data(std::string_view path)
|
|||
size_t index = 0;
|
||||
while (!blt::string::contains(file[index++], "@DATA"))
|
||||
{}
|
||||
std::vector<stt_record> c;
|
||||
std::vector<stt_record> o;
|
||||
std::vector<rice_record> c;
|
||||
std::vector<rice_record> o;
|
||||
for (std::string_view v : blt::itr_offset(file, index))
|
||||
{
|
||||
auto data = blt::string::split(v, ',');
|
||||
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]),
|
||||
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]),
|
||||
std::stod(data[6])};
|
||||
std::memset(r.type, 0, 8ul);
|
||||
std::memcpy(r.type, data[7].data(), std::min(data[7].size(), 8ul));
|
||||
|
|
@ -170,7 +171,7 @@ void load_rice_data(std::string_view path)
|
|||
o.push_back(r);
|
||||
}
|
||||
|
||||
vec_randomizer(c, rice_data.cammeo);
|
||||
vec_randomizer(c, rice_data.cammeo);
|
||||
vec_randomizer(o, rice_data.osmancik);
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue