submit

PartA/crossover_100_mutation_100_elitism_0/.~lock.crossover_100_mutation_100_elitism_0.tsv#

@@ -1 +0,0 @@
-,brett,laforge,15.02.2024 22:10,file:///home/brett/.config/libreoffice/4;

PartA/crossover_100_mutation_100_elitism_2/.~lock.crossover_100_mutation_100_elitism_2.tsv#

@@ -1 +0,0 @@
-,brett,laforge,15.02.2024 22:10,file:///home/brett/.config/libreoffice/4;

PartA/crossover_90_mutation_10_elitism_0/.~lock.crossover_90_mutation_10_elitism_0.tsv#

@@ -1 +0,0 @@
-,brett,laforge,15.02.2024 22:09,file:///home/brett/.config/libreoffice/4;

PartA/crossover_90_mutation_10_elitism_2/.~lock.crossover_90_mutation_10_elitism_2.tsv#

@@ -1 +0,0 @@
-,brett,laforge,15.02.2024 22:09,file:///home/brett/.config/libreoffice/4;

PartB/90Cross10Mut2El/aggregated_fn.tsv

@@ -10,6 +10,10 @@ Run(RV)/(PV)	CC	CO	OO	OC
 8	1009	104	1537	160
 9	1040	108	1410	252
 
+Best Result:
+	CC	CO	OO	OC
+	1073	71	1510	156
+Fitness:	0.998937
 Total results:
 	CC	CO	OO	OC
 	10226	1138	15367	1369

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report/template_Report.tex

@@ -139,11 +139,9 @@ If the difference is less than a user provided (default 1.e15) value cutoff it i
 	\caption[]{0 Elites, 10 Runs Averaged}
 	\label{fig:fp2}
 \end{figure}
-\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. 
 
-
-
 \section{Rice Classification}
 \subsection{Introduction}
 \subsection{Parameter Table}
@@ -156,27 +154,27 @@ The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.
 		\hline
 		Population Size & 5000 \\
 		\hline
-		Generations & 50 \\
+		Generations & 51 \\
 		\hline
-		Training Set & N/A \\
+		Training Set & Rice Classification (Cammeo and Osmancik) \\
 		\hline
-		Testing Set & N/A \\
+		Testing Set & Rice Classification (Cammeo and Osmancik) \\
 		\hline
 		Crossover Operator & Subtree Crossover\\
 		\hline
 		Mutation Operator & Grow Tree, Max Depth 4 \\
 		\hline
-		Crossover Rate & 0.9 or 1.0* \\
+		Crossover Rate & 0.9 or 0.9* \\
 		\hline
-		Mutation Rate & 0.1 or 1.0* \\
+		Mutation Rate & 0.1 or 0.9* \\
 		\hline
-		Elitism & Best 2 or 0 individuals Survive* \\
+		Elitism & Best 2 individuals Survive \\
 		\hline
 		Selection & Fitness Proportionate \\
 		\hline
-		Function Set & *, /, +, -, exp, log, sin, cos \\
+		Function Set & *, /, +, -, exp, log \\
 		\hline
-		Terminal Set & X, Ephemeral Value \\
+		Terminal Set & area, perimeter, major, minor, eccentricity, convex, extent, Ephemeral Value \\
 		\hline
 		Tree Initialization & Half and Half, Max Depth 2-6 \\
 		\hline
@@ -188,6 +186,57 @@ The best average fitness of all the tests was 0.19384 using 0.9 crossover and 0.
 		\hline
 	\end{tabularx}
 \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}
 This assignment was made for linux using GCC 13.2.0, however any C++17 compliant compiler should work. 
@@ -201,5 +250,12 @@ The minimum GCC version appears to be 8.5, meaning this assignment can be built
 \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}          

report/template_Report.toc

@@ -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}%

src/program/app.cpp

@@ -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<rice_record> cammeo;
-    std::vector<stt_record> osmancik;
+    std::vector<rice_record> osmancik;
-    std::vector<stt_record> set;
+    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<rice_record> c;
-    std::vector<stt_record> o;
+    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);
 }